Morton, David

Romeijnders, Ward

Schultz, Rüdiger

Stougie, Leen

Morton, D.P.

Schultz, R.

Stougie, L.

University of Groningen

   University of GroningenThe stochastic programming heritage of Maarten van der VlerkMorton, D.P.; Romeijnders, Ward; Schultz, R.; Stougie, L.Published in:Computational Management ScienceDOI:10.1007/s10287-018-0335-8IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2018Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Morton, D. P., Romeijnders, W., Schultz, R., & Stougie, L. (2018). The stochastic programming heritage ofMaarten van der Vlerk. Computational Management Science, 15(3-4), 319-323.https://doi.org/10.1007/s10287-018-0335-8CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 19-11-2022Computational Management Science (2018) 15:319–323https://doi.org/10.1007/s10287-018-0335-8EDITORIALThe stochastic programming heritage of Maarten van derVlerkDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6On October 9, 2016, Professor Maarten H. van der Vlerk died tragically. This special issue is dedicated tohis memory.Published online: 6 September 2018© Springer-Verlag GmbH Germany, part of Springer Nature 2018Maarten van der Vlerk was born in the year 1961 in Assen, The Netherlands, wherehe spent his childhood and attended school up to the A-level, confirming the req-uisite maturity for university studies. At the University of Groningen he became astudent in the Department of Econometrics and commenced his doctoral studies thereunder the supervision of Wim Klein Haneveld and Leen Stougie in 1990. Invited andencouraged by his supervisors, Maarten placed a research focus on the integer pro-gramming side of stochastic optimization. Soon, he discovered his first original resultsculminating in a 1993 paper in Mathematical Programming with François Louveauxof the Facultés Universitaires Notre Dame de la Paix in Namur, on “Stochastic pro-gramming with simple integer recourse” (Louveaux and van der Vlerk 1993). Withhis thesis “Stochastic programming with integer recourse”, Maarten was awarded hisPh.D. degree in Economics from the University of Groningen in 1995 (van der Vlerk1995).After a year as postdoc at the Center for Operations Research and Economet-rics (CORE) at the Université Catholique de Louvain at Louvain-la-Neuve, Maartenreturned to Groningen. In 1999 he received a prestigious research fellowship fromthe Royal Netherlands Academy of Sciences. In 2008, Maarten became Professor ofStochastic Optimization at the University of Groningen, Faculty of Economics andBusiness, Department of Operations.From the mid 1990s on, stochastic integer programming—with its structures to bedetected, its algorithms to be constructed, and, last but not least, its real-life appli-cations to be developed—became the field where Maarten van der Vlerk’s researchearned highest recognition in the stochastic programming community and beyond. Apermanently recurring research target of Maarten, and his various coauthors, has beenconvex approximation of the notoriously non-convex, discontinuous, mixed-integerB Rüdiger Schultzruediger.schultz@uni-due.deExtended author information available on the last page of the article123320 D. P. Morton et al.expected recourse function, together with its algorithmic utilization. Papers devotedto these topics range over a time span of more than 20 years.Maarten’s publications on structures began with “On the convex hull of the sim-ple integer recourse objective function” jointly with Wim Klein Haneveld and LeenStougie in Annals of Operations Research in 1995 (Klein Haneveld et al. 1995).His work on structures continued with “Convex approximations for complete inte-ger recourse models”, Mathematical Programming (2004) (van der Vlerk 2004), and“Simple integer recourse models: convexity and convex approximations”,Mathemat-ical Programming (2006) (Klein Haneveld et al. 2006), the latter again jointly withWim Klein Haneveld and Leen Stougie.Our understanding of the structure of integer recourse problems received anotherboost with Maarten’s 2015 paper in SIAM Journal on Optimization, “Convex approx-imations for totally unimodular integer recourse models: a uniform error bound”(Romeijnders et al. 2015), written together with Wim Klein Haneveld and their co-advised doctoral student, Ward Romeijnders. Departure points for these investigationsinvolve periodicity properties ofmixed-integer value functions and their interplaywiththe total variation of density functions. This line of research—extended with journalpublications (Romeijnders et al. 2016a, b)—continues ever since and, in fact, leadsinto the present special issue, whose first article has been coauthored by Maarten vander Vlerk.Along with structural investigation, Maarten always had an eye on algorithmsand computations. Already in a 1996 paper, “An algorithm for the construction ofthe convex hull of the simple integer recourse objective function” (Klein Haneveldet al. 1996) in Annals of Operations Research, jointly with Wim Klein Haneveld andLeen Stougie, an algorithm is proposed for finding the convex hull, i.e., tight lowerbounds, for the objective function. Detection of convex hulls for recourse functionalshas always been accompanied by numerical testing. A 2017 paper jointly with DavidMorton and Ward Romeijnders in INFORMS Journal on Computing is an example ofthe latter.In 1998, Maarten, jointly with Rüdiger Schultz and Leen Stougie, published inMathematical Programing an implicit-enumeration algorithm for two-stage stochasticinteger programs, where function value calculation is enhanced by techniques fromGröbner bases in computational algebra (Schultz et al. 1998). This work initiatedconsiderable subsequent research on primal augmentation in stochastic programming.Another topicMaarten dealt with, often in collaboration withWimKlein Haneveld,concerns integrated chance constraints. In a 2006 joint paper (Klein Haneveld andvan der Vlerk 2006), they analyze structures and propose algorithms for numericallyhandling these instruments for risk aversion.Today, almost any decision to be taken with an aspiration for being “best” isexposed to uncertainty, and stochastic optimization is increasingly the method ofchoice for practical planning under uncertainty. This is reflected by the variety ofpractical applicationsMaarten van der Vlerk worked on. To list a few, we start withtelecommunications, or more precisely, distributed processing in telecommunicationnetworks (Tomasgard et al. 1998), with Asgeir Tomasgard, Jan A. Audestad, ShaneDye, Leen Stougie, and Stein W. Wallace. The list continues with generalized assign-ment problems (Albareda-Sambola et al. 2006), with Maria Albareda-Sambola and123The stochastic programming heritage of Maarten van der Vlerk 321Elena Fernández, day-ahead paratransit planning (Cremers et al. 2009), with MariaL.A.G. Cremers and Wim Klein Haneveld, asset and liability management (KleinHaneveld et al. 2011), with Wim Klein Haneveld and Matthijs H. Streutker, as well asa joint project with Arno Maatman, Caspar Schweigman, and Arjan Ruijs on “Model-ing farmers’ response to uncertain rainfall in Burkina Faso: a stochastic programmingapproach” in Maatman et al. (2002). The intent and contribution of this project isdescribed best by the following quotation from the paper, “This study draws impor-tant policy conclusions regarding reorientation of research programs and illustrateshow operations research techniques can be usefully applied to study grass root prob-lems in developing countries.”In addition to his research contributions, Maarten continually provided leadership,developing the Stochastic Programming Bibliography early in his career, serving asSecretary and then Chair of theCommittee on Stochastic Programming, and serving asProgramDirector for the Bachelor of Science in Econometrics &Operations Researchand for the Master of Science in Econometrics, Operations Research & ActuarialStudies at the University of Groningen. Maarten was an excellent teacher, winningthe Lecturer of the Year Award in 2014 from the University of Groningen’s Faculty ofEconomics and Business. Maarten cared deeply about those in his communities, andhad a strong sense of integrity and social justice, advocating for the homeless whenhe was still in high school and holding to important principles without fail. Apartfrom beautiful mathematics Maarten enjoyed many other good things in life, which hehappily shared with his friends and colleagues: music, in particular Bach, and tennis,which he made a fixed point of the social program of any stochastic programmingmeeting.Wewill missMaarten’s boisterous laugh, his warm smile, his sense of humor,and his ability to make everyone around him a better person.1 This special issueMaarten’s articles have inspired, and will continue to inspire, contemporary researchin stochastic (mixed-integer) optimization. This is illustrated by the research laid out inthe present special issue. The lead article is a paper that Maarten van der Vlerk workedonwith Niels van der Laan andWard Romeijnders, improvingMaarten’s original errorbounds on convex approximations for simple integer recourse models. The remainingarticles are grouped according to Maarten’s research interests: exploring structures,designing algorithms, and solving practical applications.1.1 StructuresWeijun Xie and Shabbir Ahmed study a distributionally robust version of the simpleinteger recoursemodel introduced byMaarten, and they derive a closed form analyticalexpression for the objective and represent it by amixed-integer second-order cone pro-gram. Lars Hellemo, Paul Barton, andAsgeir Tomasgard propose several formulationsof stochastic programs with decision-dependent uncertainties and present a taxonomyof such problems. Vit Prochazka and Stein W.Wallace show how to save substantially123322 D. P. Morton et al.on the number of scenarios to be evaluated for exactly computing the function valuesin stochastic programs.Matthias Claus, Rüdiger Schultz, andKai Spürkel identify ver-ifiable sufficient conditions for strong convexity of objective functions in risk-aversetwo-stage stochastic programs.1.2 AlgorithmsAndy Philpott, Vitor L. de Matos, and Lea Kapelevich develop a new variant of thestochastic dual dynamic programming algorithm to handle a data-driven distribution-ally robust formulation, with application to hydroelectric scheduling. Eli Towle andJames Luedtke propose two new methods to solve the maximum-reliability stochasticnetwork interdiction problem (SNIP) based on a compact deterministic equivalent for-mulation and a path-based formulation of SNIP, respectively. For dynamic mixed 0-1optimization problems with strategic and operational stochastic parameters LaureanoF. Escudero and Juan F. Monge present a modeling framework for risk neutral andrisk averse approaches and outline a solution procedure based on nested stochasticdecomposition. Semih Atakan and Suvrajeet Sen present a new framework allowingfor effective utilization of Progressive Hedging guaranteeing convergence to globallyoptimal solutions of mixed-integer stochastic convex programs.1.3 ApplicationsJing Voon Chen, Julia L. Higle, and Michael Hintlian formulate a problem for modelcalibration of a Markov chain governing disease progression via a constrained opti-mizationmodel,where the constraints capturemodel validity, probabilistic knowledge,clinical judgement, and more. Bismark Singh, David P. Morton, and Surya Santosomodel integration of a conventional electric power generator and a wind farm, whoseoutput is uncertain, in the context of a day-ahead electricity market. Miguel A. Leje-une and Janne Kettunen propose a new fractional stochastic integer programmingmodel for forestry revenue management that includes a joint chance constraint withmultirow random technology matrix and a joint integrated chance constraint. Gior-gio Consigli, Vittorio Moriggia, Sebastiano Vitali, and Lorenzo Mercuri model anasset-liability management problem of a property and casualty (PC) company usinga dynamic stochastic program and they analyze the relationship between risk capitalconstraints and risk-adjusted performance of a PC portfolio.ReferencesAlbareda-Sambola M, van der Vlerk MH, Fernández E (2006) Exact solutions to a class of stochasticgeneralized assignment problems. Eur J Oper Res 173:465–487Cremers MLAG, Klein Haneveld WK, van der Vlerk MH (2009) A two-stage model for a day-aheadparatransit planning problem. Math Methods Oper Res 69:323–341Klein Haneveld WK, van der Vlerk MH (2006) Integrated chance constraints: reduced forms and an algo-rithm. Comput Manag Sci 3:245–269Klein HaneveldWK, Stougie L, van der VlerkMH (1995) On the convex hull of the simple integer recourseobjective function. Ann Oper Res 56:209–224123The stochastic programming heritage of Maarten van der Vlerk 323Klein Haneveld WK, Stougie L, van der Vlerk MH (1996) An algorithm for the construction of the convexhull of the simple integer recourse objective function. Ann Oper Res 64:67–81Klein Haneveld WK, Stougie L, van der Vlerk MH (2006) Simple integer recourse models: convexity andconvex approximations. Math Program 108:435–473Klein Haneveld WK, Streutker MH, van der Vlerk MH (2011) Collective adjustment of pension rights inALM models. Comput Manag Sci 8:137–156Louveaux F, van der VlerkMH (1993) Stochastic programmingwith simple integer recourse.Math Program61:301–325Maatman A, Schweigman C, Ruijs A, van der Vlerk MH (2002) Modeling farmers’ response to uncertainrainfall in Burkina Faso: a stochastic programming approach. Oper Res 50:399–414Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2015) Convex approximations for totally uni-modular integer recourse models: a uniform error bound. SIAM J Optim 25:130–158Romeijnders W, Schultz R, van der Vlerk MH, Klein Haneveld WK (2016a) A convex approximation fortwo-stage mixed-integer recourse models with a uniform error bound. SIAM J Optim 26:426–447Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2016b) Total variation bounds on the expectationof periodic functions with applications to recourse approximations. Math Program 157:3–46Romeijnders W, Morton DP, van der Vlerk MH (2017) Assessing the quality of convex approximations fortwo-stage totally unimodular integer recourse models. INFORMS J Comput 29:211–231Schultz R, Stougie L, van der Vlerk MH (1998) Solving stochastic integer programs by enumeration: aframework using Gröbner basis reductions. Math Program 83:229–252Tomasgard A, Audestad JA, Dye S, Stougie L, van der Vlerk MH, Wallace SW (1998) Modelling aspectsof distributed processing in telecommunication networks. Ann Oper Res 82:161–184van der Vlerk MH (1995) Stochastic programming with integer recourse, Thesis Rijksuniversiteit Gronin-gen, Theses on Systems, Organisations, and Management. Labyrint Publications, Capelle a/d Ijsselvan der Vlerk MH (2004) Convex approximations for complete integer recourse models. Math Program99:297–310van der Vlerk MH (2010) Convex approximations for a class of mixed-integer recourse models. Ann OperRes 177:139–150AffiliationsDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6David P. Mortondavid.morton@northwestern.eduWard Romeijndersw.romeijnders@rug.nlLeen Stougiestougie@cwi.nl1 Department of Industrial Engineering and Management Sciences, Northwestern University, 2145Sheridan Road, Evanston, IL 60208-3119, USA2 Department of Operations, Faculty of Economics and Business, University of Groningen,Nettelbosje 2, 9747 AE Groningen, The Netherlands3 Faculty of Mathematics, University of Duisburg-Essen, Thea-Leymann-Str. 9, 45127 Essen,Germany4 CWI, Science Park 123, 1098 XG Amsterdam, The Netherlands5 INRIA-Erable, Amsterdam, The Netherlands6 Vrije Universiteit, Amsterdam, The Netherlands123

English

The stochastic programming heritage of Maarten van der Vlerk

ARTS repository - University of Groningen

   University of GroningenThe stochastic programming heritage of Maarten van der VlerkMorton, D.P.; Romeijnders, Ward; Schultz, R.; Stougie, L.Published in:Computational Management ScienceDOI:10.1007/s10287-018-0335-8IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2018Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Morton, D. P., Romeijnders, W., Schultz, R., & Stougie, L. (2018). The stochastic programming heritage ofMaarten van der Vlerk. Computational Management Science, 15(3-4), 319-323.https://doi.org/10.1007/s10287-018-0335-8CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 13-02-2023Computational Management Science (2018) 15:319–323https://doi.org/10.1007/s10287-018-0335-8EDITORIALThe stochastic programming heritage of Maarten van derVlerkDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6On October 9, 2016, Professor Maarten H. van der Vlerk died tragically. This special issue is dedicated tohis memory.Published online: 6 September 2018© Springer-Verlag GmbH Germany, part of Springer Nature 2018Maarten van der Vlerk was born in the year 1961 in Assen, The Netherlands, wherehe spent his childhood and attended school up to the A-level, confirming the req-uisite maturity for university studies. At the University of Groningen he became astudent in the Department of Econometrics and commenced his doctoral studies thereunder the supervision of Wim Klein Haneveld and Leen Stougie in 1990. Invited andencouraged by his supervisors, Maarten placed a research focus on the integer pro-gramming side of stochastic optimization. Soon, he discovered his first original resultsculminating in a 1993 paper in Mathematical Programming with François Louveauxof the Facultés Universitaires Notre Dame de la Paix in Namur, on “Stochastic pro-gramming with simple integer recourse” (Louveaux and van der Vlerk 1993). Withhis thesis “Stochastic programming with integer recourse”, Maarten was awarded hisPh.D. degree in Economics from the University of Groningen in 1995 (van der Vlerk1995).After a year as postdoc at the Center for Operations Research and Economet-rics (CORE) at the Université Catholique de Louvain at Louvain-la-Neuve, Maartenreturned to Groningen. In 1999 he received a prestigious research fellowship fromthe Royal Netherlands Academy of Sciences. In 2008, Maarten became Professor ofStochastic Optimization at the University of Groningen, Faculty of Economics andBusiness, Department of Operations.From the mid 1990s on, stochastic integer programming—with its structures to bedetected, its algorithms to be constructed, and, last but not least, its real-life appli-cations to be developed—became the field where Maarten van der Vlerk’s researchearned highest recognition in the stochastic programming community and beyond. Apermanently recurring research target of Maarten, and his various coauthors, has beenconvex approximation of the notoriously non-convex, discontinuous, mixed-integerB Rüdiger Schultzruediger.schultz@uni-due.deExtended author information available on the last page of the article123320 D. P. Morton et al.expected recourse function, together with its algorithmic utilization. Papers devotedto these topics range over a time span of more than 20 years.Maarten’s publications on structures began with “On the convex hull of the sim-ple integer recourse objective function” jointly with Wim Klein Haneveld and LeenStougie in Annals of Operations Research in 1995 (Klein Haneveld et al. 1995).His work on structures continued with “Convex approximations for complete inte-ger recourse models”, Mathematical Programming (2004) (van der Vlerk 2004), and“Simple integer recourse models: convexity and convex approximations”,Mathemat-ical Programming (2006) (Klein Haneveld et al. 2006), the latter again jointly withWim Klein Haneveld and Leen Stougie.Our understanding of the structure of integer recourse problems received anotherboost with Maarten’s 2015 paper in SIAM Journal on Optimization, “Convex approx-imations for totally unimodular integer recourse models: a uniform error bound”(Romeijnders et al. 2015), written together with Wim Klein Haneveld and their co-advised doctoral student, Ward Romeijnders. Departure points for these investigationsinvolve periodicity properties ofmixed-integer value functions and their interplaywiththe total variation of density functions. This line of research—extended with journalpublications (Romeijnders et al. 2016a, b)—continues ever since and, in fact, leadsinto the present special issue, whose first article has been coauthored by Maarten vander Vlerk.Along with structural investigation, Maarten always had an eye on algorithmsand computations. Already in a 1996 paper, “An algorithm for the construction ofthe convex hull of the simple integer recourse objective function” (Klein Haneveldet al. 1996) in Annals of Operations Research, jointly with Wim Klein Haneveld andLeen Stougie, an algorithm is proposed for finding the convex hull, i.e., tight lowerbounds, for the objective function. Detection of convex hulls for recourse functionalshas always been accompanied by numerical testing. A 2017 paper jointly with DavidMorton and Ward Romeijnders in INFORMS Journal on Computing is an example ofthe latter.In 1998, Maarten, jointly with Rüdiger Schultz and Leen Stougie, published inMathematical Programing an implicit-enumeration algorithm for two-stage stochasticinteger programs, where function value calculation is enhanced by techniques fromGröbner bases in computational algebra (Schultz et al. 1998). This work initiatedconsiderable subsequent research on primal augmentation in stochastic programming.Another topicMaarten dealt with, often in collaboration withWimKlein Haneveld,concerns integrated chance constraints. In a 2006 joint paper (Klein Haneveld andvan der Vlerk 2006), they analyze structures and propose algorithms for numericallyhandling these instruments for risk aversion.Today, almost any decision to be taken with an aspiration for being “best” isexposed to uncertainty, and stochastic optimization is increasingly the method ofchoice for practical planning under uncertainty. This is reflected by the variety ofpractical applicationsMaarten van der Vlerk worked on. To list a few, we start withtelecommunications, or more precisely, distributed processing in telecommunicationnetworks (Tomasgard et al. 1998), with Asgeir Tomasgard, Jan A. Audestad, ShaneDye, Leen Stougie, and Stein W. Wallace. The list continues with generalized assign-ment problems (Albareda-Sambola et al. 2006), with Maria Albareda-Sambola and123The stochastic programming heritage of Maarten van der Vlerk 321Elena Fernández, day-ahead paratransit planning (Cremers et al. 2009), with MariaL.A.G. Cremers and Wim Klein Haneveld, asset and liability management (KleinHaneveld et al. 2011), with Wim Klein Haneveld and Matthijs H. Streutker, as well asa joint project with Arno Maatman, Caspar Schweigman, and Arjan Ruijs on “Model-ing farmers’ response to uncertain rainfall in Burkina Faso: a stochastic programmingapproach” in Maatman et al. (2002). The intent and contribution of this project isdescribed best by the following quotation from the paper, “This study draws impor-tant policy conclusions regarding reorientation of research programs and illustrateshow operations research techniques can be usefully applied to study grass root prob-lems in developing countries.”In addition to his research contributions, Maarten continually provided leadership,developing the Stochastic Programming Bibliography early in his career, serving asSecretary and then Chair of theCommittee on Stochastic Programming, and serving asProgramDirector for the Bachelor of Science in Econometrics &Operations Researchand for the Master of Science in Econometrics, Operations Research & ActuarialStudies at the University of Groningen. Maarten was an excellent teacher, winningthe Lecturer of the Year Award in 2014 from the University of Groningen’s Faculty ofEconomics and Business. Maarten cared deeply about those in his communities, andhad a strong sense of integrity and social justice, advocating for the homeless whenhe was still in high school and holding to important principles without fail. Apartfrom beautiful mathematics Maarten enjoyed many other good things in life, which hehappily shared with his friends and colleagues: music, in particular Bach, and tennis,which he made a fixed point of the social program of any stochastic programmingmeeting.Wewill missMaarten’s boisterous laugh, his warm smile, his sense of humor,and his ability to make everyone around him a better person.1 This special issueMaarten’s articles have inspired, and will continue to inspire, contemporary researchin stochastic (mixed-integer) optimization. This is illustrated by the research laid out inthe present special issue. The lead article is a paper that Maarten van der Vlerk workedonwith Niels van der Laan andWard Romeijnders, improvingMaarten’s original errorbounds on convex approximations for simple integer recourse models. The remainingarticles are grouped according to Maarten’s research interests: exploring structures,designing algorithms, and solving practical applications.1.1 StructuresWeijun Xie and Shabbir Ahmed study a distributionally robust version of the simpleinteger recoursemodel introduced byMaarten, and they derive a closed form analyticalexpression for the objective and represent it by amixed-integer second-order cone pro-gram. Lars Hellemo, Paul Barton, andAsgeir Tomasgard propose several formulationsof stochastic programs with decision-dependent uncertainties and present a taxonomyof such problems. Vit Prochazka and Stein W.Wallace show how to save substantially123322 D. P. Morton et al.on the number of scenarios to be evaluated for exactly computing the function valuesin stochastic programs.Matthias Claus, Rüdiger Schultz, andKai Spürkel identify ver-ifiable sufficient conditions for strong convexity of objective functions in risk-aversetwo-stage stochastic programs.1.2 AlgorithmsAndy Philpott, Vitor L. de Matos, and Lea Kapelevich develop a new variant of thestochastic dual dynamic programming algorithm to handle a data-driven distribution-ally robust formulation, with application to hydroelectric scheduling. Eli Towle andJames Luedtke propose two new methods to solve the maximum-reliability stochasticnetwork interdiction problem (SNIP) based on a compact deterministic equivalent for-mulation and a path-based formulation of SNIP, respectively. For dynamic mixed 0-1optimization problems with strategic and operational stochastic parameters LaureanoF. Escudero and Juan F. Monge present a modeling framework for risk neutral andrisk averse approaches and outline a solution procedure based on nested stochasticdecomposition. Semih Atakan and Suvrajeet Sen present a new framework allowingfor effective utilization of Progressive Hedging guaranteeing convergence to globallyoptimal solutions of mixed-integer stochastic convex programs.1.3 ApplicationsJing Voon Chen, Julia L. Higle, and Michael Hintlian formulate a problem for modelcalibration of a Markov chain governing disease progression via a constrained opti-mizationmodel,where the constraints capturemodel validity, probabilistic knowledge,clinical judgement, and more. Bismark Singh, David P. Morton, and Surya Santosomodel integration of a conventional electric power generator and a wind farm, whoseoutput is uncertain, in the context of a day-ahead electricity market. Miguel A. Leje-une and Janne Kettunen propose a new fractional stochastic integer programmingmodel for forestry revenue management that includes a joint chance constraint withmultirow random technology matrix and a joint integrated chance constraint. Gior-gio Consigli, Vittorio Moriggia, Sebastiano Vitali, and Lorenzo Mercuri model anasset-liability management problem of a property and casualty (PC) company usinga dynamic stochastic program and they analyze the relationship between risk capitalconstraints and risk-adjusted performance of a PC portfolio.ReferencesAlbareda-Sambola M, van der Vlerk MH, Fernández E (2006) Exact solutions to a class of stochasticgeneralized assignment problems. Eur J Oper Res 173:465–487Cremers MLAG, Klein Haneveld WK, van der Vlerk MH (2009) A two-stage model for a day-aheadparatransit planning problem. Math Methods Oper Res 69:323–341Klein Haneveld WK, van der Vlerk MH (2006) Integrated chance constraints: reduced forms and an algo-rithm. Comput Manag Sci 3:245–269Klein HaneveldWK, Stougie L, van der VlerkMH (1995) On the convex hull of the simple integer recourseobjective function. Ann Oper Res 56:209–224123The stochastic programming heritage of Maarten van der Vlerk 323Klein Haneveld WK, Stougie L, van der Vlerk MH (1996) An algorithm for the construction of the convexhull of the simple integer recourse objective function. Ann Oper Res 64:67–81Klein Haneveld WK, Stougie L, van der Vlerk MH (2006) Simple integer recourse models: convexity andconvex approximations. Math Program 108:435–473Klein Haneveld WK, Streutker MH, van der Vlerk MH (2011) Collective adjustment of pension rights inALM models. Comput Manag Sci 8:137–156Louveaux F, van der VlerkMH (1993) Stochastic programmingwith simple integer recourse.Math Program61:301–325Maatman A, Schweigman C, Ruijs A, van der Vlerk MH (2002) Modeling farmers’ response to uncertainrainfall in Burkina Faso: a stochastic programming approach. Oper Res 50:399–414Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2015) Convex approximations for totally uni-modular integer recourse models: a uniform error bound. SIAM J Optim 25:130–158Romeijnders W, Schultz R, van der Vlerk MH, Klein Haneveld WK (2016a) A convex approximation fortwo-stage mixed-integer recourse models with a uniform error bound. SIAM J Optim 26:426–447Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2016b) Total variation bounds on the expectationof periodic functions with applications to recourse approximations. Math Program 157:3–46Romeijnders W, Morton DP, van der Vlerk MH (2017) Assessing the quality of convex approximations fortwo-stage totally unimodular integer recourse models. INFORMS J Comput 29:211–231Schultz R, Stougie L, van der Vlerk MH (1998) Solving stochastic integer programs by enumeration: aframework using Gröbner basis reductions. Math Program 83:229–252Tomasgard A, Audestad JA, Dye S, Stougie L, van der Vlerk MH, Wallace SW (1998) Modelling aspectsof distributed processing in telecommunication networks. Ann Oper Res 82:161–184van der Vlerk MH (1995) Stochastic programming with integer recourse, Thesis Rijksuniversiteit Gronin-gen, Theses on Systems, Organisations, and Management. Labyrint Publications, Capelle a/d Ijsselvan der Vlerk MH (2004) Convex approximations for complete integer recourse models. Math Program99:297–310van der Vlerk MH (2010) Convex approximations for a class of mixed-integer recourse models. Ann OperRes 177:139–150AffiliationsDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6David P. Mortondavid.morton@northwestern.eduWard Romeijndersw.romeijnders@rug.nlLeen Stougiestougie@cwi.nl1 Department of Industrial Engineering and Management Sciences, Northwestern University, 2145Sheridan Road, Evanston, IL 60208-3119, USA2 Department of Operations, Faculty of Economics and Business, University of Groningen,Nettelbosje 2, 9747 AE Groningen, The Netherlands3 Faculty of Mathematics, University of Duisburg-Essen, Thea-Leymann-Str. 9, 45127 Essen,Germany4 CWI, Science Park 123, 1098 XG Amsterdam, The Netherlands5 INRIA-Erable, Amsterdam, The Netherlands6 Vrije Universiteit, Amsterdam, The Netherlands123

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   University of GroningenThe stochastic programming heritage of Maarten van der VlerkMorton, D.P.; Romeijnders, Ward; Schultz, R.; Stougie, L.Published in:Computational Management ScienceDOI:10.1007/s10287-018-0335-8IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2018Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Morton, D. P., Romeijnders, W., Schultz, R., & Stougie, L. (2018). The stochastic programming heritage ofMaarten van der Vlerk. Computational Management Science, 15(3-4), 319-323. Advance online publication.https://doi.org/10.1007/s10287-018-0335-8CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 10-09-2023Computational Management Science (2018) 15:319–323https://doi.org/10.1007/s10287-018-0335-8EDITORIALThe stochastic programming heritage of Maarten van derVlerkDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6On October 9, 2016, Professor Maarten H. van der Vlerk died tragically. This special issue is dedicated tohis memory.Published online: 6 September 2018© Springer-Verlag GmbH Germany, part of Springer Nature 2018Maarten van der Vlerk was born in the year 1961 in Assen, The Netherlands, wherehe spent his childhood and attended school up to the A-level, confirming the req-uisite maturity for university studies. At the University of Groningen he became astudent in the Department of Econometrics and commenced his doctoral studies thereunder the supervision of Wim Klein Haneveld and Leen Stougie in 1990. Invited andencouraged by his supervisors, Maarten placed a research focus on the integer pro-gramming side of stochastic optimization. Soon, he discovered his first original resultsculminating in a 1993 paper in Mathematical Programming with François Louveauxof the Facultés Universitaires Notre Dame de la Paix in Namur, on “Stochastic pro-gramming with simple integer recourse” (Louveaux and van der Vlerk 1993). Withhis thesis “Stochastic programming with integer recourse”, Maarten was awarded hisPh.D. degree in Economics from the University of Groningen in 1995 (van der Vlerk1995).After a year as postdoc at the Center for Operations Research and Economet-rics (CORE) at the Université Catholique de Louvain at Louvain-la-Neuve, Maartenreturned to Groningen. In 1999 he received a prestigious research fellowship fromthe Royal Netherlands Academy of Sciences. In 2008, Maarten became Professor ofStochastic Optimization at the University of Groningen, Faculty of Economics andBusiness, Department of Operations.From the mid 1990s on, stochastic integer programming—with its structures to bedetected, its algorithms to be constructed, and, last but not least, its real-life appli-cations to be developed—became the field where Maarten van der Vlerk’s researchearned highest recognition in the stochastic programming community and beyond. Apermanently recurring research target of Maarten, and his various coauthors, has beenconvex approximation of the notoriously non-convex, discontinuous, mixed-integerB Rüdiger Schultzruediger.schultz@uni-due.deExtended author information available on the last page of the article123320 D. P. Morton et al.expected recourse function, together with its algorithmic utilization. Papers devotedto these topics range over a time span of more than 20 years.Maarten’s publications on structures began with “On the convex hull of the sim-ple integer recourse objective function” jointly with Wim Klein Haneveld and LeenStougie in Annals of Operations Research in 1995 (Klein Haneveld et al. 1995).His work on structures continued with “Convex approximations for complete inte-ger recourse models”, Mathematical Programming (2004) (van der Vlerk 2004), and“Simple integer recourse models: convexity and convex approximations”,Mathemat-ical Programming (2006) (Klein Haneveld et al. 2006), the latter again jointly withWim Klein Haneveld and Leen Stougie.Our understanding of the structure of integer recourse problems received anotherboost with Maarten’s 2015 paper in SIAM Journal on Optimization, “Convex approx-imations for totally unimodular integer recourse models: a uniform error bound”(Romeijnders et al. 2015), written together with Wim Klein Haneveld and their co-advised doctoral student, Ward Romeijnders. Departure points for these investigationsinvolve periodicity properties ofmixed-integer value functions and their interplaywiththe total variation of density functions. This line of research—extended with journalpublications (Romeijnders et al. 2016a, b)—continues ever since and, in fact, leadsinto the present special issue, whose first article has been coauthored by Maarten vander Vlerk.Along with structural investigation, Maarten always had an eye on algorithmsand computations. Already in a 1996 paper, “An algorithm for the construction ofthe convex hull of the simple integer recourse objective function” (Klein Haneveldet al. 1996) in Annals of Operations Research, jointly with Wim Klein Haneveld andLeen Stougie, an algorithm is proposed for finding the convex hull, i.e., tight lowerbounds, for the objective function. Detection of convex hulls for recourse functionalshas always been accompanied by numerical testing. A 2017 paper jointly with DavidMorton and Ward Romeijnders in INFORMS Journal on Computing is an example ofthe latter.In 1998, Maarten, jointly with Rüdiger Schultz and Leen Stougie, published inMathematical Programing an implicit-enumeration algorithm for two-stage stochasticinteger programs, where function value calculation is enhanced by techniques fromGröbner bases in computational algebra (Schultz et al. 1998). This work initiatedconsiderable subsequent research on primal augmentation in stochastic programming.Another topicMaarten dealt with, often in collaboration withWimKlein Haneveld,concerns integrated chance constraints. In a 2006 joint paper (Klein Haneveld andvan der Vlerk 2006), they analyze structures and propose algorithms for numericallyhandling these instruments for risk aversion.Today, almost any decision to be taken with an aspiration for being “best” isexposed to uncertainty, and stochastic optimization is increasingly the method ofchoice for practical planning under uncertainty. This is reflected by the variety ofpractical applicationsMaarten van der Vlerk worked on. To list a few, we start withtelecommunications, or more precisely, distributed processing in telecommunicationnetworks (Tomasgard et al. 1998), with Asgeir Tomasgard, Jan A. Audestad, ShaneDye, Leen Stougie, and Stein W. Wallace. The list continues with generalized assign-ment problems (Albareda-Sambola et al. 2006), with Maria Albareda-Sambola and123The stochastic programming heritage of Maarten van der Vlerk 321Elena Fernández, day-ahead paratransit planning (Cremers et al. 2009), with MariaL.A.G. Cremers and Wim Klein Haneveld, asset and liability management (KleinHaneveld et al. 2011), with Wim Klein Haneveld and Matthijs H. Streutker, as well asa joint project with Arno Maatman, Caspar Schweigman, and Arjan Ruijs on “Model-ing farmers’ response to uncertain rainfall in Burkina Faso: a stochastic programmingapproach” in Maatman et al. (2002). The intent and contribution of this project isdescribed best by the following quotation from the paper, “This study draws impor-tant policy conclusions regarding reorientation of research programs and illustrateshow operations research techniques can be usefully applied to study grass root prob-lems in developing countries.”In addition to his research contributions, Maarten continually provided leadership,developing the Stochastic Programming Bibliography early in his career, serving asSecretary and then Chair of theCommittee on Stochastic Programming, and serving asProgramDirector for the Bachelor of Science in Econometrics &Operations Researchand for the Master of Science in Econometrics, Operations Research & ActuarialStudies at the University of Groningen. Maarten was an excellent teacher, winningthe Lecturer of the Year Award in 2014 from the University of Groningen’s Faculty ofEconomics and Business. Maarten cared deeply about those in his communities, andhad a strong sense of integrity and social justice, advocating for the homeless whenhe was still in high school and holding to important principles without fail. Apartfrom beautiful mathematics Maarten enjoyed many other good things in life, which hehappily shared with his friends and colleagues: music, in particular Bach, and tennis,which he made a fixed point of the social program of any stochastic programmingmeeting.Wewill missMaarten’s boisterous laugh, his warm smile, his sense of humor,and his ability to make everyone around him a better person.1 This special issueMaarten’s articles have inspired, and will continue to inspire, contemporary researchin stochastic (mixed-integer) optimization. This is illustrated by the research laid out inthe present special issue. The lead article is a paper that Maarten van der Vlerk workedonwith Niels van der Laan andWard Romeijnders, improvingMaarten’s original errorbounds on convex approximations for simple integer recourse models. The remainingarticles are grouped according to Maarten’s research interests: exploring structures,designing algorithms, and solving practical applications.1.1 StructuresWeijun Xie and Shabbir Ahmed study a distributionally robust version of the simpleinteger recoursemodel introduced byMaarten, and they derive a closed form analyticalexpression for the objective and represent it by amixed-integer second-order cone pro-gram. Lars Hellemo, Paul Barton, andAsgeir Tomasgard propose several formulationsof stochastic programs with decision-dependent uncertainties and present a taxonomyof such problems. Vit Prochazka and Stein W.Wallace show how to save substantially123322 D. P. Morton et al.on the number of scenarios to be evaluated for exactly computing the function valuesin stochastic programs.Matthias Claus, Rüdiger Schultz, andKai Spürkel identify ver-ifiable sufficient conditions for strong convexity of objective functions in risk-aversetwo-stage stochastic programs.1.2 AlgorithmsAndy Philpott, Vitor L. de Matos, and Lea Kapelevich develop a new variant of thestochastic dual dynamic programming algorithm to handle a data-driven distribution-ally robust formulation, with application to hydroelectric scheduling. Eli Towle andJames Luedtke propose two new methods to solve the maximum-reliability stochasticnetwork interdiction problem (SNIP) based on a compact deterministic equivalent for-mulation and a path-based formulation of SNIP, respectively. For dynamic mixed 0-1optimization problems with strategic and operational stochastic parameters LaureanoF. Escudero and Juan F. Monge present a modeling framework for risk neutral andrisk averse approaches and outline a solution procedure based on nested stochasticdecomposition. Semih Atakan and Suvrajeet Sen present a new framework allowingfor effective utilization of Progressive Hedging guaranteeing convergence to globallyoptimal solutions of mixed-integer stochastic convex programs.1.3 ApplicationsJing Voon Chen, Julia L. Higle, and Michael Hintlian formulate a problem for modelcalibration of a Markov chain governing disease progression via a constrained opti-mizationmodel,where the constraints capturemodel validity, probabilistic knowledge,clinical judgement, and more. Bismark Singh, David P. Morton, and Surya Santosomodel integration of a conventional electric power generator and a wind farm, whoseoutput is uncertain, in the context of a day-ahead electricity market. Miguel A. Leje-une and Janne Kettunen propose a new fractional stochastic integer programmingmodel for forestry revenue management that includes a joint chance constraint withmultirow random technology matrix and a joint integrated chance constraint. Gior-gio Consigli, Vittorio Moriggia, Sebastiano Vitali, and Lorenzo Mercuri model anasset-liability management problem of a property and casualty (PC) company usinga dynamic stochastic program and they analyze the relationship between risk capitalconstraints and risk-adjusted performance of a PC portfolio.ReferencesAlbareda-Sambola M, van der Vlerk MH, Fernández E (2006) Exact solutions to a class of stochasticgeneralized assignment problems. Eur J Oper Res 173:465–487Cremers MLAG, Klein Haneveld WK, van der Vlerk MH (2009) A two-stage model for a day-aheadparatransit planning problem. Math Methods Oper Res 69:323–341Klein Haneveld WK, van der Vlerk MH (2006) Integrated chance constraints: reduced forms and an algo-rithm. Comput Manag Sci 3:245–269Klein HaneveldWK, Stougie L, van der VlerkMH (1995) On the convex hull of the simple integer recourseobjective function. Ann Oper Res 56:209–224123The stochastic programming heritage of Maarten van der Vlerk 323Klein Haneveld WK, Stougie L, van der Vlerk MH (1996) An algorithm for the construction of the convexhull of the simple integer recourse objective function. Ann Oper Res 64:67–81Klein Haneveld WK, Stougie L, van der Vlerk MH (2006) Simple integer recourse models: convexity andconvex approximations. Math Program 108:435–473Klein Haneveld WK, Streutker MH, van der Vlerk MH (2011) Collective adjustment of pension rights inALM models. Comput Manag Sci 8:137–156Louveaux F, van der VlerkMH (1993) Stochastic programmingwith simple integer recourse.Math Program61:301–325Maatman A, Schweigman C, Ruijs A, van der Vlerk MH (2002) Modeling farmers’ response to uncertainrainfall in Burkina Faso: a stochastic programming approach. Oper Res 50:399–414Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2015) Convex approximations for totally uni-modular integer recourse models: a uniform error bound. SIAM J Optim 25:130–158Romeijnders W, Schultz R, van der Vlerk MH, Klein Haneveld WK (2016a) A convex approximation fortwo-stage mixed-integer recourse models with a uniform error bound. SIAM J Optim 26:426–447Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2016b) Total variation bounds on the expectationof periodic functions with applications to recourse approximations. Math Program 157:3–46Romeijnders W, Morton DP, van der Vlerk MH (2017) Assessing the quality of convex approximations fortwo-stage totally unimodular integer recourse models. INFORMS J Comput 29:211–231Schultz R, Stougie L, van der Vlerk MH (1998) Solving stochastic integer programs by enumeration: aframework using Gröbner basis reductions. Math Program 83:229–252Tomasgard A, Audestad JA, Dye S, Stougie L, van der Vlerk MH, Wallace SW (1998) Modelling aspectsof distributed processing in telecommunication networks. Ann Oper Res 82:161–184van der Vlerk MH (1995) Stochastic programming with integer recourse, Thesis Rijksuniversiteit Gronin-gen, Theses on Systems, Organisations, and Management. Labyrint Publications, Capelle a/d Ijsselvan der Vlerk MH (2004) Convex approximations for complete integer recourse models. Math Program99:297–310van der Vlerk MH (2010) Convex approximations for a class of mixed-integer recourse models. Ann OperRes 177:139–150AffiliationsDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6David P. Mortondavid.morton@northwestern.eduWard Romeijndersw.romeijnders@rug.nlLeen Stougiestougie@cwi.nl1 Department of Industrial Engineering and Management Sciences, Northwestern University, 2145Sheridan Road, Evanston, IL 60208-3119, USA2 Department of Operations, Faculty of Economics and Business, University of Groningen,Nettelbosje 2, 9747 AE Groningen, The Netherlands3 Faculty of Mathematics, University of Duisburg-Essen, Thea-Leymann-Str. 9, 45127 Essen,Germany4 CWI, Science Park 123, 1098 XG Amsterdam, The Netherlands5 INRIA-Erable, Amsterdam, The Netherlands6 Vrije Universiteit, Amsterdam, The Netherlands123

Proceedings - University of Groningen

Morton, D.P. (David)

Romeijnders, W. (Ward)

Schultz, R. (R\xc3\xbcdiger)

Stougie, L. (Leen)

CWI's Institutional Repository

Computational Management Science (2018) 15:319–323https://doi.org/10.1007/s10287-018-0335-8EDITORIALThe stochastic programming heritage of Maarten van derVlerkDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6On October 9, 2016, Professor Maarten H. van der Vlerk died tragically. This special issue is dedicated tohis memory.Published online: 6 September 2018© Springer-Verlag GmbH Germany, part of Springer Nature 2018Maarten van der Vlerk was born in the year 1961 in Assen, The Netherlands, wherehe spent his childhood and attended school up to the A-level, confirming the req-uisite maturity for university studies. At the University of Groningen he became astudent in the Department of Econometrics and commenced his doctoral studies thereunder the supervision of Wim Klein Haneveld and Leen Stougie in 1990. Invited andencouraged by his supervisors, Maarten placed a research focus on the integer pro-gramming side of stochastic optimization. Soon, he discovered his first original resultsculminating in a 1993 paper in Mathematical Programming with François Louveauxof the Facultés Universitaires Notre Dame de la Paix in Namur, on “Stochastic pro-gramming with simple integer recourse” (Louveaux and van der Vlerk 1993). Withhis thesis “Stochastic programming with integer recourse”, Maarten was awarded hisPh.D. degree in Economics from the University of Groningen in 1995 (van der Vlerk1995).After a year as postdoc at the Center for Operations Research and Economet-rics (CORE) at the Université Catholique de Louvain at Louvain-la-Neuve, Maartenreturned to Groningen. In 1999 he received a prestigious research fellowship fromthe Royal Netherlands Academy of Sciences. In 2008, Maarten became Professor ofStochastic Optimization at the University of Groningen, Faculty of Economics andBusiness, Department of Operations.From the mid 1990s on, stochastic integer programming—with its structures to bedetected, its algorithms to be constructed, and, last but not least, its real-life appli-cations to be developed—became the field where Maarten van der Vlerk’s researchearned highest recognition in the stochastic programming community and beyond. Apermanently recurring research target of Maarten, and his various coauthors, has beenconvex approximation of the notoriously non-convex, discontinuous, mixed-integerB Rüdiger Schultzruediger.schultz@uni-due.deExtended author information available on the last page of the article123320 D. P. Morton et al.expected recourse function, together with its algorithmic utilization. Papers devotedto these topics range over a time span of more than 20 years.Maarten’s publications on structures began with “On the convex hull of the sim-ple integer recourse objective function” jointly with Wim Klein Haneveld and LeenStougie in Annals of Operations Research in 1995 (Klein Haneveld et al. 1995).His work on structures continued with “Convex approximations for complete inte-ger recourse models”, Mathematical Programming (2004) (van der Vlerk 2004), and“Simple integer recourse models: convexity and convex approximations”, Mathemat-ical Programming (2006) (Klein Haneveld et al. 2006), the latter again jointly withWim Klein Haneveld and Leen Stougie.Our understanding of the structure of integer recourse problems received anotherboost with Maarten’s 2015 paper in SIAM Journal on Optimization, “Convex approx-imations for totally unimodular integer recourse models: a uniform error bound”(Romeijnders et al. 2015), written together with Wim Klein Haneveld and their co-advised doctoral student, Ward Romeijnders. Departure points for these investigationsinvolve periodicity properties of mixed-integer value functions and their interplay withthe total variation of density functions. This line of research—extended with journalpublications (Romeijnders et al. 2016a, b)—continues ever since and, in fact, leadsinto the present special issue, whose first article has been coauthored by Maarten vander Vlerk.Along with structural investigation, Maarten always had an eye on algorithmsand computations. Already in a 1996 paper, “An algorithm for the construction ofthe convex hull of the simple integer recourse objective function” (Klein Haneveldet al. 1996) in Annals of Operations Research, jointly with Wim Klein Haneveld andLeen Stougie, an algorithm is proposed for finding the convex hull, i.e., tight lowerbounds, for the objective function. Detection of convex hulls for recourse functionalshas always been accompanied by numerical testing. A 2017 paper jointly with DavidMorton and Ward Romeijnders in INFORMS Journal on Computing is an example ofthe latter.In 1998, Maarten, jointly with Rüdiger Schultz and Leen Stougie, published inMathematical Programing an implicit-enumeration algorithm for two-stage stochasticinteger programs, where function value calculation is enhanced by techniques fromGröbner bases in computational algebra (Schultz et al. 1998). This work initiatedconsiderable subsequent research on primal augmentation in stochastic programming.Another topic Maarten dealt with, often in collaboration with Wim Klein Haneveld,concerns integrated chance constraints. In a 2006 joint paper (Klein Haneveld andvan der Vlerk 2006), they analyze structures and propose algorithms for numericallyhandling these instruments for risk aversion.Today, almost any decision to be taken with an aspiration for being “best” isexposed to uncertainty, and stochastic optimization is increasingly the method ofchoice for practical planning under uncertainty. This is reflected by the variety ofpractical applications Maarten van der Vlerk worked on. To list a few, we start withtelecommunications, or more precisely, distributed processing in telecommunicationnetworks (Tomasgard et al. 1998), with Asgeir Tomasgard, Jan A. Audestad, ShaneDye, Leen Stougie, and Stein W. Wallace. The list continues with generalized assign-ment problems (Albareda-Sambola et al. 2006), with Maria Albareda-Sambola and123The stochastic programming heritage of Maarten van der Vlerk 321Elena Fernández, day-ahead paratransit planning (Cremers et al. 2009), with MariaL.A.G. Cremers and Wim Klein Haneveld, asset and liability management (KleinHaneveld et al. 2011), with Wim Klein Haneveld and Matthijs H. Streutker, as well asa joint project with Arno Maatman, Caspar Schweigman, and Arjan Ruijs on “Model-ing farmers’ response to uncertain rainfall in Burkina Faso: a stochastic programmingapproach” in Maatman et al. (2002). The intent and contribution of this project isdescribed best by the following quotation from the paper, “This study draws impor-tant policy conclusions regarding reorientation of research programs and illustrateshow operations research techniques can be usefully applied to study grass root prob-lems in developing countries.”In addition to his research contributions, Maarten continually provided leadership,developing the Stochastic Programming Bibliography early in his career, serving asSecretary and then Chair of the Committee on Stochastic Programming, and serving asProgram Director for the Bachelor of Science in Econometrics & Operations Researchand for the Master of Science in Econometrics, Operations Research & ActuarialStudies at the University of Groningen. Maarten was an excellent teacher, winningthe Lecturer of the Year Award in 2014 from the University of Groningen’s Faculty ofEconomics and Business. Maarten cared deeply about those in his communities, andhad a strong sense of integrity and social justice, advocating for the homeless whenhe was still in high school and holding to important principles without fail. Apartfrom beautiful mathematics Maarten enjoyed many other good things in life, which hehappily shared with his friends and colleagues: music, in particular Bach, and tennis,which he made a fixed point of the social program of any stochastic programmingmeeting. We will miss Maarten’s boisterous laugh, his warm smile, his sense of humor,and his ability to make everyone around him a better person.1 This special issueMaarten’s articles have inspired, and will continue to inspire, contemporary researchin stochastic (mixed-integer) optimization. This is illustrated by the research laid out inthe present special issue. The lead article is a paper that Maarten van der Vlerk workedon with Niels van der Laan and Ward Romeijnders, improving Maarten’s original errorbounds on convex approximations for simple integer recourse models. The remainingarticles are grouped according to Maarten’s research interests: exploring structures,designing algorithms, and solving practical applications.1.1 StructuresWeijun Xie and Shabbir Ahmed study a distributionally robust version of the simpleinteger recourse model introduced by Maarten, and they derive a closed form analyticalexpression for the objective and represent it by a mixed-integer second-order cone pro-gram. Lars Hellemo, Paul Barton, and Asgeir Tomasgard propose several formulationsof stochastic programs with decision-dependent uncertainties and present a taxonomyof such problems. Vit Prochazka and Stein W. Wallace show how to save substantially123322 D. P. Morton et al.on the number of scenarios to be evaluated for exactly computing the function valuesin stochastic programs. Matthias Claus, Rüdiger Schultz, and Kai Spürkel identify ver-ifiable sufficient conditions for strong convexity of objective functions in risk-aversetwo-stage stochastic programs.1.2 AlgorithmsAndy Philpott, Vitor L. de Matos, and Lea Kapelevich develop a new variant of thestochastic dual dynamic programming algorithm to handle a data-driven distribution-ally robust formulation, with application to hydroelectric scheduling. Eli Towle andJames Luedtke propose two new methods to solve the maximum-reliability stochasticnetwork interdiction problem (SNIP) based on a compact deterministic equivalent for-mulation and a path-based formulation of SNIP, respectively. For dynamic mixed 0-1optimization problems with strategic and operational stochastic parameters LaureanoF. Escudero and Juan F. Monge present a modeling framework for risk neutral andrisk averse approaches and outline a solution procedure based on nested stochasticdecomposition. Semih Atakan and Suvrajeet Sen present a new framework allowingfor effective utilization of Progressive Hedging guaranteeing convergence to globallyoptimal solutions of mixed-integer stochastic convex programs.1.3 ApplicationsJing Voon Chen, Julia L. Higle, and Michael Hintlian formulate a problem for modelcalibration of a Markov chain governing disease progression via a constrained opti-mization model, where the constraints capture model validity, probabilistic knowledge,clinical judgement, and more. Bismark Singh, David P. Morton, and Surya Santosomodel integration of a conventional electric power generator and a wind farm, whoseoutput is uncertain, in the context of a day-ahead electricity market. Miguel A. Leje-une and Janne Kettunen propose a new fractional stochastic integer programmingmodel for forestry revenue management that includes a joint chance constraint withmultirow random technology matrix and a joint integrated chance constraint. Gior-gio Consigli, Vittorio Moriggia, Sebastiano Vitali, and Lorenzo Mercuri model anasset-liability management problem of a property and casualty (PC) company usinga dynamic stochastic program and they analyze the relationship between risk capitalconstraints and risk-adjusted performance of a PC portfolio.ReferencesAlbareda-Sambola M, van der Vlerk MH, Fernández E (2006) Exact solutions to a class of stochasticgeneralized assignment problems. Eur J Oper Res 173:465–487Cremers MLAG, Klein Haneveld WK, van der Vlerk MH (2009) A two-stage model for a day-aheadparatransit planning problem. Math Methods Oper Res 69:323–341Klein Haneveld WK, van der Vlerk MH (2006) Integrated chance constraints: reduced forms and an algo-rithm. Comput Manag Sci 3:245–269Klein Haneveld WK, Stougie L, van der Vlerk MH (1995) On the convex hull of the simple integer recourseobjective function. Ann Oper Res 56:209–224123The stochastic programming heritage of Maarten van der Vlerk 323Klein Haneveld WK, Stougie L, van der Vlerk MH (1996) An algorithm for the construction of the convexhull of the simple integer recourse objective function. Ann Oper Res 64:67–81Klein Haneveld WK, Stougie L, van der Vlerk MH (2006) Simple integer recourse models: convexity andconvex approximations. Math Program 108:435–473Klein Haneveld WK, Streutker MH, van der Vlerk MH (2011) Collective adjustment of pension rights inALM models. Comput Manag Sci 8:137–156Louveaux F, van der Vlerk MH (1993) Stochastic programming with simple integer recourse. Math Program61:301–325Maatman A, Schweigman C, Ruijs A, van der Vlerk MH (2002) Modeling farmers’ response to uncertainrainfall in Burkina Faso: a stochastic programming approach. Oper Res 50:399–414Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2015) Convex approximations for totally uni-modular integer recourse models: a uniform error bound. SIAM J Optim 25:130–158Romeijnders W, Schultz R, van der Vlerk MH, Klein Haneveld WK (2016a) A convex approximation fortwo-stage mixed-integer recourse models with a uniform error bound. SIAM J Optim 26:426–447Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2016b) Total variation bounds on the expectationof periodic functions with applications to recourse approximations. Math Program 157:3–46Romeijnders W, Morton DP, van der Vlerk MH (2017) Assessing the quality of convex approximations fortwo-stage totally unimodular integer recourse models. INFORMS J Comput 29:211–231Schultz R, Stougie L, van der Vlerk MH (1998) Solving stochastic integer programs by enumeration: aframework using Gröbner basis reductions. Math Program 83:229–252Tomasgard A, Audestad JA, Dye S, Stougie L, van der Vlerk MH, Wallace SW (1998) Modelling aspectsof distributed processing in telecommunication networks. Ann Oper Res 82:161–184van der Vlerk MH (1995) Stochastic programming with integer recourse, Thesis Rijksuniversiteit Gronin-gen, Theses on Systems, Organisations, and Management. Labyrint Publications, Capelle a/d Ijsselvan der Vlerk MH (2004) Convex approximations for complete integer recourse models. Math Program99:297–310van der Vlerk MH (2010) Convex approximations for a class of mixed-integer recourse models. Ann OperRes 177:139–150AffiliationsDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6David P. Mortondavid.morton@northwestern.eduWard Romeijndersw.romeijnders@rug.nlLeen Stougiestougie@cwi.nl1 Department of Industrial Engineering and Management Sciences, Northwestern University, 2145Sheridan Road, Evanston, IL 60208-3119, USA2 Department of Operations, Faculty of Economics and Business, University of Groningen,Nettelbosje 2, 9747 AE Groningen, The Netherlands3 Faculty of Mathematics, University of Duisburg-Essen, Thea-Leymann-Str. 9, 45127 Essen,Germany4 CWI, Science Park 123, 1098 XG Amsterdam, The Netherlands5 INRIA-Erable, Amsterdam, The Netherlands6 Vrije Universiteit, Amsterdam, The Netherlands123

   University of GroningenThe stochastic programming heritage of Maarten van der VlerkMorton, D.P.; Romeijnders, Ward; Schultz, R.; Stougie, L.Published in:Computational Management ScienceDOI:10.1007/s10287-018-0335-8IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2018Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Morton, D. P., Romeijnders, W., Schultz, R., & Stougie, L. (2018). The stochastic programming heritage ofMaarten van der Vlerk. Computational Management Science, 15(3-4), 319-323.https://doi.org/10.1007/s10287-018-0335-8CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 03-06-2022Computational Management Science (2018) 15:319–323https://doi.org/10.1007/s10287-018-0335-8EDITORIALThe stochastic programming heritage of Maarten van derVlerkDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6On October 9, 2016, Professor Maarten H. van der Vlerk died tragically. This special issue is dedicated tohis memory.Published online: 6 September 2018© Springer-Verlag GmbH Germany, part of Springer Nature 2018Maarten van der Vlerk was born in the year 1961 in Assen, The Netherlands, wherehe spent his childhood and attended school up to the A-level, confirming the req-uisite maturity for university studies. At the University of Groningen he became astudent in the Department of Econometrics and commenced his doctoral studies thereunder the supervision of Wim Klein Haneveld and Leen Stougie in 1990. Invited andencouraged by his supervisors, Maarten placed a research focus on the integer pro-gramming side of stochastic optimization. Soon, he discovered his first original resultsculminating in a 1993 paper in Mathematical Programming with François Louveauxof the Facultés Universitaires Notre Dame de la Paix in Namur, on “Stochastic pro-gramming with simple integer recourse” (Louveaux and van der Vlerk 1993). Withhis thesis “Stochastic programming with integer recourse”, Maarten was awarded hisPh.D. degree in Economics from the University of Groningen in 1995 (van der Vlerk1995).After a year as postdoc at the Center for Operations Research and Economet-rics (CORE) at the Université Catholique de Louvain at Louvain-la-Neuve, Maartenreturned to Groningen. In 1999 he received a prestigious research fellowship fromthe Royal Netherlands Academy of Sciences. In 2008, Maarten became Professor ofStochastic Optimization at the University of Groningen, Faculty of Economics andBusiness, Department of Operations.From the mid 1990s on, stochastic integer programming—with its structures to bedetected, its algorithms to be constructed, and, last but not least, its real-life appli-cations to be developed—became the field where Maarten van der Vlerk’s researchearned highest recognition in the stochastic programming community and beyond. Apermanently recurring research target of Maarten, and his various coauthors, has beenconvex approximation of the notoriously non-convex, discontinuous, mixed-integerB Rüdiger Schultzruediger.schultz@uni-due.deExtended author information available on the last page of the article123320 D. P. Morton et al.expected recourse function, together with its algorithmic utilization. Papers devotedto these topics range over a time span of more than 20 years.Maarten’s publications on structures began with “On the convex hull of the sim-ple integer recourse objective function” jointly with Wim Klein Haneveld and LeenStougie in Annals of Operations Research in 1995 (Klein Haneveld et al. 1995).His work on structures continued with “Convex approximations for complete inte-ger recourse models”, Mathematical Programming (2004) (van der Vlerk 2004), and“Simple integer recourse models: convexity and convex approximations”,Mathemat-ical Programming (2006) (Klein Haneveld et al. 2006), the latter again jointly withWim Klein Haneveld and Leen Stougie.Our understanding of the structure of integer recourse problems received anotherboost with Maarten’s 2015 paper in SIAM Journal on Optimization, “Convex approx-imations for totally unimodular integer recourse models: a uniform error bound”(Romeijnders et al. 2015), written together with Wim Klein Haneveld and their co-advised doctoral student, Ward Romeijnders. Departure points for these investigationsinvolve periodicity properties ofmixed-integer value functions and their interplaywiththe total variation of density functions. This line of research—extended with journalpublications (Romeijnders et al. 2016a, b)—continues ever since and, in fact, leadsinto the present special issue, whose first article has been coauthored by Maarten vander Vlerk.Along with structural investigation, Maarten always had an eye on algorithmsand computations. Already in a 1996 paper, “An algorithm for the construction ofthe convex hull of the simple integer recourse objective function” (Klein Haneveldet al. 1996) in Annals of Operations Research, jointly with Wim Klein Haneveld andLeen Stougie, an algorithm is proposed for finding the convex hull, i.e., tight lowerbounds, for the objective function. Detection of convex hulls for recourse functionalshas always been accompanied by numerical testing. A 2017 paper jointly with DavidMorton and Ward Romeijnders in INFORMS Journal on Computing is an example ofthe latter.In 1998, Maarten, jointly with Rüdiger Schultz and Leen Stougie, published inMathematical Programing an implicit-enumeration algorithm for two-stage stochasticinteger programs, where function value calculation is enhanced by techniques fromGröbner bases in computational algebra (Schultz et al. 1998). This work initiatedconsiderable subsequent research on primal augmentation in stochastic programming.Another topicMaarten dealt with, often in collaboration withWimKlein Haneveld,concerns integrated chance constraints. In a 2006 joint paper (Klein Haneveld andvan der Vlerk 2006), they analyze structures and propose algorithms for numericallyhandling these instruments for risk aversion.Today, almost any decision to be taken with an aspiration for being “best” isexposed to uncertainty, and stochastic optimization is increasingly the method ofchoice for practical planning under uncertainty. This is reflected by the variety ofpractical applicationsMaarten van der Vlerk worked on. To list a few, we start withtelecommunications, or more precisely, distributed processing in telecommunicationnetworks (Tomasgard et al. 1998), with Asgeir Tomasgard, Jan A. Audestad, ShaneDye, Leen Stougie, and Stein W. Wallace. The list continues with generalized assign-ment problems (Albareda-Sambola et al. 2006), with Maria Albareda-Sambola and123The stochastic programming heritage of Maarten van der Vlerk 321Elena Fernández, day-ahead paratransit planning (Cremers et al. 2009), with MariaL.A.G. Cremers and Wim Klein Haneveld, asset and liability management (KleinHaneveld et al. 2011), with Wim Klein Haneveld and Matthijs H. Streutker, as well asa joint project with Arno Maatman, Caspar Schweigman, and Arjan Ruijs on “Model-ing farmers’ response to uncertain rainfall in Burkina Faso: a stochastic programmingapproach” in Maatman et al. (2002). The intent and contribution of this project isdescribed best by the following quotation from the paper, “This study draws impor-tant policy conclusions regarding reorientation of research programs and illustrateshow operations research techniques can be usefully applied to study grass root prob-lems in developing countries.”In addition to his research contributions, Maarten continually provided leadership,developing the Stochastic Programming Bibliography early in his career, serving asSecretary and then Chair of theCommittee on Stochastic Programming, and serving asProgramDirector for the Bachelor of Science in Econometrics &Operations Researchand for the Master of Science in Econometrics, Operations Research & ActuarialStudies at the University of Groningen. Maarten was an excellent teacher, winningthe Lecturer of the Year Award in 2014 from the University of Groningen’s Faculty ofEconomics and Business. Maarten cared deeply about those in his communities, andhad a strong sense of integrity and social justice, advocating for the homeless whenhe was still in high school and holding to important principles without fail. Apartfrom beautiful mathematics Maarten enjoyed many other good things in life, which hehappily shared with his friends and colleagues: music, in particular Bach, and tennis,which he made a fixed point of the social program of any stochastic programmingmeeting.Wewill missMaarten’s boisterous laugh, his warm smile, his sense of humor,and his ability to make everyone around him a better person.1 This special issueMaarten’s articles have inspired, and will continue to inspire, contemporary researchin stochastic (mixed-integer) optimization. This is illustrated by the research laid out inthe present special issue. The lead article is a paper that Maarten van der Vlerk workedonwith Niels van der Laan andWard Romeijnders, improvingMaarten’s original errorbounds on convex approximations for simple integer recourse models. The remainingarticles are grouped according to Maarten’s research interests: exploring structures,designing algorithms, and solving practical applications.1.1 StructuresWeijun Xie and Shabbir Ahmed study a distributionally robust version of the simpleinteger recoursemodel introduced byMaarten, and they derive a closed form analyticalexpression for the objective and represent it by amixed-integer second-order cone pro-gram. Lars Hellemo, Paul Barton, andAsgeir Tomasgard propose several formulationsof stochastic programs with decision-dependent uncertainties and present a taxonomyof such problems. Vit Prochazka and Stein W.Wallace show how to save substantially123322 D. P. Morton et al.on the number of scenarios to be evaluated for exactly computing the function valuesin stochastic programs.Matthias Claus, Rüdiger Schultz, andKai Spürkel identify ver-ifiable sufficient conditions for strong convexity of objective functions in risk-aversetwo-stage stochastic programs.1.2 AlgorithmsAndy Philpott, Vitor L. de Matos, and Lea Kapelevich develop a new variant of thestochastic dual dynamic programming algorithm to handle a data-driven distribution-ally robust formulation, with application to hydroelectric scheduling. Eli Towle andJames Luedtke propose two new methods to solve the maximum-reliability stochasticnetwork interdiction problem (SNIP) based on a compact deterministic equivalent for-mulation and a path-based formulation of SNIP, respectively. For dynamic mixed 0-1optimization problems with strategic and operational stochastic parameters LaureanoF. Escudero and Juan F. Monge present a modeling framework for risk neutral andrisk averse approaches and outline a solution procedure based on nested stochasticdecomposition. Semih Atakan and Suvrajeet Sen present a new framework allowingfor effective utilization of Progressive Hedging guaranteeing convergence to globallyoptimal solutions of mixed-integer stochastic convex programs.1.3 ApplicationsJing Voon Chen, Julia L. Higle, and Michael Hintlian formulate a problem for modelcalibration of a Markov chain governing disease progression via a constrained opti-mizationmodel,where the constraints capturemodel validity, probabilistic knowledge,clinical judgement, and more. Bismark Singh, David P. Morton, and Surya Santosomodel integration of a conventional electric power generator and a wind farm, whoseoutput is uncertain, in the context of a day-ahead electricity market. Miguel A. Leje-une and Janne Kettunen propose a new fractional stochastic integer programmingmodel for forestry revenue management that includes a joint chance constraint withmultirow random technology matrix and a joint integrated chance constraint. Gior-gio Consigli, Vittorio Moriggia, Sebastiano Vitali, and Lorenzo Mercuri model anasset-liability management problem of a property and casualty (PC) company usinga dynamic stochastic program and they analyze the relationship between risk capitalconstraints and risk-adjusted performance of a PC portfolio.ReferencesAlbareda-Sambola M, van der Vlerk MH, Fernández E (2006) Exact solutions to a class of stochasticgeneralized assignment problems. Eur J Oper Res 173:465–487Cremers MLAG, Klein Haneveld WK, van der Vlerk MH (2009) A two-stage model for a day-aheadparatransit planning problem. Math Methods Oper Res 69:323–341Klein Haneveld WK, van der Vlerk MH (2006) Integrated chance constraints: reduced forms and an algo-rithm. Comput Manag Sci 3:245–269Klein HaneveldWK, Stougie L, van der VlerkMH (1995) On the convex hull of the simple integer recourseobjective function. Ann Oper Res 56:209–224123The stochastic programming heritage of Maarten van der Vlerk 323Klein Haneveld WK, Stougie L, van der Vlerk MH (1996) An algorithm for the construction of the convexhull of the simple integer recourse objective function. Ann Oper Res 64:67–81Klein Haneveld WK, Stougie L, van der Vlerk MH (2006) Simple integer recourse models: convexity andconvex approximations. Math Program 108:435–473Klein Haneveld WK, Streutker MH, van der Vlerk MH (2011) Collective adjustment of pension rights inALM models. Comput Manag Sci 8:137–156Louveaux F, van der VlerkMH (1993) Stochastic programmingwith simple integer recourse.Math Program61:301–325Maatman A, Schweigman C, Ruijs A, van der Vlerk MH (2002) Modeling farmers’ response to uncertainrainfall in Burkina Faso: a stochastic programming approach. Oper Res 50:399–414Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2015) Convex approximations for totally uni-modular integer recourse models: a uniform error bound. SIAM J Optim 25:130–158Romeijnders W, Schultz R, van der Vlerk MH, Klein Haneveld WK (2016a) A convex approximation fortwo-stage mixed-integer recourse models with a uniform error bound. SIAM J Optim 26:426–447Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2016b) Total variation bounds on the expectationof periodic functions with applications to recourse approximations. Math Program 157:3–46Romeijnders W, Morton DP, van der Vlerk MH (2017) Assessing the quality of convex approximations fortwo-stage totally unimodular integer recourse models. INFORMS J Comput 29:211–231Schultz R, Stougie L, van der Vlerk MH (1998) Solving stochastic integer programs by enumeration: aframework using Gröbner basis reductions. Math Program 83:229–252Tomasgard A, Audestad JA, Dye S, Stougie L, van der Vlerk MH, Wallace SW (1998) Modelling aspectsof distributed processing in telecommunication networks. Ann Oper Res 82:161–184van der Vlerk MH (1995) Stochastic programming with integer recourse, Thesis Rijksuniversiteit Gronin-gen, Theses on Systems, Organisations, and Management. Labyrint Publications, Capelle a/d Ijsselvan der Vlerk MH (2004) Convex approximations for complete integer recourse models. Math Program99:297–310van der Vlerk MH (2010) Convex approximations for a class of mixed-integer recourse models. Ann OperRes 177:139–150AffiliationsDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6David P. Mortondavid.morton@northwestern.eduWard Romeijndersw.romeijnders@rug.nlLeen Stougiestougie@cwi.nl1 Department of Industrial Engineering and Management Sciences, Northwestern University, 2145Sheridan Road, Evanston, IL 60208-3119, USA2 Department of Operations, Faculty of Economics and Business, University of Groningen,Nettelbosje 2, 9747 AE Groningen, The Netherlands3 Faculty of Mathematics, University of Duisburg-Essen, Thea-Leymann-Str. 9, 45127 Essen,Germany4 CWI, Science Park 123, 1098 XG Amsterdam, The Netherlands5 INRIA-Erable, Amsterdam, The Netherlands6 Vrije Universiteit, Amsterdam, The Netherlands123

   University of GroningenThe stochastic programming heritage of Maarten van der VlerkMorton, D.P.; Romeijnders, Ward; Schultz, R.; Stougie, L.Published in:Computational Management ScienceDOI:10.1007/s10287-018-0335-8IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2018Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Morton, D. P., Romeijnders, W., Schultz, R., & Stougie, L. (2018). The stochastic programming heritage ofMaarten van der Vlerk. Computational Management Science, 15(3-4), 319-323.https://doi.org/10.1007/s10287-018-0335-8CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 28-04-2023Computational Management Science (2018) 15:319–323https://doi.org/10.1007/s10287-018-0335-8EDITORIALThe stochastic programming heritage of Maarten van derVlerkDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6On October 9, 2016, Professor Maarten H. van der Vlerk died tragically. This special issue is dedicated tohis memory.Published online: 6 September 2018© Springer-Verlag GmbH Germany, part of Springer Nature 2018Maarten van der Vlerk was born in the year 1961 in Assen, The Netherlands, wherehe spent his childhood and attended school up to the A-level, confirming the req-uisite maturity for university studies. At the University of Groningen he became astudent in the Department of Econometrics and commenced his doctoral studies thereunder the supervision of Wim Klein Haneveld and Leen Stougie in 1990. Invited andencouraged by his supervisors, Maarten placed a research focus on the integer pro-gramming side of stochastic optimization. Soon, he discovered his first original resultsculminating in a 1993 paper in Mathematical Programming with François Louveauxof the Facultés Universitaires Notre Dame de la Paix in Namur, on “Stochastic pro-gramming with simple integer recourse” (Louveaux and van der Vlerk 1993). Withhis thesis “Stochastic programming with integer recourse”, Maarten was awarded hisPh.D. degree in Economics from the University of Groningen in 1995 (van der Vlerk1995).After a year as postdoc at the Center for Operations Research and Economet-rics (CORE) at the Université Catholique de Louvain at Louvain-la-Neuve, Maartenreturned to Groningen. In 1999 he received a prestigious research fellowship fromthe Royal Netherlands Academy of Sciences. In 2008, Maarten became Professor ofStochastic Optimization at the University of Groningen, Faculty of Economics andBusiness, Department of Operations.From the mid 1990s on, stochastic integer programming—with its structures to bedetected, its algorithms to be constructed, and, last but not least, its real-life appli-cations to be developed—became the field where Maarten van der Vlerk’s researchearned highest recognition in the stochastic programming community and beyond. Apermanently recurring research target of Maarten, and his various coauthors, has beenconvex approximation of the notoriously non-convex, discontinuous, mixed-integerB Rüdiger Schultzruediger.schultz@uni-due.deExtended author information available on the last page of the article123320 D. P. Morton et al.expected recourse function, together with its algorithmic utilization. Papers devotedto these topics range over a time span of more than 20 years.Maarten’s publications on structures began with “On the convex hull of the sim-ple integer recourse objective function” jointly with Wim Klein Haneveld and LeenStougie in Annals of Operations Research in 1995 (Klein Haneveld et al. 1995).His work on structures continued with “Convex approximations for complete inte-ger recourse models”, Mathematical Programming (2004) (van der Vlerk 2004), and“Simple integer recourse models: convexity and convex approximations”,Mathemat-ical Programming (2006) (Klein Haneveld et al. 2006), the latter again jointly withWim Klein Haneveld and Leen Stougie.Our understanding of the structure of integer recourse problems received anotherboost with Maarten’s 2015 paper in SIAM Journal on Optimization, “Convex approx-imations for totally unimodular integer recourse models: a uniform error bound”(Romeijnders et al. 2015), written together with Wim Klein Haneveld and their co-advised doctoral student, Ward Romeijnders. Departure points for these investigationsinvolve periodicity properties ofmixed-integer value functions and their interplaywiththe total variation of density functions. This line of research—extended with journalpublications (Romeijnders et al. 2016a, b)—continues ever since and, in fact, leadsinto the present special issue, whose first article has been coauthored by Maarten vander Vlerk.Along with structural investigation, Maarten always had an eye on algorithmsand computations. Already in a 1996 paper, “An algorithm for the construction ofthe convex hull of the simple integer recourse objective function” (Klein Haneveldet al. 1996) in Annals of Operations Research, jointly with Wim Klein Haneveld andLeen Stougie, an algorithm is proposed for finding the convex hull, i.e., tight lowerbounds, for the objective function. Detection of convex hulls for recourse functionalshas always been accompanied by numerical testing. A 2017 paper jointly with DavidMorton and Ward Romeijnders in INFORMS Journal on Computing is an example ofthe latter.In 1998, Maarten, jointly with Rüdiger Schultz and Leen Stougie, published inMathematical Programing an implicit-enumeration algorithm for two-stage stochasticinteger programs, where function value calculation is enhanced by techniques fromGröbner bases in computational algebra (Schultz et al. 1998). This work initiatedconsiderable subsequent research on primal augmentation in stochastic programming.Another topicMaarten dealt with, often in collaboration withWimKlein Haneveld,concerns integrated chance constraints. In a 2006 joint paper (Klein Haneveld andvan der Vlerk 2006), they analyze structures and propose algorithms for numericallyhandling these instruments for risk aversion.Today, almost any decision to be taken with an aspiration for being “best” isexposed to uncertainty, and stochastic optimization is increasingly the method ofchoice for practical planning under uncertainty. This is reflected by the variety ofpractical applicationsMaarten van der Vlerk worked on. To list a few, we start withtelecommunications, or more precisely, distributed processing in telecommunicationnetworks (Tomasgard et al. 1998), with Asgeir Tomasgard, Jan A. Audestad, ShaneDye, Leen Stougie, and Stein W. Wallace. The list continues with generalized assign-ment problems (Albareda-Sambola et al. 2006), with Maria Albareda-Sambola and123The stochastic programming heritage of Maarten van der Vlerk 321Elena Fernández, day-ahead paratransit planning (Cremers et al. 2009), with MariaL.A.G. Cremers and Wim Klein Haneveld, asset and liability management (KleinHaneveld et al. 2011), with Wim Klein Haneveld and Matthijs H. Streutker, as well asa joint project with Arno Maatman, Caspar Schweigman, and Arjan Ruijs on “Model-ing farmers’ response to uncertain rainfall in Burkina Faso: a stochastic programmingapproach” in Maatman et al. (2002). The intent and contribution of this project isdescribed best by the following quotation from the paper, “This study draws impor-tant policy conclusions regarding reorientation of research programs and illustrateshow operations research techniques can be usefully applied to study grass root prob-lems in developing countries.”In addition to his research contributions, Maarten continually provided leadership,developing the Stochastic Programming Bibliography early in his career, serving asSecretary and then Chair of theCommittee on Stochastic Programming, and serving asProgramDirector for the Bachelor of Science in Econometrics &Operations Researchand for the Master of Science in Econometrics, Operations Research & ActuarialStudies at the University of Groningen. Maarten was an excellent teacher, winningthe Lecturer of the Year Award in 2014 from the University of Groningen’s Faculty ofEconomics and Business. Maarten cared deeply about those in his communities, andhad a strong sense of integrity and social justice, advocating for the homeless whenhe was still in high school and holding to important principles without fail. Apartfrom beautiful mathematics Maarten enjoyed many other good things in life, which hehappily shared with his friends and colleagues: music, in particular Bach, and tennis,which he made a fixed point of the social program of any stochastic programmingmeeting.Wewill missMaarten’s boisterous laugh, his warm smile, his sense of humor,and his ability to make everyone around him a better person.1 This special issueMaarten’s articles have inspired, and will continue to inspire, contemporary researchin stochastic (mixed-integer) optimization. This is illustrated by the research laid out inthe present special issue. The lead article is a paper that Maarten van der Vlerk workedonwith Niels van der Laan andWard Romeijnders, improvingMaarten’s original errorbounds on convex approximations for simple integer recourse models. The remainingarticles are grouped according to Maarten’s research interests: exploring structures,designing algorithms, and solving practical applications.1.1 StructuresWeijun Xie and Shabbir Ahmed study a distributionally robust version of the simpleinteger recoursemodel introduced byMaarten, and they derive a closed form analyticalexpression for the objective and represent it by amixed-integer second-order cone pro-gram. Lars Hellemo, Paul Barton, andAsgeir Tomasgard propose several formulationsof stochastic programs with decision-dependent uncertainties and present a taxonomyof such problems. Vit Prochazka and Stein W.Wallace show how to save substantially123322 D. P. Morton et al.on the number of scenarios to be evaluated for exactly computing the function valuesin stochastic programs.Matthias Claus, Rüdiger Schultz, andKai Spürkel identify ver-ifiable sufficient conditions for strong convexity of objective functions in risk-aversetwo-stage stochastic programs.1.2 AlgorithmsAndy Philpott, Vitor L. de Matos, and Lea Kapelevich develop a new variant of thestochastic dual dynamic programming algorithm to handle a data-driven distribution-ally robust formulation, with application to hydroelectric scheduling. Eli Towle andJames Luedtke propose two new methods to solve the maximum-reliability stochasticnetwork interdiction problem (SNIP) based on a compact deterministic equivalent for-mulation and a path-based formulation of SNIP, respectively. For dynamic mixed 0-1optimization problems with strategic and operational stochastic parameters LaureanoF. Escudero and Juan F. Monge present a modeling framework for risk neutral andrisk averse approaches and outline a solution procedure based on nested stochasticdecomposition. Semih Atakan and Suvrajeet Sen present a new framework allowingfor effective utilization of Progressive Hedging guaranteeing convergence to globallyoptimal solutions of mixed-integer stochastic convex programs.1.3 ApplicationsJing Voon Chen, Julia L. Higle, and Michael Hintlian formulate a problem for modelcalibration of a Markov chain governing disease progression via a constrained opti-mizationmodel,where the constraints capturemodel validity, probabilistic knowledge,clinical judgement, and more. Bismark Singh, David P. Morton, and Surya Santosomodel integration of a conventional electric power generator and a wind farm, whoseoutput is uncertain, in the context of a day-ahead electricity market. Miguel A. Leje-une and Janne Kettunen propose a new fractional stochastic integer programmingmodel for forestry revenue management that includes a joint chance constraint withmultirow random technology matrix and a joint integrated chance constraint. Gior-gio Consigli, Vittorio Moriggia, Sebastiano Vitali, and Lorenzo Mercuri model anasset-liability management problem of a property and casualty (PC) company usinga dynamic stochastic program and they analyze the relationship between risk capitalconstraints and risk-adjusted performance of a PC portfolio.ReferencesAlbareda-Sambola M, van der Vlerk MH, Fernández E (2006) Exact solutions to a class of stochasticgeneralized assignment problems. Eur J Oper Res 173:465–487Cremers MLAG, Klein Haneveld WK, van der Vlerk MH (2009) A two-stage model for a day-aheadparatransit planning problem. Math Methods Oper Res 69:323–341Klein Haneveld WK, van der Vlerk MH (2006) Integrated chance constraints: reduced forms and an algo-rithm. Comput Manag Sci 3:245–269Klein HaneveldWK, Stougie L, van der VlerkMH (1995) On the convex hull of the simple integer recourseobjective function. Ann Oper Res 56:209–224123The stochastic programming heritage of Maarten van der Vlerk 323Klein Haneveld WK, Stougie L, van der Vlerk MH (1996) An algorithm for the construction of the convexhull of the simple integer recourse objective function. Ann Oper Res 64:67–81Klein Haneveld WK, Stougie L, van der Vlerk MH (2006) Simple integer recourse models: convexity andconvex approximations. Math Program 108:435–473Klein Haneveld WK, Streutker MH, van der Vlerk MH (2011) Collective adjustment of pension rights inALM models. Comput Manag Sci 8:137–156Louveaux F, van der VlerkMH (1993) Stochastic programmingwith simple integer recourse.Math Program61:301–325Maatman A, Schweigman C, Ruijs A, van der Vlerk MH (2002) Modeling farmers’ response to uncertainrainfall in Burkina Faso: a stochastic programming approach. Oper Res 50:399–414Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2015) Convex approximations for totally uni-modular integer recourse models: a uniform error bound. SIAM J Optim 25:130–158Romeijnders W, Schultz R, van der Vlerk MH, Klein Haneveld WK (2016a) A convex approximation fortwo-stage mixed-integer recourse models with a uniform error bound. SIAM J Optim 26:426–447Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2016b) Total variation bounds on the expectationof periodic functions with applications to recourse approximations. Math Program 157:3–46Romeijnders W, Morton DP, van der Vlerk MH (2017) Assessing the quality of convex approximations fortwo-stage totally unimodular integer recourse models. INFORMS J Comput 29:211–231Schultz R, Stougie L, van der Vlerk MH (1998) Solving stochastic integer programs by enumeration: aframework using Gröbner basis reductions. Math Program 83:229–252Tomasgard A, Audestad JA, Dye S, Stougie L, van der Vlerk MH, Wallace SW (1998) Modelling aspectsof distributed processing in telecommunication networks. Ann Oper Res 82:161–184van der Vlerk MH (1995) Stochastic programming with integer recourse, Thesis Rijksuniversiteit Gronin-gen, Theses on Systems, Organisations, and Management. Labyrint Publications, Capelle a/d Ijsselvan der Vlerk MH (2004) Convex approximations for complete integer recourse models. Math Program99:297–310van der Vlerk MH (2010) Convex approximations for a class of mixed-integer recourse models. Ann OperRes 177:139–150AffiliationsDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6David P. Mortondavid.morton@northwestern.eduWard Romeijndersw.romeijnders@rug.nlLeen Stougiestougie@cwi.nl1 Department of Industrial Engineering and Management Sciences, Northwestern University, 2145Sheridan Road, Evanston, IL 60208-3119, USA2 Department of Operations, Faculty of Economics and Business, University of Groningen,Nettelbosje 2, 9747 AE Groningen, The Netherlands3 Faculty of Mathematics, University of Duisburg-Essen, Thea-Leymann-Str. 9, 45127 Essen,Germany4 CWI, Science Park 123, 1098 XG Amsterdam, The Netherlands5 INRIA-Erable, Amsterdam, The Netherlands6 Vrije Universiteit, Amsterdam, The Netherlands123

   University of GroningenThe stochastic programming heritage of Maarten van der VlerkMorton, D.P.; Romeijnders, Ward; Schultz, R.; Stougie, L.Published in:Computational Management ScienceDOI:10.1007/s10287-018-0335-8IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2018Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Morton, D. P., Romeijnders, W., Schultz, R., & Stougie, L. (2018). The stochastic programming heritage ofMaarten van der Vlerk. Computational Management Science, 15(3-4), 319-323. Advance online publication.https://doi.org/10.1007/s10287-018-0335-8CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 31-10-2023Computational Management Science (2018) 15:319–323https://doi.org/10.1007/s10287-018-0335-8EDITORIALThe stochastic programming heritage of Maarten van derVlerkDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6On October 9, 2016, Professor Maarten H. van der Vlerk died tragically. This special issue is dedicated tohis memory.Published online: 6 September 2018© Springer-Verlag GmbH Germany, part of Springer Nature 2018Maarten van der Vlerk was born in the year 1961 in Assen, The Netherlands, wherehe spent his childhood and attended school up to the A-level, confirming the req-uisite maturity for university studies. At the University of Groningen he became astudent in the Department of Econometrics and commenced his doctoral studies thereunder the supervision of Wim Klein Haneveld and Leen Stougie in 1990. Invited andencouraged by his supervisors, Maarten placed a research focus on the integer pro-gramming side of stochastic optimization. Soon, he discovered his first original resultsculminating in a 1993 paper in Mathematical Programming with François Louveauxof the Facultés Universitaires Notre Dame de la Paix in Namur, on “Stochastic pro-gramming with simple integer recourse” (Louveaux and van der Vlerk 1993). Withhis thesis “Stochastic programming with integer recourse”, Maarten was awarded hisPh.D. degree in Economics from the University of Groningen in 1995 (van der Vlerk1995).After a year as postdoc at the Center for Operations Research and Economet-rics (CORE) at the Université Catholique de Louvain at Louvain-la-Neuve, Maartenreturned to Groningen. In 1999 he received a prestigious research fellowship fromthe Royal Netherlands Academy of Sciences. In 2008, Maarten became Professor ofStochastic Optimization at the University of Groningen, Faculty of Economics andBusiness, Department of Operations.From the mid 1990s on, stochastic integer programming—with its structures to bedetected, its algorithms to be constructed, and, last but not least, its real-life appli-cations to be developed—became the field where Maarten van der Vlerk’s researchearned highest recognition in the stochastic programming community and beyond. Apermanently recurring research target of Maarten, and his various coauthors, has beenconvex approximation of the notoriously non-convex, discontinuous, mixed-integerB Rüdiger Schultzruediger.schultz@uni-due.deExtended author information available on the last page of the article123320 D. P. Morton et al.expected recourse function, together with its algorithmic utilization. Papers devotedto these topics range over a time span of more than 20 years.Maarten’s publications on structures began with “On the convex hull of the sim-ple integer recourse objective function” jointly with Wim Klein Haneveld and LeenStougie in Annals of Operations Research in 1995 (Klein Haneveld et al. 1995).His work on structures continued with “Convex approximations for complete inte-ger recourse models”, Mathematical Programming (2004) (van der Vlerk 2004), and“Simple integer recourse models: convexity and convex approximations”,Mathemat-ical Programming (2006) (Klein Haneveld et al. 2006), the latter again jointly withWim Klein Haneveld and Leen Stougie.Our understanding of the structure of integer recourse problems received anotherboost with Maarten’s 2015 paper in SIAM Journal on Optimization, “Convex approx-imations for totally unimodular integer recourse models: a uniform error bound”(Romeijnders et al. 2015), written together with Wim Klein Haneveld and their co-advised doctoral student, Ward Romeijnders. Departure points for these investigationsinvolve periodicity properties ofmixed-integer value functions and their interplaywiththe total variation of density functions. This line of research—extended with journalpublications (Romeijnders et al. 2016a, b)—continues ever since and, in fact, leadsinto the present special issue, whose first article has been coauthored by Maarten vander Vlerk.Along with structural investigation, Maarten always had an eye on algorithmsand computations. Already in a 1996 paper, “An algorithm for the construction ofthe convex hull of the simple integer recourse objective function” (Klein Haneveldet al. 1996) in Annals of Operations Research, jointly with Wim Klein Haneveld andLeen Stougie, an algorithm is proposed for finding the convex hull, i.e., tight lowerbounds, for the objective function. Detection of convex hulls for recourse functionalshas always been accompanied by numerical testing. A 2017 paper jointly with DavidMorton and Ward Romeijnders in INFORMS Journal on Computing is an example ofthe latter.In 1998, Maarten, jointly with Rüdiger Schultz and Leen Stougie, published inMathematical Programing an implicit-enumeration algorithm for two-stage stochasticinteger programs, where function value calculation is enhanced by techniques fromGröbner bases in computational algebra (Schultz et al. 1998). This work initiatedconsiderable subsequent research on primal augmentation in stochastic programming.Another topicMaarten dealt with, often in collaboration withWimKlein Haneveld,concerns integrated chance constraints. In a 2006 joint paper (Klein Haneveld andvan der Vlerk 2006), they analyze structures and propose algorithms for numericallyhandling these instruments for risk aversion.Today, almost any decision to be taken with an aspiration for being “best” isexposed to uncertainty, and stochastic optimization is increasingly the method ofchoice for practical planning under uncertainty. This is reflected by the variety ofpractical applicationsMaarten van der Vlerk worked on. To list a few, we start withtelecommunications, or more precisely, distributed processing in telecommunicationnetworks (Tomasgard et al. 1998), with Asgeir Tomasgard, Jan A. Audestad, ShaneDye, Leen Stougie, and Stein W. Wallace. The list continues with generalized assign-ment problems (Albareda-Sambola et al. 2006), with Maria Albareda-Sambola and123The stochastic programming heritage of Maarten van der Vlerk 321Elena Fernández, day-ahead paratransit planning (Cremers et al. 2009), with MariaL.A.G. Cremers and Wim Klein Haneveld, asset and liability management (KleinHaneveld et al. 2011), with Wim Klein Haneveld and Matthijs H. Streutker, as well asa joint project with Arno Maatman, Caspar Schweigman, and Arjan Ruijs on “Model-ing farmers’ response to uncertain rainfall in Burkina Faso: a stochastic programmingapproach” in Maatman et al. (2002). The intent and contribution of this project isdescribed best by the following quotation from the paper, “This study draws impor-tant policy conclusions regarding reorientation of research programs and illustrateshow operations research techniques can be usefully applied to study grass root prob-lems in developing countries.”In addition to his research contributions, Maarten continually provided leadership,developing the Stochastic Programming Bibliography early in his career, serving asSecretary and then Chair of theCommittee on Stochastic Programming, and serving asProgramDirector for the Bachelor of Science in Econometrics &Operations Researchand for the Master of Science in Econometrics, Operations Research & ActuarialStudies at the University of Groningen. Maarten was an excellent teacher, winningthe Lecturer of the Year Award in 2014 from the University of Groningen’s Faculty ofEconomics and Business. Maarten cared deeply about those in his communities, andhad a strong sense of integrity and social justice, advocating for the homeless whenhe was still in high school and holding to important principles without fail. Apartfrom beautiful mathematics Maarten enjoyed many other good things in life, which hehappily shared with his friends and colleagues: music, in particular Bach, and tennis,which he made a fixed point of the social program of any stochastic programmingmeeting.Wewill missMaarten’s boisterous laugh, his warm smile, his sense of humor,and his ability to make everyone around him a better person.1 This special issueMaarten’s articles have inspired, and will continue to inspire, contemporary researchin stochastic (mixed-integer) optimization. This is illustrated by the research laid out inthe present special issue. The lead article is a paper that Maarten van der Vlerk workedonwith Niels van der Laan andWard Romeijnders, improvingMaarten’s original errorbounds on convex approximations for simple integer recourse models. The remainingarticles are grouped according to Maarten’s research interests: exploring structures,designing algorithms, and solving practical applications.1.1 StructuresWeijun Xie and Shabbir Ahmed study a distributionally robust version of the simpleinteger recoursemodel introduced byMaarten, and they derive a closed form analyticalexpression for the objective and represent it by amixed-integer second-order cone pro-gram. Lars Hellemo, Paul Barton, andAsgeir Tomasgard propose several formulationsof stochastic programs with decision-dependent uncertainties and present a taxonomyof such problems. Vit Prochazka and Stein W.Wallace show how to save substantially123322 D. P. Morton et al.on the number of scenarios to be evaluated for exactly computing the function valuesin stochastic programs.Matthias Claus, Rüdiger Schultz, andKai Spürkel identify ver-ifiable sufficient conditions for strong convexity of objective functions in risk-aversetwo-stage stochastic programs.1.2 AlgorithmsAndy Philpott, Vitor L. de Matos, and Lea Kapelevich develop a new variant of thestochastic dual dynamic programming algorithm to handle a data-driven distribution-ally robust formulation, with application to hydroelectric scheduling. Eli Towle andJames Luedtke propose two new methods to solve the maximum-reliability stochasticnetwork interdiction problem (SNIP) based on a compact deterministic equivalent for-mulation and a path-based formulation of SNIP, respectively. For dynamic mixed 0-1optimization problems with strategic and operational stochastic parameters LaureanoF. Escudero and Juan F. Monge present a modeling framework for risk neutral andrisk averse approaches and outline a solution procedure based on nested stochasticdecomposition. Semih Atakan and Suvrajeet Sen present a new framework allowingfor effective utilization of Progressive Hedging guaranteeing convergence to globallyoptimal solutions of mixed-integer stochastic convex programs.1.3 ApplicationsJing Voon Chen, Julia L. Higle, and Michael Hintlian formulate a problem for modelcalibration of a Markov chain governing disease progression via a constrained opti-mizationmodel,where the constraints capturemodel validity, probabilistic knowledge,clinical judgement, and more. Bismark Singh, David P. Morton, and Surya Santosomodel integration of a conventional electric power generator and a wind farm, whoseoutput is uncertain, in the context of a day-ahead electricity market. Miguel A. Leje-une and Janne Kettunen propose a new fractional stochastic integer programmingmodel for forestry revenue management that includes a joint chance constraint withmultirow random technology matrix and a joint integrated chance constraint. Gior-gio Consigli, Vittorio Moriggia, Sebastiano Vitali, and Lorenzo Mercuri model anasset-liability management problem of a property and casualty (PC) company usinga dynamic stochastic program and they analyze the relationship between risk capitalconstraints and risk-adjusted performance of a PC portfolio.ReferencesAlbareda-Sambola M, van der Vlerk MH, Fernández E (2006) Exact solutions to a class of stochasticgeneralized assignment problems. Eur J Oper Res 173:465–487Cremers MLAG, Klein Haneveld WK, van der Vlerk MH (2009) A two-stage model for a day-aheadparatransit planning problem. Math Methods Oper Res 69:323–341Klein Haneveld WK, van der Vlerk MH (2006) Integrated chance constraints: reduced forms and an algo-rithm. Comput Manag Sci 3:245–269Klein HaneveldWK, Stougie L, van der VlerkMH (1995) On the convex hull of the simple integer recourseobjective function. Ann Oper Res 56:209–224123The stochastic programming heritage of Maarten van der Vlerk 323Klein Haneveld WK, Stougie L, van der Vlerk MH (1996) An algorithm for the construction of the convexhull of the simple integer recourse objective function. Ann Oper Res 64:67–81Klein Haneveld WK, Stougie L, van der Vlerk MH (2006) Simple integer recourse models: convexity andconvex approximations. Math Program 108:435–473Klein Haneveld WK, Streutker MH, van der Vlerk MH (2011) Collective adjustment of pension rights inALM models. Comput Manag Sci 8:137–156Louveaux F, van der VlerkMH (1993) Stochastic programmingwith simple integer recourse.Math Program61:301–325Maatman A, Schweigman C, Ruijs A, van der Vlerk MH (2002) Modeling farmers’ response to uncertainrainfall in Burkina Faso: a stochastic programming approach. Oper Res 50:399–414Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2015) Convex approximations for totally uni-modular integer recourse models: a uniform error bound. SIAM J Optim 25:130–158Romeijnders W, Schultz R, van der Vlerk MH, Klein Haneveld WK (2016a) A convex approximation fortwo-stage mixed-integer recourse models with a uniform error bound. SIAM J Optim 26:426–447Romeijnders W, van der Vlerk MH, Klein Haneveld WK (2016b) Total variation bounds on the expectationof periodic functions with applications to recourse approximations. Math Program 157:3–46Romeijnders W, Morton DP, van der Vlerk MH (2017) Assessing the quality of convex approximations fortwo-stage totally unimodular integer recourse models. INFORMS J Comput 29:211–231Schultz R, Stougie L, van der Vlerk MH (1998) Solving stochastic integer programs by enumeration: aframework using Gröbner basis reductions. Math Program 83:229–252Tomasgard A, Audestad JA, Dye S, Stougie L, van der Vlerk MH, Wallace SW (1998) Modelling aspectsof distributed processing in telecommunication networks. Ann Oper Res 82:161–184van der Vlerk MH (1995) Stochastic programming with integer recourse, Thesis Rijksuniversiteit Gronin-gen, Theses on Systems, Organisations, and Management. Labyrint Publications, Capelle a/d Ijsselvan der Vlerk MH (2004) Convex approximations for complete integer recourse models. Math Program99:297–310van der Vlerk MH (2010) Convex approximations for a class of mixed-integer recourse models. Ann OperRes 177:139–150AffiliationsDavid P. Morton1 ·Ward Romeijnders2 · Rüdiger Schultz3 · Leen Stougie4,5,6David P. Mortondavid.morton@northwestern.eduWard Romeijndersw.romeijnders@rug.nlLeen Stougiestougie@cwi.nl1 Department of Industrial Engineering and Management Sciences, Northwestern University, 2145Sheridan Road, Evanston, IL 60208-3119, USA2 Department of Operations, Faculty of Economics and Business, University of Groningen,Nettelbosje 2, 9747 AE Groningen, The Netherlands3 Faculty of Mathematics, University of Duisburg-Essen, Thea-Leymann-Str. 9, 45127 Essen,Germany4 CWI, Science Park 123, 1098 XG Amsterdam, The Netherlands5 INRIA-Erable, Amsterdam, The Netherlands6 Vrije Universiteit, Amsterdam, The Netherlands123

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