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A generic coordination mechanism for lot-sizing in supply chains

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Abstract

A new generic mechanism to coordinate decentral planning of a group of independent and self-interested decision makers, who are searching for an agreeable contract regarding multiple interdependent issues, in the case of asymmetric information is presented. The basic idea of the mechanism is that the group members cooperatively carry out an evolutionary search in the contract space. Therefore the (1,λ)-selection procedure, which is used in many evolutionary strategies, is combined with the Borda maximin voting rule, which has been applied successfully in group decision making. The proposed mechanism is realized, applied and evaluated for production coordination in a supply chain. A decentralized variant of the multi-level uncapacitated lot-sizing problem (MLULSP) is taken as the production model. For the evaluation 95 problem instances are generated based on MLULSP instances taken from the literature, with problem sizes varying from 5 to 500 items, from 12 to 52 periods. Experimental results show that the proposed mechanism is effective to determine fair cost distributions.

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References

  1. Afentakis, P., & Gavish, B. (1986). Optimal lot-sizing algorithms for complex product structures. Operations Research, 34(2), 237–249.

    Article  Google Scholar 

  2. Afentakis, P., Gavish, B., & Karmarkar, U. S. (1984). Computationally efficient optimal solutions to the lot-sizing problem in multistage assembly systems. Management Science, 30(2), 222–239.

    Article  Google Scholar 

  3. Albrecht, M., Püttmann, C., Scheckenbach, B., Stadtler, H., & Braun, H. (2006). Recommendation for collaborative planning interface for service SC at the master planning level of an APS. Report 2.5. RWTH Aachen University. http://www.fir.rwth-aachen.de/projekt-seiten/incoco/files/DL_2_5.pdf. Accessed 15 January 2008.

  4. Arkin, E., Joneja, D., & Roundy, R. (1989). Computational complexity of uncapacitated multi-echelon production planning problems. Operations Research Letters, 8(2), 61–66.

    Article  Google Scholar 

  5. Arrow, K. J., Sen, A. K., & Suzumura, K. (2002). Handbook of social choice and welfare. Amsterdam: Elsevier.

    Google Scholar 

  6. Arunachalam, R., & Sadeh, N. (2005). The supply chain trading agent competition. Electronic Commerce Research and Applications, 5(1), 66–84.

    Article  Google Scholar 

  7. Bäck, T. (1993). Optimal mutation rates in genetic search. In J. Laird (Ed.), Proceedings of the 5th international conference on genetic algorithms, Urbana-Champaign, IL (pp. 2–8). San Francisco: Morgan Kaufmann.

    Google Scholar 

  8. Barbarosoglu, G. (2000). An integrated supplier-buyer model for improving supply chain coordination. Production Planning & Control, 11(8), 732–741.

    Article  Google Scholar 

  9. Benton, W. C., & Srivastava, R. (1985). Product structure complexity and multilevel lot sizing using alternative costing policies. Decision Sciences, 16(4), 357–369.

    Article  Google Scholar 

  10. Beyer, H.-G., & Schwefel, H.-P. (2002). Evolution strategies. Natural Computing, 1(1), 3–52.

    Article  Google Scholar 

  11. Bichler, M., Kersten, G. E., & Strecker, S. (2003). Towards a structured design of electronic negotiations. Group Decision and Negotiation, 12(4), 311–335.

    Article  Google Scholar 

  12. Bookbinder, J. H., & Koch, L. A. (1990). Production planning for mixed assembly/arborescent systems. Journal of Operations Management, 9, 7–23.

    Article  Google Scholar 

  13. Brams, S. J., & King, D. L. (2005). Efficient fair division: Help the worst off or avoid envy? Rationality and Society, 17(4), 387–421.

    Article  Google Scholar 

  14. Brams, S. J., & Taylor, A. D. (1996). Fair division: From cake-cutting to dispute resolution. New York: Cambridge University Press.

    Google Scholar 

  15. Brams, S. J., & Togman, J. M. (1996). Camp David: Was the agreement fair? Conflict Management and Peace Science, 15(1), 99–112.

    Article  Google Scholar 

  16. Cachon, G. P. (2004). Supply chain coordination with contracts. In A. G. de Kok & S. C. Graves (Eds.), Supply chain management: Design, coordination and operation (pp. 220–339). Amsterdam: Elsevier.

    Google Scholar 

  17. Cachon, G. P., & Lariviere, M. A. (2005). Supply chain coordination with revenue-sharing contracts: Strength and limitations. Management Science, 51(1), 30–44.

    Article  Google Scholar 

  18. Chen, C.-L., Wang, B.-W., & Lee, W.-C. (2003). Multiobjective optimization for a multienterprise supply chain network. Industrial & Engineering Chemistry Research, 42(9), 1879–1889.

    Article  Google Scholar 

  19. Chevaleyre, Y., Dunne, P. E., Endriss, U., Lang, J., Lemaître, M., Maudet, N., Padget, J., Phelps, S., Rodríguez-Aguilar, J. A., & Sousa, P. (2006). Issues in multiagent resource allocation. Informatica, 30(1), 3–31.

    Google Scholar 

  20. Christopher, M. (1998). Logistics and supply chain management—strategies for reducing cost and improving service (2nd edn.). London: Financial Times/Prentice Hall.

    Google Scholar 

  21. Chu, C.-L., & Leon, V. J. (2008). Single-vendor multi-buyer inventory coordination under private information. European Journal of Operational Research, 191(2), 484–502.

    Article  Google Scholar 

  22. Coleman, B. J., & McKnew, M. A. (1991). An improved heuristic for multilevel lot sizing in material requirements planning. Decision Sciences, 22(1), 136–156.

    Article  Google Scholar 

  23. Conitzer, V., & Sandholm, T. (2002). Vote elicitation: Complexity and strategy-proofness. In R. Dechter, M. Kearns. & R. Sutton (Eds.), Proceedings of the 18th national conference on artificial intelligence, Edmonton, Alberta, Canada (pp. 392–397). Menlo Park: American Association for Artificial Intelligence.

    Google Scholar 

  24. Conitzer, V., & Sandholm, T. (2005). Communication complexity of common voting rules. In J. Riedl, M. Kearns, & M. Reiter (Eds.), Proceedings of the 6th ACM conference on electronic commerce, Vancouver, BC, Canada (pp. 78–87). New York: ACM.

    Chapter  Google Scholar 

  25. Dellaert, N. P., & Jeunet, J. (2000). Solving large unconstrained multilevel lot-sizing problems using a hybrid genetic algorithm. International Journal of Production Research, 38(5), 1083–1099.

    Article  Google Scholar 

  26. Dellaert, N. P., & Jeunet, J. (2003). Randomized multi-level lot-sizing heuristics for general product structures. European Journal of Operational Research, 148(1), 211–228.

    Article  Google Scholar 

  27. De Sinopoli, F., Dutta, B., & Laslier, J.-F. (2006). Approval voting: Three examples. International Journal of Game Theory, 35(1), 27–38.

    Article  Google Scholar 

  28. Dudek, G. (2004). Collaborative planning in supply chains: A negotiation-based approach. Berlin: Springer.

    Google Scholar 

  29. Dudek, G., & Stadtler, H. (2005). Negotiation-based collaborative planning between supply chains partners. European Journal of Operational Research, 163(3), 668–687.

    Article  Google Scholar 

  30. Dudek, G., & Stadtler, H. (2007). Negotiation-based collaborative planning in divergent two-tier supply chains. International Journal of Production Research, 45(2), 465–484.

    Article  Google Scholar 

  31. Ehtamo, H., Verkama, M., & Hämäläinen, R. P. (1999). How to select fair improving directions in a negotiation model over continuous issues. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 29(1), 26–33.

    Article  Google Scholar 

  32. Ehtamo, H., Kettunen, E., & Hämäläinen, R. P. (2001). Searching for joint gains in multi-party negotiations. European Journal of Operational Research, 130(1), 54–69.

    Article  Google Scholar 

  33. Endriss, U., & Maudet, N. (2004). Welfare engineering in multiagent systems. Berlin: Springer.

    Google Scholar 

  34. Ertogral, K., & Wu, S. D. (2000). Auction-theoretic coordination of production planning in the supply chain. IIE Transactions, 32(10), 931–940.

    Google Scholar 

  35. Fandel, G., & Stammen-Hegener, C. (2006). Simultaneous lot sizing and scheduling for multi-product multi-level production. International Journal of Production Economics, 104(2), 308–316.

    Article  Google Scholar 

  36. Fink, A. (2004). Supply chain coordination by means of automated negotiations. In Proceedings of the 37th Hawaii international conference on system sciences (CD-ROM, p. 10). Washington: IEEE Computer Society.

    Google Scholar 

  37. Fink, A. (2006). Supply chain coordination by means of automated negotiation between autonomous agents. In B. Chaib-draa & J. Müller (Eds.), Multiagent-based supply chain management (pp. 351–372). Berlin: Springer.

    Chapter  Google Scholar 

  38. Fink, A. (2007). Barwertorientierte Projektplanung mit mehreren Akteuren mittels eines verhandlungsbasierten Koordinationsmechanismus. In O. Oberweis, C. Weinhardt, H. Gimpel, A. Koschmider, V. Pankratius, & B. Schnizler (Eds.), eOrganisation: service-, prozess-, market-engineering (pp. 465–482). Karlsruhe: Universitätsverlag.

    Google Scholar 

  39. Fisher, R. (1978). International mediation: A working guide. New York: International Peace Academy.

    Google Scholar 

  40. Fisher, R., & Ury, W. (1987). Getting to yes. Negotiating agreement without giving in. New York: Penguin.

    Google Scholar 

  41. Gjerdrum, J., Shah, N., & Papageorgiou, L. G. (2001). Transfer prices for multienterprise supply chain optimization. Industrial & Engineering Chemistry Research, 40(7), 1650–1660.

    Article  Google Scholar 

  42. Gjerdrum, J., Shah, N., & Papageorgiou, L. G. (2002). Fair transfer price and inventory holding policies in two-enterprise supply chains. European Journal of Operational Research, 143(3), 582–599.

    Article  Google Scholar 

  43. Heckelman, J. C. (2003). Probabilistic Borda rule voting. Social Choice and Welfare, 21(3), 455–468.

    Article  Google Scholar 

  44. Homberger, J. (2008). A parallel genetic algorithm for the multi-level unconstrained lot-sizing problem. INFORMS Journal on Computing, 20(1), 124–132.

    Article  Google Scholar 

  45. Homberger, J., & Gehring, H. (2009). An ant colony optimization approach for the multi-level unconstrained lot-sizing problem. In Proceedings of the 42nd Hawaii international conference on system sciences (CD-ROM, p. 7) Washington: IEEE Comput. Soc.

    Google Scholar 

  46. Ito, T., Klein, M., & Hattori, H. (2007). Multi-issue negotiation protocol for agents: Exploring nonlinear utility spaces. In M. M. Veloso (Ed.), Proceedings of the 20th international joint conference on artificial intelligence, Hyderabad, India (pp. 1347–1352). Menlo Park: AAAI Press.

    Google Scholar 

  47. Jaffe, J. (1981). Bottleneck flow control. IEEE Transactions on Communications, 26(7), 954–962.

    Article  Google Scholar 

  48. Jennings, N. R., Parsons, S. D., Sierra, C., & Faratin, P. (2000). Issues in automated negotiation and electronic commerce: Extending the contract net framework. In M. N. Huhns & M. P. Singh (Eds.), Readings in agents (pp. 66–73). San Francisco: Morgan Kaufmann.

    Google Scholar 

  49. Jennings, N. R., Faratin, P., Lomuscio, A. R., Parsons, S., Woolridge, M., & Sierra, C. (2001). Automated negotiation: Prospects, methods and challenges. Group Decision and Negotiation, 2(10), 199–215.

    Article  Google Scholar 

  50. Jones, G. T. (2006). Hybrid computational models for the mediated negotiation of complex contracts. In Proceedings of the 14th annual conference of the North American association for computational social and organizational sciences, Notre Dame, Indiana. http://www.gregorytoddjones.com/pdfs/NAACSOS%20Complex%20Contracts.pdf. Accessed 6 Mai 2009.

  51. Kersten, G. E. (2003). The science and engineering of e-negotiation: An introduction. In Proceedings of the 36th Hawaii international conference on system sciences (CD-ROM, p. 10). Washington: IEEE Comput. Soc.

    Google Scholar 

  52. Klein, M., Faratin, P., & Bar-Yam, Y. (2002). Using an annealing mediator to solve the prisoners’ dilemma in the negotiation of complex contracts source. In J. A. Padget, O. Shehory, D. C. Parkes, N. M. Sadeh, & W. E. Walsh (Eds.), Lecture notes in computer science: Vol. 2531. Agent-mediated electronic commerce IV. Designing mechanisms and systems (pp. 194–202). London: Springer.

    Chapter  Google Scholar 

  53. Klein, M., Faratin, P., Sayama, H., & Bar-Yam, Y. (2003a). Negotiating complex contracts. Group Decision and Negotiation, 12(2), 111–125.

    Article  Google Scholar 

  54. Klein, M., Faratin, P., Sayama, H., & Bar-Yam, Y. (2003b). Protocols for negotiating complex contracts. IEEE Intelligent Systems, 18(6), 32–38.

    Article  Google Scholar 

  55. Klein, M., Faratin, P., Sayama, H., & Bar-Yam, Y. (2003c). Negotiation algorithms for collaborative design settings. In ISPE conference editors, Proceedings of the 10th ISPE international conference on concurrent engineering, research & applications (pp. 161–167). London: Taylor & Francis.

    Google Scholar 

  56. Kube, S., & Puppe, C. (2009). (When and how) do voters try to manipulate? Public Choice, 139(1), 39–52.

    Article  Google Scholar 

  57. Kumar, A., & Kleinberg, J. (2000). Fairness measures for resource allocation. In Proceedings of the 41st annual IEEE symposium on foundations of computer science (pp. 75–85).

  58. Lau, J. S. K., Huang, G. Q., Mak, K. L., & Liang, L. (2005). Distributed project scheduling with information sharing in supply chain: Part II theoretical analysis and computational study. International Journal of Production Research, 43(23), 4899–4927.

    Article  Google Scholar 

  59. Lee, S., & Kumara, S. (2007). Multiagent system approach for dynamic lot-sizing in supply chains. In H. Jung, B. Jeong, & F. F. Chen (Eds.), Trends in supply chain design and management—Part II (pp. 311–330). London: Springer.

    Chapter  Google Scholar 

  60. Lin, R. J. (2004). Bilateral multi-issue contract negotiation for task redistribution using a mediation service. Agent-Mediated Electronic Commerce workshop, New York, 2004

  61. Lin, R. J., & Chou, S.-C. T. (2003). Mediating a bilateral multi-issue negotiation. Electronic Commerce Research and Applications, 3(2), 126–138.

    Article  Google Scholar 

  62. Lomuscio, A. R., Wooldridge, M., & Jennings, N. R. (2003). A classification scheme for negotiation in electronic commerce. Group Decision and Negotiation, 12(1), 31–56.

    Article  Google Scholar 

  63. Maes, J., McClain, J. O., & Van Wassenhove, L. N. (1991). Multilevel capacitated lot sizing complexity and LP based heuristics. European Journal of Operational Research, 53(2), 131–148.

    Article  Google Scholar 

  64. Marbach, P. (2002). Priority service and max-min fairness. In Proceedings of INFOCOM 2002. The 21st annual joint conference of the IEEE computer and communications societies (Vol. 1, pp. 266–275). doi:10.1109/INFCOM.2002.1019268

  65. Meinhardt, H. I. (2002). Cooperative decision making in common pool situations. Berlin: Springer.

    Google Scholar 

  66. Moulin, H. (1988). Axioms of cooperative decision making. New York: Cambridge University Press.

    Google Scholar 

  67. Moulin, H. (2003). Fair division and collective welfare. Cambridge: MIT Press.

    Google Scholar 

  68. Nash, J. F. (1950). The bargaining problem. Econometrica, 18(2), 155–162.

    Article  Google Scholar 

  69. Nissen, V. (1997). Quadratic assignment. In T. Bäck, D. Fogel, & Z. Michalewicz (Eds.), Handbook of evolutionary computation. New York: Oxford University Press. Section G9.10.

    Google Scholar 

  70. Nissen, V., & Gold, S. (2008). Intelligent network design with an evolution strategy. In Tagungsband der Teilkonferenz der Multikonferenz Wirtschaftsinformatik. München, Germany (pp. 189–204).

  71. Pitakaso, R., Almeder, C., Doerner, K. F., & Hartl, R. F. (2007). A MAX-MIN ant system for unconstrained multi-level lot-sizing problems. Computers and Operations Research, 34(9), 2533–2552.

    Article  Google Scholar 

  72. Raiffa, H. (1982). The art and science of negotiation. Cambridge: Belknap Press of Harvard University.

    Google Scholar 

  73. Rawls, J. (1971). A theory of justice. Oxford: Oxford University Press.

    Google Scholar 

  74. Rechenberg, I. (1971). Evolutionsstrategie—Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. PhD-Thesis, Technical University of Berlin.

  75. Salomon, M., & Kuik, R. (1993). Statistical search methods for lotsizing problems. Annals of Operations Research, 41(4), 453–468.

    Article  Google Scholar 

  76. Sandholm, T. (1999). Distributed rational decision making. In G. Weiss (Ed.), Multiagent systems: A modern approach to distributed artificial intelligence (pp. 201–258). Cambridge: MIT Press.

    Google Scholar 

  77. Sandholm, T. (2003). Automated mechanism design: A new application area for search algorithms. In Principles and practice of constraint programming, CP 2003. Proceedings of 9th conference CP 2003 (pp. 19–36). http://www.cs.cmu.edu/~sandholm/. Accessed March 02, 2008.

  78. Schwefel, H.-P. (1974). Evolutionsstrategie und numerische Optimierung. PhD Thesis, Technical University of Berlin.

  79. Stadtler, H. (2009). A framework for collaborative planning and state-of-the-art. OR Spectrum, 31(1), 5–30.

    Article  Google Scholar 

  80. Steinberg, E., & Napier, H. A. (1980). Optimal multi-level lot sizing for requirements planning systems. Management Science, 26(12), 1258–1271.

    Article  Google Scholar 

  81. Straube, F., & Beyer, I. (2006). Decentralized planning in global and local networks—coordination of inter- and intraorganizational networks at tactical level. In Proceedings of the international federation of scholarly associations of management (IFSAM) VIIIth World congress 2006, CD ROM: Track 10 Global and local Networks, p. 7.

  82. Ströbel, M., & Weinhardt, C. (2003). The Montreal taxonomy for electronic negotiations. Group Decision and Negotiation, 12(2), 143–164.

    Article  Google Scholar 

  83. Tung, H.-W., & Lin, R. J. (2005). Automated contract negotiation using a mediation service. In 7th IEEE international conference on E-commerce technology, CEC’05 (pp. 374–377), Munich, Germany, 2005.

  84. Veinott Jr., A. F. (1969). Minimum concave-cost solution of Leontief substitution models of multifacility inventory systems. Operations Research, 17(2), 262–291.

    Article  Google Scholar 

  85. Veral, E. A., & LaForge, R. L. (1985). The performance of a simple incremental lot-sizing rule in a multilevel inventory environment. Decision Sciences, 16(1), 57–72.

    Article  Google Scholar 

  86. Vetschera, R. (2005). Strategic manipulation of preference information in multi-criteria group decision methods. Group Decision and Negotiation, 14(5), 393–414.

    Article  Google Scholar 

  87. Voß, S., & Woodruff, D. L. (2006). Introduction to computational optimization models for production planning in a supply chain (2nd edn.). Berlin: Springer.

    Google Scholar 

  88. Weiß, G. (2001). Multiagent systems—a modern approach to distributed artificial intelligence (3rd edn.). Cambridge: MIT Press.

    Google Scholar 

  89. Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics, 1(6), 80–83.

    Article  Google Scholar 

  90. Willmes, L., & Bäck, T. (2003). Evolution strategies for engineering design optimization. In K. J. Bathe (Ed.), Proceedings of 2nd MIT conference on computational fluid and solid mechanics (Vol. 2, pp. 2394–2397). Cambridge: MIT Press.

    Google Scholar 

  91. Yelle, L. E. (1979). Materials requirements lot sizing: A multilevel approach. International Journal of Production Research, 17(3), 223–232.

    Article  Google Scholar 

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  1. Hochschule für Technik, Faculty of Geomatics, Computer Science and Mathematics, Stuttgart University of Applied Sciences, Schellingstr. 24, 70174, Stuttgart, Germany

    Jörg Homberger

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Homberger, J. A generic coordination mechanism for lot-sizing in supply chains. Electron Commer Res 11, 123–149 (2011). https://doi.org/10.1007/s10660-010-9053-1

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