Abstract
Combinatorial auctions are used as a distributed coordination mechanism in Multiagent Systems. The use of combinatorial auctions as negotiation and coordination mechanism is especially appropriate in systems with interdependencies and complementarities such as manufacturing scheduling systems. In this work we review some updating price mechanisms for combinatorial auctions based on the Lagrangian Relaxation Method. We focus our research to solve the optimization scheduling problem in the shop floor, taking into account the objectives of resource allocation in dynamic environments, i.e. -robustness, stability, adaptability, and efficiency-.
Chapter PDF
Similar content being viewed by others
References
Pinedo, E.P.M.L.: Scheduling, 2nd edn. Springer, New York (2008)
Shen, W.: Distributed manufacturing scheduling using intelligent agents. Intelligent Systems 17(1), 88–94 (2002)
Shen, W., et al.: Applications of agent-based systems in intelligent manufacturing: An updated review. Advanced Engineering Informatics 20(4), 415–431 (2006)
Lee, J.-H., Kim, C.-O.: Multi-agent systems applications in manufacturing systems and supply chain management: a review paper. International Journal of Production Research 46(1), 233–265 (2008)
Ouelhadj, D., Petrovic, S.: A survey of dynamic scheduling in manufacturing systems. Journal of Scheduling 12(4), 417–431 (2009)
Kutanoglu, E., Wu, S.D.: On combinatorial auction and Lagrangean relaxation for distributed resource scheduling. IIE Transactions 31(9), 813–826 (1999)
Wellman, M.P.: A Market-Oriented Programming Environment and its Application to Distributed Multicommodity Flow Problems. Journal of Artificial Intelligence Research 1, 1–23 (1993)
Rassenti, S., Smith, V., Bulfin, R.: A Combinatorial Auction Mechanism for Airport Time Slot Allocation. The Bell Journal of Economics 13(2), 402–417 (1982)
de Vries, S., Vohra, R.V.: Combinatorial Auctions: A Survey. Informs Journal on Computing 15(3), 284–309 (2003)
Dewan, P., Joshi, S.: Auction-based distributed scheduling in a dynamic job shop environment. International Journal of Production Research 40(5), 1173–1191 (2002)
Fisher, M.L.: The Lagrangian Relaxation Method for Solving Integer Programming Problems. Management Science 50(12 Suppl.), 1861–1871 (2004)
Duffie, N.A.: Synthesis of heterarchical manufacturing systems. Comput. Ind. 14(1-3), 167–174 (1990)
Kaskavelis, C.A., Caramanis, M.C.: Efficient Lagrangian relaxation algorithms for industry size job-shop scheduling problems. IIE Transactions 30(11), 1085–1097 (1998)
Sun, T., Luh, P., Liu, M.: Lagrangian relaxation for complex job shop scheduling. In: Proceedings 2006 IEEE International Conference on En Robotics and Automation, ICRA 2006, pp. 1432–1437 (2006)
Tang, L., Xuan, H., Liu, J.: A new Lagrangian relaxation algorithm for hybrid flowshop scheduling to minimize total weighted completion time. Computers & Operations Research 33(11), 3344–3359 (2006)
Ni, M., Luh, P., Moser, B.: An Optimization-Based Approach for Design Project Scheduling. IEEE Transactions on Automation Science and Engineering 5(3), 394–406 (2008)
Arauzo, J., et al.: Gestión eficiente de carteras de proyectos. Propuesta de un sistema inteligente de soporte a la decisión para oficinas técnicas y empresas consultoras= efficient projet porfolio management. And intelligent decision system for engineering and consultancy firms. Dyna 84(9), 761–772 (2009)
Geoffrion, A.M.: Lagrangean relaxation for integer programming. In: En Approaches to Integer Programming, pp. 82–114 (1974)
Guignard, M.: Lagrangean relaxation. TOP 11(2), 151–200 (2003)
Camerini, P.M., Fratta, L., Maffioli, F.: On improving relaxation methods by modified gradient techniques. En Nondifferentiable Optimization, 26–34 (1975)
Crowder, H.: Computational Improvements for Subgradient Optimization. In: Symposia Mathematica, pp. 357–372. Academic Press, New York (1976)
Brännlund, U.: A generalized subgradient method with relaxation step. Mathematical Programming 71(2), 207–219 (1995)
Wang, J., et al.: An optimization-based algorithm for job shop scheduling. SADHANA 22, 241–256 (1997)
Zhao, X., Luh, P., Wang, J.: The surrogate gradient algorithm for Lagrangian relaxation method. In: Proceedings of the 36th IEEE Conference on Decision and Control, vol. 1, pp. 305–310 (1997)
Chen, H., Luh, P.: An alternative framework to Lagrangian relaxation approach for job shop scheduling. European Journal of Operational Research 149(3), 499–512 (2003)
Zhao, X., Luh, P.B., Wang, J.: Surrogate Gradient Algorithm for Lagrangian Relaxation. Journal of Optimization Theory and Applications 100(3), 699–712 (1999)
Zhao, X., Luh, P.: Fuzzy gradient method in Lagrangian relaxation for integer programming problems. In: Proceedings of the 37th IEEE Conference on En Decision and Control, vol. 3, pp. 3372–3377 (1998)
Demirkol, E., Mehta, S., Uzsoy, R.: Benchmarks for shop scheduling problems. European Journal of Operational Research 109(1), 137–141 (1998)
Kreipl, S.: A large step random walk for minimizing total weighted tardiness in a job shop. Journal of Scheduling 3(3), 125–138 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 IFIP
About this paper
Cite this paper
Lavios Villahoz, J.J., del Olmo Martínez, R., Arauzo, A.A. (2010). Price-Setting Combinatorial Auctions for Coordination and Control of Manufacturing Multiagent Systems: Updating Prices Methods. In: Ortiz, Á., Franco, R.D., Gasquet, P.G. (eds) Balanced Automation Systems for Future Manufacturing Networks. BASYS 2010. IFIP Advances in Information and Communication Technology, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14341-0_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-14341-0_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14340-3
Online ISBN: 978-3-642-14341-0
eBook Packages: Computer ScienceComputer Science (R0)