Improving Multi-actor Production, Inventory and Transportation Planning through Agent-Based Optimization
We present an agent-based optimization approach that is built upon the principles of Dantzig-Wolfe column generation, which is a classic reformulation technique. We show how the approach can be used to optimize production, inventory, and transportation, which may result in improved planning for the involved supply chain actors. An important advantage is the possibility to keep information locally when possible, while still enabling global optimization of supply chain activities. In particular, the approach can be used as strategic decision support to show how the involved actors may benefit from applying Vendor Managed Inventory (VMI). In a case study, the approach has been applied to a real-world integrated production, inventory and routing problem, and the results from our experiments indicate that an increased number of VMI customers may give a significant reduction of the total cost in the system. Moreover, we analyze the communication overhead that is caused by using an agent-based, rather than a traditional (non agent-based) approach to decomposition, and some advantages and disadvantages are discussed.
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- 3.Daugherty, P.J., Myers, M.B., Autry, C.W.: Automatic replenishment programs: an empirical examination. Journal of Business Logistics 20(2), 63–82 (1999)Google Scholar
- 7.Petcu, A.: A class of algorithms for distributed constraint optimization. Ph.D. thesis, Swiss Federal Institute of Technology, Lausanne, Switzerland (2007)Google Scholar
- 13.Toth, P., Vigo, D. (eds.): The Vehicle Routing Problem. SIAM monographs on discrete mathematics and applications, Philadelphia (2002)Google Scholar
- 15.Sarmiento, A.M., Nagi, R.: A review of integrated analysis of production-distribution systems. IIE Transactions 31, 1061–1074 (1999)Google Scholar
- 24.Davidsson, P., Holmgren, J., Persson, J.A., Ramstedt, L.: Multi agent based simulation of transport chains. In: AAMAS 2008: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1153–1160. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2008)Google Scholar
- 25.Sohier, E.: Modelling a complex production scheduling problem - optimization techniques. Master’s thesis, Blekinge Institute of Technology, Sweden (2006)Google Scholar