228 research outputs found

    Task Allocation Strategies in Multi-Robot Environment

    Get PDF
    Multirobot systems (MRS) hold the promise of improved performance and increased fault tolerance for large-scale problems. A robot team can accomplish a given task more quickly than a single agent by executing them concurrently. A team can also make effective use of specialists designed for a single purpose rather than requiring that a single robot be a generalist. Multirobot coordination, however, is a complex problem. An empirical study is described in the thesis that sought general guidelines for task allocation strategies. Different strategies are identified, and demonstrated in the multi-robot environment.Robot selection is one of the critical issues in the design of robotic workcells. Robot selection for an application is generally done based on experience, intuition and at most using the kinematic considerations like workspace, manipulability, etc. This problem has become more difficult in recent years due to increasing complexity, available features, and facilities offered by different robotic products. A systematic procedure is developed for selection of robot manipulators based on their different pertinent attributes. The robot selection procedure allows rapid convergence from a very large number of candidate robots to a manageable shortlist of potentially suitable robots. Subsequently, the selection procedure proceeds to rank the alternatives in the shortlist by employing different attributes based specification methods. This is an attempt to create exhaustive procedure by identifying maximum possible number of attributes for robot manipulators.Availability of large number of robot configurations has made the robot workcell designers think over the issue of selecting the most suitable one for a given set of operations. The process of selection of the appropriate kind of robot must consider the various attributes of the robot manipulator in conjunction with the requirement of the various operations for accomplishing the task. The present work is an attempt to develop a systematic procedure for selection of robot based on an integrated model encompassing the manipulator attributes and manipulator requirements

    A comprehensive survey on cultural algorithms

    Get PDF
    Peer reviewedPostprin

    Coordination mechanisms with mathematical programming models for decentralized decision-making, a literature review

    Full text link
    [EN] The increase in the complexity of supply chains requires greater efforts to align the activities of all its members in order to improve the creation of value of their products or services offered to customers. In general, the information is asymmetric; each member has its own objective and limitations that may be in conflict with other members. Operations managements face the challenge of coordinating activities in such a way that the supply chain as a whole remains competitive, while each member improves by cooperating. This document aims to offer a systematic review of the collaborative planning in the last decade on the mechanisms of coordination in mathematical programming models that allow us to position existing concepts and identify areas where more research is needed.Rius-Sorolla, G.; Maheut, J.; Estelles Miguel, S.; García Sabater, JP. (2020). Coordination mechanisms with mathematical programming models for decentralized decision-making, a literature review. Central European Journal of Operations Research. 28(1):61-104. https://doi.org/10.1007/s10100-018-0594-zS61104281Acar Y, Atadeniz SN (2015) Comparison of integrated and local planning approaches for the supply network of a globally-dispersed enterprise. Int J Prod Econ 167:204–219. https://doi.org/10.1016/j.ijpe.2015.05.028Agnetis A, Hall NG, Pacciarelli D (2006) Supply chain scheduling: sequence coordination. Discrete Appl Math 154(15):2044–2063. https://doi.org/10.1016/j.dam.2005.04.019Agnetis A, Aloulou MA, Fu LL (2016) Production and interplant batch delivery scheduling: Dominance and cooperation. Int J Prod Econ 182:38–49. https://doi.org/10.1016/j.ijpe.2016.08.007Albrecht M (2010) Supply chain coordination mechanisms Lecture notes in economics and mathematical systems, vol 628. Springer, Berlin. https://doi.org/10.1007/978-3-642-02833-5Albrecht M, Stadtler H (2015) Coordinating decentralized linear programs by exchange of primal information. Eur J Oper Res 247(3):788–796. https://doi.org/10.1016/j.ejor.2015.06.045Arkan A, Hejazi SR (2012) Coordinating orders in a two echelon supply chain with controllable lead time and ordering cost using the credit period. Comput Ind Eng 62(1):56–69. https://doi.org/10.1016/j.cie.2011.08.016Arshinder, Kanda A, Deshmukh SG (2008) Supply chain coordination: perspectives, empirical studies and research directions. Int J Prod Econ 115(2):316–335. https://doi.org/10.1016/j.ijpe.2008.05.011Attanasio A, Ghiani G, Grandinetti L, Guerriero F (2006) Auction algorithms for decentralized parallel machine scheduling. Parallel Comput 32(9):701–709. https://doi.org/10.1016/j.parco.2006.03.002Badole CM, Jain R, Rathore APS, Nepal B (2012) Research and opportunities in supply chain modeling: a review. Int J Supply Chain Manag 1(3):63–86Bajgiran OS, Zanjani MK, Nourelfath M (2016) The value of integrated tactical planning optimization in the lumber supply chain. Int J Prod Econ 171:22–33. https://doi.org/10.1016/j.ijpe.2015.10.021Behnamian J (2014) Multi-cut Benders decomposition approach to collaborative scheduling. Int J Comput Integr Manuf 28(11):1–11. https://doi.org/10.1080/0951192X.2014.961963Ben-Daya M, Darwish M, Ertogral K (2008) The joint economic lot sizing problem: review and extensions. Eur J Oper Res 185(2):726–742. https://doi.org/10.1016/j.ejor.2006.12.026Benders JF (1962) Partitioning procedures for solving mixed-variables programming problems. Numer Math 4(1):238–252. https://doi.org/10.1007/BF01386316Bhatnagar R, Chandra P, Goyal SK (1993) Models for multi-plant coordination. Eur J Oper Res 67(2):141–160. https://doi.org/10.1016/0377-2217(93)90058-UBuer T, Homberger JJ, Gehring H (2013) A collaborative ant colony metaheuristic for distributed multi-level uncapacitated lot-sizing. Int J Prod Res 51(17):5253–5270. https://doi.org/10.1080/00207543.2013.802822Buer T, Ziebuhr M, Kopfer H (2015) A coordination mechanism for a collaborative lot-sizing problem with rivaling agents. In: Mattfeld D, Spengler T, Brinkmann J, Grunewald M (eds) Logistics management. Springer, Cham. https://doi.org/10.1007/978-3-319-13177-1_26Buxmann P, Ahsen A Von, Díaz LM (2008) Economic evaluation of cooperation scenarios in supply chains. J Enterp Inf Manag 21(3):247–262. https://doi.org/10.1108/17410390810866628Chakraborty A, Chatterjee AK (2015) A surcharge pricing scheme for supply chain coordination under JIT environment. Eur J Oper Res 253(1):14–24. https://doi.org/10.1016/j.ejor.2016.02.001Chen IJ, Paulraj A, Lado AA (2004) Strategic purchasing, supply management, and firm performance. J Oper Manag 22(5):505–523. https://doi.org/10.1016/j.jom.2004.06.002Cheng JH (2011) Inter-organizational relationships and information sharing in supply chains. Int J Inf Manag 31(4):374–384. https://doi.org/10.1016/j.ijinfomgt.2010.09.004Cheng R, Forbes JF, San Yip W, Fraser Forbes J, Yip WS (2008) Dantzig–Wolfe decomposition and plant-wide MPC coordination. Comput Chem Eng 32(7):1507–1522. https://doi.org/10.1016/j.compchemeng.2007.07.003Cooper MC, Lambert DM, Pagh JD (1997) Supply chain management: more than a new name for logistics. Int J Logist Manag 8(1):1–14. https://doi.org/10.1108/09574099710805556Dantzig GB, Wolfe P (1960) Decomposition principle for linear programs. Oper Res 8(1):101–111. https://doi.org/10.1287/opre.8.1.101Dash RK, Vytelingum P, Rogers A, David E, Jennings NR (2007) Market-based task allocation mechanisms for limited-capacity suppliers. IEEE Trans Syst Man Cybern Part A Syst Hum 37(3):391–405. https://doi.org/10.1109/TSMCA.2007.893474Dudek G, Stadtler H (2005) Negotiation-based collaborative planning between supply chains partners. Eur J Operat Res 163(3):668–687. https://doi.org/10.1016/j.ejor.2004.01.014Dudek G, Stadtler H (2007) Negotiation-based collaborative planning in divergent two-tier supply chains. Int J Prod Res 45(2):465–484Ertogral K, David Wu S (2000) Auction-theoretic coordination of production planning in the supply chain. IIE Trans 32:931–940. https://doi.org/10.1080/07408170008967451Eslikizi S, Ziebuhr M, Kopfer H, Buer T (2015) Shapley-based side payments and simulated annealing for distributed lot-sizing. IFAC-PapersOnLine 48(3):1592–1597. https://doi.org/10.1016/j.ifacol.2015.06.313Fan M, Stallaert J, Whinston AB (2003) Decentralized mechanism design for supply chain organizations using an auction market. Inf Syst Res 14(1):1–22. https://doi.org/10.1287/isre.14.1.1.14763Feng Y, D’Amours S, Beauregard R (2008) The value of sales and operations planning in oriented strand board industry with make-to-order manufacturing system: cross functional integration under deterministic demand and spot market recourse. Int J Prod Econ 115(1):189–209. https://doi.org/10.1016/j.ijpe.2008.06.002Fisher ML (1985) An applications oriented guide to Lagrangian relaxation. Interfaces 15(2):10–21. https://doi.org/10.1287/inte.15.2.10Fisher ML (2004) The Lagrangian relaxation method for solving integer programming problems. Manag Sci 50(12 Supplement):1861–1871. https://doi.org/10.1287/mnsc.1040.0263Frazzon E, Makuschewits T, Scholz-Reiter B, Novaes AGN (2010) Assessing the integrated scheduling of manufacturing and transportation systems along global supply chains. In: World conference on transport research, LisbonGaudreault J, Forget P, Frayret JMJ, Rousseau A, Lemieux S, D’Amours S (2010) Distributed operations planning in the softwood lumber supply chain: models and coordination. Int J Ind Eng Theory Appl Pract 17(3):168–189Gunnerud V, Foss B (2010) Oil production optimization—a piecewise linear model, solved with two decomposition strategies. Comput Chem Eng 34(11):1803–1812. https://doi.org/10.1016/j.compchemeng.2009.10.019Harb H, Paprott JN, Matthes P, Schütz T, Streblow R, Mueller D (2015) Decentralized scheduling strategy of heating systems for balancing the residual load. Build Environ 86:132–140. https://doi.org/10.1016/j.buildenv.2014.12.015Held M, Karp RM (1970) The traveling-salesman problem and minimum spanning trees. Oper Res 18(6):1138–1162. https://doi.org/10.1287/opre.18.6.1138Held M, Karp RM (1971) The traveling-salesman problem and minimum spanning trees: part II. Math Program 1(1):6–25. https://doi.org/10.1007/BF01584070Homberger J (2010) Decentralized multi-level uncapacitated lot-sizing by automated negotiation. 4OR 8(2):155–180. https://doi.org/10.1007/s10288-009-0104-1Homberger J (2011) A generic coordination mechanism for lot-sizing in supply chains. Electron Commer Res 11(2):123–149. https://doi.org/10.1007/s10660-010-9053-1Homberger J, Gehring H (2010) A pheromone-based negotiation mechanism for lot-sizing in supply chains. In: 2010 43rd Hawaii international conference on system sciences. IEEE, pp 1–10. https://doi.org/10.1109/hicss.2010.26Homberger J, Gehring H (2011) An ant colony optimization-based negotiation approach for lot-sizing in supply chains. Int J Inf Process Manag 2(3):86–99. https://doi.org/10.4156/ijipm.vol2.issue3.10Homberger J, Gehring H, Buer T (2015) Integrating side payments into collaborative planning for the distributed multi-level unconstrained lot sizing problem. In: Bui TX, Sprague RH (eds) 2015 48th Hawaii international conference on system sciences, vol 2015. IEEE, pp 1068–1077. https://doi.org/10.1109/hicss.2015.131Huang GQ, Lau JSK, Mak KL (2003) The impacts of sharing production information on supply chain dynamics: a review of the literature. Int J Prod Res 41(7):1483–1517. https://doi.org/10.1080/0020754031000069625Jeong I-J (2012) A centralized/decentralized design of a full return contract for a risk-free manufacturer and a risk-neutral retailer under partial information sharing. Int J Prod Econ 136(1):110–115. https://doi.org/10.1016/j.ijpe.2011.09.019Jeong IJ, Leon VJ (2002) Decision-making and cooperative interaction via coupling agents in organizationally distributed systems. IIE Trans (Inst Ind Eng) 34(9):789–802. https://doi.org/10.1023/A:1015548705266Jeong IJ, Yim SB (2009) A job shop distributed scheduling based on Lagrangian relaxation to minimise total completion time. Int J Prod Res 47(24):6783–6805. https://doi.org/10.1080/00207540701824217Jia ZZ, Deschamps JC, Dupas R (2016) A negotiation protocol to improve planning coordination in transport-driven supply chains. J Manuf Syst 38:13–26. https://doi.org/10.1016/j.jmsy.2015.10.003Jung H, Chen FF, Jeong B (2008) Decentralized supply chain planning framework for third party logistics partnership. Comput Ind Eng 55(2):348–364. https://doi.org/10.1016/j.cie.2007.12.017Katok E, Pavlov V (2013) Fairness in supply chain contracts: a laboratory study. J Oper Manag 31(3):129–137. https://doi.org/10.1016/j.jom.2013.01.001Kelly JD, Zyngier D (2008) Hierarchical decomposition heuristic for scheduling: coordinated reasoning for decentralized and distributed decision-making problems. Comput Chem Eng 32(11):2684–2705. https://doi.org/10.1016/j.compchemeng.2007.08.007Kong J, Rönnqvist M (2014) Coordination between strategic forest management and tactical logistic and production planning in the forestry supply chain. Int Trans Oper Res 21(5):703–735. https://doi.org/10.1111/itor.12089Kovács A, Egri P, Kis T, Váncza J (2013) Inventory control in supply chains: alternative approaches to a two-stage lot-sizing problem. Int J Prod Econ 143(2):385–394. https://doi.org/10.1016/j.ijpe.2012.01.001Kumar BK, Nagaraju D, Narayanan S (2016) Supply chain coordination models: a literature review. Indian J Sci Technol. https://doi.org/10.17485/ijst/2016/v9i38/86938Kutanoglu E, David Wu S (1999) On combinatorial auction and Lagrangean relaxation for distributed resource scheduling. IIE Trans 31(9):813–826. https://doi.org/10.1080/07408179908969883Lau HC, Zhao ZJ, Ge SS, Lee TH (2011) Allocating resources in multiagent flowshops with adaptive auctions. IEEE Trans Autom Sci Eng 8(4):732–743. https://doi.org/10.1109/TASE.2011.2160536Lee DJ, Jeong IJ (2010) A distributed coordination for a single warehouse-multiple retailer problem under private information. Int J Prod Econ 125(1):190–199. https://doi.org/10.1016/j.ijpe.2010.02.001Lehoux N, D’Amours S, Frein Y, Langevin A, Penz B (2010a) Collaboration for a two-echelon supply chain in the pulp and paper industry: the use of incentives to increase profit. J Oper Res Soc 62(4):581–592. https://doi.org/10.1057/jors.2009.167Lehoux N, D’Amours S, Langevin A (2010b) A win–win collaboration approach for a two-echelon supply chain: a case study in the pulp and paper industry. Eur J Ind Eng 4(4):493. https://doi.org/10.1504/EJIE.2010.035656Lehoux N, D’Amours S, Langevin A (2014) Inter-firm collaborations and supply chain coordination: review of key elements and case study. Prod Plan Control 25(10):858–872. https://doi.org/10.1080/09537287.2013.771413Li X, Wang Q (2007) Coordination mechanisms of supply chain systems. Eur J Oper Res 179(1):1–16. https://doi.org/10.1016/j.ejor.2006.06.023Lu SYP, Lau HYK, Yiu CKF (2012) A hybrid solution to collaborative decision-making in a decentralized supply-chain. J Eng Technol Manag 29(1):95–111. https://doi.org/10.1016/j.jengtecman.2011.09.008Mahdiraji HA, Zavadskas EK, Hajiagha SHR (2015) Game theoretic approach for coordinating unlimited multi echelon supply chains. Transform Bus Econ 14(2):133–151Maheut J, Besga JM, Uribetxebarria J, Garcia-Sabater JP (2014a) A decision support system for modelling and implementing the supply network configuration and operations scheduling problem in the machine tool industry. Prod Plan Control 25(8):679–697. https://doi.org/10.1080/09537287.2013.798087Maheut J, Garcia-Sabater JP, Garcia-Sabater JJ, Marin-Garcia J (2014b) Coordination mechanism for MILP models to plan operations within an advanced planning and scheduling system in a motor company: a case study. In: Prado-Prado JC, García-Arca J (eds) Annals of industrial engineering 2012. Springer, London, pp 245–253. https://doi.org/10.1007/978-1-4471-5349-8_29Manrodt KB, Vitasek K (2004) Global process standardization: a case study. J Bus Logist 25(1):1–23. https://doi.org/10.1002/j.2158-1592.2004.tb00168.xMarin-Garcia JA, Ramirez Bayarri L, Atares Huerta L (2015) Protocol: comparing advantages and disadvantages of rating scales, behavior observation scales and paired comparison scales for behavior assessment of competencies in workers. A systematic literature review. Work Pap Oper Manag 6(2):49. https://doi.org/10.4995/wpom.v6i2.4032Mason AN, Villalobos JR (2015) Coordination of perishable crop production using auction mechanisms. Agric Syst 138:18–30. https://doi.org/10.1016/j.agsy.2015.04.008McAfee RP, McMillan J (1987) Auctions and bidding. J Econ Lit 25(2):699–738Medina-Lopez C, Marin-Garcia JA, Alfalla-Luque R (2010) Una propuesta metodológica para la realización de búsquedas sistemáticas de bibliografía (A methodological proposal for the systematic literature review). Work Pap Oper Manag. https://doi.org/10.4995/wpom.v1i2.786Mouret S, Grossmann IE, Pestiaux P (2011) A new Lagrangian decomposition approach applied to the integration of refinery planning and crude-oil scheduling. Comput Chem Eng 35(12):2750–2766. https://doi.org/10.1016/j.compchemeng.2011.03.026Mula J, Peidro D, Díaz-Madroñero M, Vicens E (2010) Mathematical programming models for supply chain production and transport planning. Eur J Oper Res 204(3):377–390. https://doi.org/10.1016/j.ejor.2009.09.008Nie L, Xu X, Zhan D (2008) Collaborative planning in supply chains by lagrangian relaxation and genetic algorithms. Int J Inf Technol Decis Mak 7(1):183–197. https://doi.org/10.1142/s0219622008002879Nishi T, Shinozaki R, Konishi M (2008) An augmented Lagrangian approach for distributed supply chain planning for multiple companies. IEEE Trans Autom Sci Eng 5(2):259–274. https://doi.org/10.1109/TASE.2007.894727Ouelhadj D, Petrovic S (2009) A survey of dynamic scheduling in manufacturing systems. J Sched 12(4):417–431. https://doi.org/10.1007/s10951-008-0090-8Pibernik R, Sucky E (2007) An approach to inter-domain master planning in supply chains. Int J Prod Econ 108(1–2):200–212. https://doi.org/10.1016/j.ijpe.2006.12.010Pittman SD, Bare BB, Briggs DG (2007) Hierarchical production planning in forestry using price-directed decomposition. Can J For 37(10):2010–2021. https://doi.org/10.1139/X07-026Polyak BT (1969) Minimization of unsmooth functionals. USSR Comput Math Math Phys 9(3):14–29. https://doi.org/10.1016/0041-5553(69)90061-5Pukkala T, Heinonen T, Kurttila M (2009) An application of a reduced cost approach to spatial forest planning. For Sci 55(1):13–22Qu T, Nie DX, Chen X, Chen XD, Dai QY, Huang GQ (2015) Optimal configuration of cluster supply chains with augmented Lagrange coordination. Comput Ind Eng 84(SI):43–55. https://doi.org/10.1016/j.cie.2014.12.026Reiss F, Buer T (2014) A coordination mechanism for capacitated lot-sizing in non-hierarchical n-tier supply chains. In: 2014 IEEE symposium on computational intelligence in production and logistics systems (Cipls), pp 9–15. https://doi.org/10.1109/cipls.2014.7007155Rius-Sorolla G, Maheut J, Estelles-Miguel S, Garcia-Sabater JP (2017) Protocol: systematic literature review on coordination mechanisms for the mathematical programming models in production planning with decentralized decision making. Work Pap Oper Manag 8(2):22. https://doi.org/10.4995/wpom.v8i2.7858Sahin F, Robinson EPP (2002) Flow coordination and information sharing in supply chains: review, implications, and directions for future research. Decis Sci 33(4):505–535. https://doi.org/10.1111/j.1540-5915.2002.tb01654.xSilva CA, Sousa JMC, Runkler TA, Sá da Costa J (2009) Distributed supply chain management using ant colony optimization. Eur J Oper Res 199(2):349–358. https://doi.org/10.1016/j.ejor.2008.11.021Simatupang T, Sridharan R (2006) The collaboration index: a measure for supply chain collaboration. Int J Phys Distrib Logist Manag 35:44–62. https://doi.org/10.1108/09600030510577421Singh G, Ernst A (2011) Resource constraint scheduling with a fractional shared resource. Oper Res Lett 39(5):363–368. https://doi.org/10.1016/j.orl.2011.06.003Singh G, O’Keefe CM (2016) Decentralised scheduling with confidentiality protection. Oper Res Lett 44(4):514–519. https://doi.org/10.1016/j.orl.2016.05.004Sokoler LE, Standardi L, Edlund K, Poulsen NK, Madsen H, Jørgensen JB (2014) A Dantzig–Wolfe decomposition algorithm for linear economic model predictive control of dynamically decoupled subsystems. J Process Control 24(8):1225–1236. https://doi.org/10.1016/j.jprocont.2014.05.013Sridharan R, Simatupang TM (2009) Managerial views of supply chain collaboration. Gadjah Mada Int J Bus 11(2):253–273Stadtler H (2007) A framework for collaborative planning and state-of-the-art. OR Spectr 31(1):5–30. https://doi.org/10.1007/s00291-007-0104-5Stadtler H, Kilger C (2008) Supply chain management and advanced planning. In: Stadtler H, Kilger C (eds) Supply chain management and advanced planning. Concepts, models, software, and case studies. Springer, BerlinStank TP, Goldsby TJ, Vickery SK (1999) Effect of service supplier performance on satisfaction and loyalty of store managers in the fast food industry. J Oper Manag 17(4):429–447. https://doi.org/10.1016/S0272-6963(98)00052-7Taghipour A, Frayret JM (2013) An algorithm to improve operations planning in decentralized supply chains. In: 2013 international conference on advanced logistics and transport, ICALT 2013, pp 100–103. https://doi.org/10.1109/icadlt.2013.6568442Tang SH, Rahimi I, Karimi H (2016a) Objectives, products and demand requirements in integrated supply chain network design: a review. Int J Ind Syst Eng 23(2):181. https://doi.org/10.1504/IJISE.2016.076399Tang J, Zeng C, Pan Z (2016b) Auction-based cooperation mechanism to parts scheduling for flexible job shop with inter-cells. Appl Soft Comput 49:590–602. https://doi.org/10.1016/j.asoc.2016.08.046Thomas A, Singh G, Krishnamoorthy M, Venkateswaran J (2013) Distributed optimisation method for multi-resource constrained scheduling in coal supply chains. Int J Prod Res 51(9):2740–2759. https://doi.org/10.1080/00207543.2012.737955Thomas A, Venkateswaran J, Singh G, Krishnamoorthy M (2014) A resource constrained scheduling problem with multiple independent producers and a single linking constraint: a coal supply chain example. Eur J Oper Res 236(3):946–956. https://doi.org/10.1016/j.ejor.2013.10.006Thomas A, Krishnamoorthy M, Singh G, Venkateswaran J (2015) Coordination in a multiple producers–distributor supply chain and the value of information. Int J Prod Econ 167:63–73. https://doi.org/10.1016/j.ijpe.2015.05.020VICS (2004) Collaborative planning, forecasting and replenishment. Retrieved January 21, 2017, from https://www.gs1us.org/Vitasek K (2016) Strategic sourcing business models. Strateg Outsour Int J 9(2):126–138. https://doi.org/10.1108/SO-02-2016-0003Walther G, Schmid E, Spengler TS (2008) Negotiation-based coordination in product recovery networks. Int J Prod Econ 111(2):334–350. https://doi.org/10.1016/j.ijpe.2006.12.069Wang L, Pfohl HC, Berbner U, Keck AK (2016) Supply chain collaboration or conflict? Information sharing and supply chain performance in the automotive industry. In: Clausen U, Friedrich H, Thaller C, Geiger C (eds) Commercial transport. Springer, Cham, pp 303–318. https://doi.org/10.1007/978-3-319-21266-1Wenzel S, Paulen R, Krämer S, Beisheim B, Engell S (2016a) Shared resource allocation in an integrated petrochemical site by price-based coordination using quadratic approximation. In: 2016 European control conference, ECC 2016, pp 1045–1050. https://doi.org/10.1109/ecc.2016.7810427Wenzel S, Paulen R, Stojanovski G, Kraemer S, Beisheim B, Engell S (2016b) Optimal resource allocation in industrial complexes by distributed optimization and dynamic pricing. At-Automatisierungstechnik 64(6):428–442. https://doi.org/10.1515/auto-2016-0003Whang S (1995) Coordination in operat

    L'intertextualité dans les publications scientifiques

    No full text
    La base de données bibliographiques de l'IEEE contient un certain nombre de duplications avérées avec indication des originaux copiés. Ce corpus est utilisé pour tester une méthode d'attribution d'auteur. La combinaison de la distance intertextuelle avec la fenêtre glissante et diverses techniques de classification permet d'identifier ces duplications avec un risque d'erreur très faible. Cette expérience montre également que plusieurs facteurs brouillent l'identité de l'auteur scientifique, notamment des collectifs de chercheurs à géométrie variable et une forte dose d'intertextualité acceptée voire recherchée
    corecore