'Datafication': Making sense of (big) data in a complex world

This is a pre-print of an article published in European Journal of Information Systems. The definitive publisher-authenticated version is available at the link below. Copyright @ 2013 Operational Research Society Ltd.No abstract available (Editorial

On a retailer’s EOQ in a supply chain with two-level trade credit

Recently, Teng and Goyal [Journal of the Operational Research Society, Vol. 58, pp. 1252-1255, 2007.] extended and modified Huang’s model [Journal of the Operational Research Society, Vol. 54, pp. 1011-1015, 2003.] to develop their model and established the proper theoretical results to obtain the optimal solution. Their inventory model is correct and interesting. However, they give the optimal solutions showing that Theorems 1 and 2 in Teng and Goyal are not complete. The main purpose of this paper is to overcome Teng and Goyal’s shortcomings and to present complete proofs of their Theorems 1 and 2

Reflections on queue modelling from the last 50 years

Queueing theory continues to be one of the most researched areas of operational research, and has generated numerous review papers over the years. The phrase 'queue modelling' is used in the title to indicate a more practical emphasis. This paper uses work taken predominantly from the last 50 years of pages of the Operational Research Quarterly and the Journal of the Operational Research Society to offer a commentary on attempts of operational researchers to tackle real queueing problems, and on research foci past and future. A new discipline of 'queue modelling' is proposed, drawing upon the combined strengths of analytic and simulation approaches with the responsibility to derive meaningful insights for managers

Super-efficiency and stability intervals in additive DEA

This is a PDF file of an unedited manuscript that has been accepted for publication in Journal of the Operational Research Society. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. The final version will be available at: http://dx.doi.org/10.1057/jors.2012.1

Scientific exploration in the era of ocean observatories

Journal ArticleSociety's critical and urgent need to better understand the world's oceans is amply documented and has led to a unique convergence of operational and scientific interests in the US, organized around the concept of ocean observatories: cyber-­facilitated integrations of observations, simulations, and stakeholders. In particular, programs are emerging aimed at creating an operational Integrated Ocean Observing System (IOOS)1 to address broad society needs and an open, ocean-observing research infrastruc­ture (the Ocean Observatories Initia­tive [OOI]).

Assessing the Efficiency of Public Universities through DEA. A Case Study

[EN] This paper presents the results of an efficiency study of Colombian public universities in 2012, conducted using the methodology of Data Envelopment Analysis (DEA) and the models CCR, BCC and SBM under output orientation. The main objective is to determine technical, pure technical, scale and mix efficiencies using data acquired from the Ministry of National Education. An analysis of the results shows the extent to which outputs of inefficient Higher Education Institutions (HEIs) could be improved and the possible cause of this inefficiency. The universities were also ranked using a Pareto efficient cross-efficiency model and a study was made of changes to overall productivity between 2011 and 2012. The results showed Tolima, Caldas and UNAD to be the best-performing universities, with Universidad del Pacífico as the worst performer. Malmquist index was applied to analyze the change in productivity from 2011 to 2012. The Universidad de La Guajira showed great improvement in technical efficiency between 2011 and 2012.Monica Martinez-Gomez has been funded by the research project GVA/20161004: Project of Conselleria d'Educacio, Investigacio, Cultura i Esport de la Generalitat Valenciana, through the project "Validacion de la competencia transversal de innovacion mediante un modelo de Medida formativo"Visbal-Cadavid, D.; Martínez-Gómez, M.; Guijarro, F. (2017). Assessing the Efficiency of Public Universities through DEA. A Case Study. Sustainability. 9(8):1-19. https://doi.org/10.3390/su9081416S11998Bayraktar, E., Tatoglu, E., & Zaim, S. (2013). Measuring the relative efficiency of quality management practices in Turkish public and private universities. Journal of the Operational Research Society, 64(12), 1810-1830. doi:10.1057/jors.2013.2Mayston, D. J. (2017). Convexity, quality and efficiency in education. Journal of the Operational Research Society, 68(4), 446-455. doi:10.1057/jors.2015.91Palomares-Montero, D., García-Aracil, A., & Castro-Martínez, E. (2008). Assessment of Higher Education Institutions: A Bibliographic Review of Indicatorsâ Systems. Revista española de Documentación Científica, 31(2). doi:10.3989/redc.2008.v31.i2.425Witte, K. D., & López-Torres, L. (2017). Efficiency in education: a review of literature and a way forward. Journal of the Operational Research Society, 68(4), 339-363. doi:10.1057/jors.2015.92Barra, C., & Zotti, R. (2016). Measuring Efficiency in Higher Education: An Empirical Study Using a Bootstrapped Data Envelopment Analysis. International Advances in Economic Research, 22(1), 11-33. doi:10.1007/s11294-015-9558-4Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi:10.1016/0377-2217(78)90138-8Agasisti, T., & Bianco, A. D. (2009). Measuring efficiency of Higher Education institutions. International Journal of Management and Decision Making, 10(5/6), 443. doi:10.1504/ijmdm.2009.026687Agasisti, T., Barra, C., & Zotti, R. (2016). Evaluating the efficiency of Italian public universities (2008–2011) in presence of (unobserved) heterogeneity. Socio-Economic Planning Sciences, 55, 47-58. doi:10.1016/j.seps.2016.06.002Da Silva e Souza, G., & Gomes, E. G. (2015). Management of agricultural research centers in Brazil: A DEA application using a dynamic GMM approach. European Journal of Operational Research, 240(3), 819-824. doi:10.1016/j.ejor.2014.07.027Gökşen, Y., Doğan, O., & Özkarabacak, B. (2015). A Data Envelopment Analysis Application for Measuring Efficiency of University Departments. Procedia Economics and Finance, 19, 226-237. doi:10.1016/s2212-5671(15)00024-6Katharaki, M., & Katharakis, G. (2010). A comparative assessment of Greek universities’ efficiency using quantitative analysis. International Journal of Educational Research, 49(4-5), 115-128. doi:10.1016/j.ijer.2010.11.001Podinovski, V. V., & Wan Husain, W. R. (2015). The hybrid returns-to-scale model and its extension by production trade-offs: an application to the efficiency assessment of public universities in Malaysia. Annals of Operations Research, 250(1), 65-84. doi:10.1007/s10479-015-1854-0Thanassoulis, E., Kortelainen, M., Johnes, G., & Johnes, J. (2011). Costs and efficiency of higher education institutions in England: a DEA analysis. Journal of the Operational Research Society, 62(7), 1282-1297. doi:10.1057/jors.2010.68Wu, J., Chu, J., Sun, J., & Zhu, Q. (2016). DEA cross-efficiency evaluation based on Pareto improvement. European Journal of Operational Research, 248(2), 571-579. doi:10.1016/j.ejor.2015.07.042Kwon, H.-B., & Lee, J. (2015). Two-stage production modeling of large U.S. banks: A DEA-neural network approach. Expert Systems with Applications, 42(19), 6758-6766. doi:10.1016/j.eswa.2015.04.062Tao, L., Liu, X., & Chen, Y. (2012). Online banking performance evaluation using data envelopment analysis and axiomatic fuzzy set clustering. Quality & Quantity, 47(2), 1259-1273. doi:10.1007/s11135-012-9767-3Tsolas, I. E., & Charles, V. (2015). Incorporating risk into bank efficiency: A satisficing DEA approach to assess the Greek banking crisis. Expert Systems with Applications, 42(7), 3491-3500. doi:10.1016/j.eswa.2014.12.033Wanke, P., & Barros, C. (2014). Two-stage DEA: An application to major Brazilian banks. Expert Systems with Applications, 41(5), 2337-2344. doi:10.1016/j.eswa.2013.09.031Aristovnik, A., Seljak, J., & Mencinger, J. (2014). Performance measurement of police forces at the local level: A non-parametric mathematical programming approach. Expert Systems with Applications, 41(4), 1647-1653. doi:10.1016/j.eswa.2013.08.061Fang, L., & Li, H. (2015). Centralized resource allocation based on the cost–revenue analysis. Computers & Industrial Engineering, 85, 395-401. doi:10.1016/j.cie.2015.04.018Du, J., Cook, W. D., Liang, L., & Zhu, J. (2014). Fixed cost and resource allocation based on DEA cross-efficiency. European Journal of Operational Research, 235(1), 206-214. doi:10.1016/j.ejor.2013.10.002Lozano, S. (2015). A joint-inputs Network DEA approach to production and pollution-generating technologies. Expert Systems with Applications, 42(21), 7960-7968. doi:10.1016/j.eswa.2015.06.023Woo, C., Chung, Y., Chun, D., Seo, H., & Hong, S. (2015). The static and dynamic environmental efficiency of renewable energy: A Malmquist index analysis of OECD countries. Renewable and Sustainable Energy Reviews, 47, 367-376. doi:10.1016/j.rser.2015.03.070Azadeh, A., Motevali Haghighi, S., Zarrin, M., & Khaefi, S. (2015). Performance evaluation of Iranian electricity distribution units by using stochastic data envelopment analysis. International Journal of Electrical Power & Energy Systems, 73, 919-931. doi:10.1016/j.ijepes.2015.06.002Omrani, H., Gharizadeh Beiragh, R., & Shafiei Kaleibari, S. (2015). Performance assessment of Iranian electricity distribution companies by an integrated cooperative game data envelopment analysis principal component analysis approach. International Journal of Electrical Power & Energy Systems, 64, 617-625. doi:10.1016/j.ijepes.2014.07.045Escorcia Caballero, R., Visbal Cadavid, D., & Agudelo Toloza, J. M. (2015). Eficiencia en las instituciones educativas públicas de la ciudad de Santa Marta (Colombia) mediante "Análisis Envolvente de Datos. Ingeniare. Revista chilena de ingeniería, 23(4), 579-593. doi:10.4067/s0718-33052015000400009Grosskopf, S., Hayes, K., & Taylor, L. L. (2014). Applied efficiency analysis in education. Economics and Business Letters, 3(1), 19. doi:10.17811/ebl.3.1.2014.19-26Huguenin, J.-M. (2015). Determinants of school efficiency. International Journal of Educational Management, 29(5), 539-562. doi:10.1108/ijem-12-2013-0183Avilés Sacoto, S., Güemes Castorena, D., Cook, W. D., & Cantú Delgado, H. (2015). Time-staged outputs in DEA. Omega, 55, 1-9. doi:10.1016/j.omega.2015.01.019De Witte, K., & Rogge, N. (2011). Accounting for exogenous influences in performance evaluations of teachers. Economics of Education Review, 30(4), 641-653. doi:10.1016/j.econedurev.2011.02.002Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078-1092. doi:10.1287/mnsc.30.9.1078Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509. doi:10.1016/s0377-2217(99)00407-5Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253. doi:10.2307/2343100Scheel, H., & Scholtes, S. (2003). Continuity of DEA Efficiency Measures. Operations Research, 51(1), 149-159. doi:10.1287/opre.51.1.149.12803Andersen, P., & Petersen, N. C. (1993). A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Management Science, 39(10), 1261-1264. doi:10.1287/mnsc.39.10.1261Fang, H.-H., Lee, H.-S., Hwang, S.-N., & Chung, C.-C. (2013). A slacks-based measure of super-efficiency in data envelopment analysis: An alternative approach. Omega, 41(4), 731-734. doi:10.1016/j.omega.2012.10.004Doyle, J., & Green, R. (1994). Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses. Journal of the Operational Research Society, 45(5), 567-578. doi:10.1057/jors.1994.84Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation, 1986(32), 73-105. doi:10.1002/ev.1441Yang, G., Yang, J., Liu, W., & Li, X. (2013). Cross-efficiency aggregation in DEA models using the evidential-reasoning approach. European Journal of Operational Research, 231(2), 393-404. doi:10.1016/j.ejor.2013.05.017Zerafat Angiz, M., Mustafa, A., & Kamali, M. J. (2013). Cross-ranking of Decision Making Units in Data Envelopment Analysis. Applied Mathematical Modelling, 37(1-2), 398-405. doi:10.1016/j.apm.2012.02.038Banker, R. D., & Chang, H. (2006). The super-efficiency procedure for outlier identification, not for ranking efficient units. European Journal of Operational Research, 175(2), 1311-1320. doi:10.1016/j.ejor.2005.06.028Thanassoulis, E., Shiraz, R. K., & Maniadakis, N. (2015). A cost Malmquist productivity index capturing group performance. European Journal of Operational Research, 241(3), 796-805. doi:10.1016/j.ejor.2014.09.002Wijesiri, M., & Meoli, M. (2015). Productivity change of microfinance institutions in Kenya: A bootstrap Malmquist approach. Journal of Retailing and Consumer Services, 25, 115-121. doi:10.1016/j.jretconser.2015.04.004Eskelinen, J. (2017). Comparison of variable selection techniques for data envelopment analysis in a retail bank. European Journal of Operational Research, 259(2), 778-788. doi:10.1016/j.ejor.2016.11.009Jenkins, L., & Anderson, M. (2003). A multivariate statistical approach to reducing the number of variables in data envelopment analysis. European Journal of Operational Research, 147(1), 51-61. doi:10.1016/s0377-2217(02)00243-6Land, K. C., Knox Lovell, C. A., & Thore, S. (1994). Productive efficiency under capitalism and state socialism: Technological Forecasting and Social Change, 46(2), 139-152. doi:10.1016/0040-1625(94)90022-

Consolidation of Customer Orders Into Truckloads at a Large Manufacturer

Journal of the Operational Research Society, 48, pp. 779-785.We describe the development and operation of an interactive system based on a mathematical optimization model which is used by a major US manufacturer to consolidate customer orders into truckloads. Dozens of users employ the system daily for planning delivery of orders from manufacturing plants to customers by truckload carriers, saving numerous hours of the users' time and reducing transportation costs

Incorporating remote visits into an outpatient clinic

Copyright @ 2009 Operational Research Society Ltd. This is a post-peer-review, pre-copyedit version of an article published in Journal of Simulation. The definitive publisher-authenticated version Eatock and Eldabi (2009), "Incorporating remote visits into an outpatient clinic", Journal of Simulation, 3, 179–188 is available online at the link below.Most telemedicine studies are concerned with either the technological or diagnostic comparisons, rather than assessing the impact on clinic management. This has attributed to the retrospective nature of the studies, with lack of data being the main cause for not using simulation for prospective analysis. This article demonstrates the use of simulation to assess the impact of prospective systems by utilising data generated from clinical trials. The example used here is the introduction of remote consultations into an outpatient's clinic. The article addresses the issues of using secondary data, in terms of the differences between the trial, the model and future reality. The result of running the simulation model show that exchanging the mode of service delivery does not improve patient wait times as expected, and that a protocol change in association with the introduction of remote visits is necessary to provide a substantial reduction in patient wait times

Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem

[EN] This paper addresses an energy-based extension of the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP) called MRCPSP-ENERGY. This extension considers the energy consumption as an additional resource that leads to different execution modes (and durations) of the activities. Consequently, different schedules can be obtained. The objective is to maximize the efficiency of the project, which takes into account the minimization of both makespan and energy consumption. This is a well-known NP-hard problem, such that the application of metaheuristic techniques is necessary to address real-size problems in a reasonable time. This paper shows that the Activity List representation, commonly used in metaheuristics, can lead to obtaining many redundant solutions, that is, solutions that have different representations but are in fact the same. This is a serious disadvantage for a search procedure. We propose a genetic algorithm(GA) for solving the MRCPSP-ENERGY, trying to avoid redundant solutions by focusing the search on the execution modes, by using the Mode List representation. The proposed GA is evaluated on different instances of the PSPLIB-ENERGY library and compared to the results obtained by both exact methods and approximate methods reported in the literature. This library is an extension of the well-known PSPLIB library, which contains MRCPSP-ENERGY test cases.This paper has been partially supported by the Spanish Research Projects TIN2013-46511-C2-1-P and TIN2016-80856-R.Morillo-Torres, D.; Barber, F.; Salido, MA. (2017). Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem. Mathematical Problems in Engineering. 2017:1-15. https://doi.org/10.1155/2017/4627856S1152017Mouzon, G., Yildirim, M. B., & Twomey, J. (2007). Operational methods for minimization of energy consumption of manufacturing equipment. International Journal of Production Research, 45(18-19), 4247-4271. doi:10.1080/00207540701450013Hartmann, S., & Sprecher, A. (1996). A note on «hierarchical models for multi-project planning and scheduling». European Journal of Operational Research, 94(2), 377-383. doi:10.1016/0377-2217(95)00158-1Christofides, N., Alvarez-Valdes, R., & Tamarit, J. M. (1987). Project scheduling with resource constraints: A branch and bound approach. European Journal of Operational Research, 29(3), 262-273. doi:10.1016/0377-2217(87)90240-2Zhu, G., Bard, J. F., & Yu, G. (2006). A Branch-and-Cut Procedure for the Multimode Resource-Constrained Project-Scheduling Problem. INFORMS Journal on Computing, 18(3), 377-390. doi:10.1287/ijoc.1040.0121Kolisch, R., & Hartmann, S. (1999). Heuristic Algorithms for the Resource-Constrained Project Scheduling Problem: Classification and Computational Analysis. International Series in Operations Research & Management Science, 147-178. doi:10.1007/978-1-4615-5533-9_7Józefowska, J., Mika, M., Różycki, R., Waligóra, G., & Węglarz, J. (2001). Annals of Operations Research, 102(1/4), 137-155. doi:10.1023/a:1010954031930Bouleimen, K., & Lecocq, H. (2003). A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. European Journal of Operational Research, 149(2), 268-281. doi:10.1016/s0377-2217(02)00761-0Alcaraz, J., Maroto, C., & Ruiz, R. (2003). Solving the Multi-Mode Resource-Constrained Project Scheduling Problem with genetic algorithms. Journal of the Operational Research Society, 54(6), 614-626. doi:10.1057/palgrave.jors.2601563Zhang, H., Tam, C. M., & Li, H. (2006). Multimode Project Scheduling Based on Particle Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering, 21(2), 93-103. doi:10.1111/j.1467-8667.2005.00420.xJarboui, B., Damak, N., Siarry, P., & Rebai, A. (2008). A combinatorial particle swarm optimization for solving multi-mode resource-constrained project scheduling problems. Applied Mathematics and Computation, 195(1), 299-308. doi:10.1016/j.amc.2007.04.096Li, H., & Zhang, H. (2013). Ant colony optimization-based multi-mode scheduling under renewable and nonrenewable resource constraints. Automation in Construction, 35, 431-438. doi:10.1016/j.autcon.2013.05.030Lova, A., Tormos, P., Cervantes, M., & Barber, F. (2009). An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes. International Journal of Production Economics, 117(2), 302-316. doi:10.1016/j.ijpe.2008.11.002Peteghem, V. V., & Vanhoucke, M. (2010). A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. European Journal of Operational Research, 201(2), 409-418. doi:10.1016/j.ejor.2009.03.034Węglarz, J., Józefowska, J., Mika, M., & Waligóra, G. (2011). Project scheduling with finite or infinite number of activity processing modes – A survey. European Journal of Operational Research, 208(3), 177-205. doi:10.1016/j.ejor.2010.03.037Kolisch, R., & Hartmann, S. (2006). Experimental investigation of heuristics for resource-constrained project scheduling: An update. European Journal of Operational Research, 174(1), 23-37. doi:10.1016/j.ejor.2005.01.065Debels, D., De Reyck, B., Leus, R., & Vanhoucke, M. (2006). A hybrid scatter search/electromagnetism meta-heuristic for project scheduling. European Journal of Operational Research, 169(2), 638-653. doi:10.1016/j.ejor.2004.08.020Paraskevopoulos, D. C., Tarantilis, C. D., & Ioannou, G. (2012). Solving project scheduling problems with resource constraints via an event list-based evolutionary algorithm. Expert Systems with Applications, 39(4), 3983-3994. doi:10.1016/j.eswa.2011.09.062Drexl, A. (1991). Scheduling of Project Networks by Job Assignment. Management Science, 37(12), 1590-1602. doi:10.1287/mnsc.37.12.1590BOCTOR, F. F. (1996). Resource-constrained project scheduling by simulated annealing. International Journal of Production Research, 34(8), 2335-2351. doi:10.1080/0020754960890502