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Equilibrium in competing supply-demand flow problems

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this work, we consider a basic version of the Supply-Demand Flow Problem with a set of clients whose demand has to be satisfied by two sets of production centers, each owned by one agent. The agents want to choose the demand centers optimally according to their own independent objective functions. We formulate the problem of efficiently determining an equilibrium solution. We prove that the solution is Pareto-optimal and equivalent to a Kalai-Smorodinsky solution of a suitably defined bargaining problem. Moreover, the results of a computational study are reported, showing that such an equilibrium provides a good compromise between agents' expectations and a reasonable solution in terms of the total cost experienced by the two agents. The proposed approach fits in many competitive problems arising in the management of distributed supply chains.
Czasopismo
Rocznik
Strony
7--17
Opis fizyczny
Bibliogr. 28 poz., rys., wykr.
Twórcy
autor
autor
autor
autor
Bibliografia
  • [1] Albino V., Carhonara N., Giannoccaro I., Coordination mechanisms based on cooperation and competition within industrial districts: An agent-based computational approach. Journal of Societies and Social Simulation, 6, No. 4, 2003.
  • [2] Albino V., Carbonara N., Giannoccaro I., Supply chain cooperation in industrial districts: A simulation analysis, European Journal of Operational Research, 177, 2007, pp. 261-280.
  • [3] Anussornnitisarn P., Nof S.Y., Etzion O.. Decentralized control of cooperative and autonomous agents for solving the distributed resources allocation problem. Int. Journal of Production Economics, 98, 2005, pp. 114-128.
  • [4] Bielli M., Felici G., Mecoli M., Innovation in modeling complex supply chain and logistic networks, Proc. Int. Conf. Information Systems, Logistics and Supply Chain, Lyon, France, 14-17 May 2006.
  • [5] Bielli M., Felici G., Mecoli M., Decision support tools in designing supply chains and logistic networks, Proc. MITIP Conference, Budapest, Hungary, 11-12 September 2006, pp. 205-210.
  • [6] Bielli M., Mecoli M., Villa A.. Autonomy versus efficiency in management of large-scale logistic networks, Preprints of the 16th IFAC World Conference, Prague, 3-8 July 2005.
  • [7] Burkard R.E., Cela E., Linear Assignment Problems and Extensions, [in:] D.-Z. Du, P.M. Pardalos (eds.), Handbook of Combinatorial Optimization. Supplement A, Kluwer Academic Publishers 1999, pp. 75-149.
  • [8] Dang T.T., Frankovic B., Budinska 1., The multi-agent approach to supply chain management optimization, Acta Electrotechnicaet Informatica, 93(4), 2003, p. 512.
  • [9] Dudek G.. Stadtler H., Negotiation-based collaborative planning between supply chain partners, European Journal of Operational Research, 163, 2005, pp. 668-687.
  • [10] Felici G., Gentile C., Rinaldi G., Solving large MIP models in supply chain management with Branch&Cut, IASI Tech. Rep., 522, 2000.
  • [11] Felici G., Mecoli M., Mirchandani R.B.. Pacifici A., Assignment Problem with two Competing Agents. I.A.S.I.-CNR Tech. Rep., R-617, Roma. Italy, October 2004.
  • [12] Jeong I., Leon V.J., Decision-making and cooperative interaction via coupling agents in organizationally distributed systems. II Trans., 34. 2002, pp. 789-802.
  • [13] Kalai E.. Smorodinsky M., Others solutions to Nash’s bargaining problem, Econometrica. 43, 1975, pp. 513-518.
  • [14] Lin X., Soh L., Hybrid negotiation for resource coordination in Multi-agent Systems, Web Intelligence and Agent Systems, (WIAS), 3(4), 2005. pp. 231-259.
  • [15] Moyaux T., Chaib-draa B., D’Amours S.. Multi-agent simulation of collaborative strategies in a supply chain, Proc. 3rd Int. Joint Conf. Autonomous Agents and Mulliagent Systems, ACM Press, N.Y., 19-23 July 2004.
  • [16] Nagarajan M., Sosic G., Game-Theoretic Analysis of Cooperation Among Supply Chain Agents, Review and Extensions, (to appear in 2007).
  • [17] Nagurney A., Matsypura D., Global supply chain network dynamics with multicriteria decision-making under risk and uncertainly, Transportation Research E, 41, 2005, pp. 585-612.
  • [18] Nash J.F., The bargaining problem, Econometrica, 18, 1950, pp. 155-162.
  • [19] Ortuzar J.D., Willumsen L.G., Modelling Transport. Wiley, 2001.
  • [20] Parsons S., Wooldridge M., Game Theory and Decision Theory in Multi-Agent Systems, Autonomous Agents and Multi-Agent Systems, Kluwer Academic Press. Hingham, MA, USA, 5, 3, 2002, pp. 243-254.
  • [21] Rossi F., Arbib C., Optimal resource assignment through negotiations in a multi-agent manufacturing system, II Trans. Design and Manufacturing, 10, 32, 2000, pp. 963-974.
  • [22] Rudherg M., Olhager J., Manufacturing networks and supply chains: an operations strategy perspective. Omega. 31, I, 2003, pp. 29-39.
  • [23] Santos E., Zhang F., Luh P., Intro-organizational logistics management through multi-agents systems. Electronic Commerce Research, 3(12), 2003. pp. 337-364.
  • [24] Silva C., Sousa J.C., da Costa J.S., Runkler T., A multi-agent approach for supply chain management using ant colony optimization. Proc. the 2004 IEEE Int. Conf. System, Man and Cybernetics, The Hague, The Netherlands, 2004.
  • [25] Stadtler H., Supply chain management and advanced planning basics, overview and challenges, European Journal of Operational Research, 163, 2005, pp. 575-588.
  • [26] Villa A., Emerging trends in large-scale supply chain management. Int. Journal of Production Research, 40, 15, 2002a, pp. 3487-3498.
  • [27] Villa A., Autonomy versus efficiency in multi-agents management of extended enterprises. Journal of Intelligent Manufacturing, 13, 2002b, pp. 429-438.
  • [28] Villa A., Modelling collaborative demand and supply networks, 21st European Conf. on Operational Research, 2-5 July 2006. Iceland.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0027-0089
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