Abstract
In this paper, the static layout of a traditional multi-machine factory producing a set of distinct goods is integrated with a set of mobile production units - robots. The robots dynamically change their work position to increment the product rate of the different typologies of products in respect to the fluctuations of the demands and production costs during a given time horizon. Assuming that the planning time horizon is subdivided into a finite number of time periods, this particularly flexible layout requires the definition and the solution of a complex scheduling problem, involving for each period of the planning time horizon, the determination of the position of the robots, i.e., the assignment to the respective tasks in order to minimize production costs given the product demand rates during the planning time horizon.
We propose a decentralized multi-agent system (MAS) scheduling model with as many agents as there are the tasks in the system, plus a resource (robot) owner which assigns the robots to the tasks in each time period on the basis of the requests coming from the competing task agents. The MAS model is coupled with an iterative auction based negotiation protocol to coordinate the agents’ decisions. The resource prices are updated using a strategy inspired by the subgradient technique used in the Lagrangian relaxation approach. To measure the effectiveness of the results, the same are evaluated in respect to that of the benchmark centralized model.
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References
Barahona, F., Anbil, R.: The volume algorithm: producing primal solutions with a subgradient method. Math. Prog. 87, 385–399 (2000)
Buyya, R., Abramson, D., Giddy, J., Stockinger, H.: Economic models for resource management and scheduling in grid computing. Concurrency and Computation: Practice and Experience 14, 1507–1542 (2002)
Chen, H., Chu, C., Proth, J.M.: An improvement of the Lagrangean relaxation approach for job shop scheduling: A dynamic programming method. IEEE Trans. on Rob. and Autom. 14, 786–795 (1998)
Chevaleyre, Y., Endriss, U., Estivie, S., Maudet, N.: Multiagent resource allocation with k-additive utility functions. In: Proc. DIMACS-LAMSADE Workshop on Comp. Science and Decision Theory, Annales du LAMSADE, pp. 83–100 (2004)
Chevaleyre, Y., Dunne, P.E., Endriss, U., Lang, J., Lemaitre, M., Maudet, N., Padget, J., Phelps, S.: Issues in multiagent resource allocation. Informatica 30, 3–31 (2006)
Christensen, J.H.: Holonic manufacturing systems: Initial architecture and standards directions. In: Proc. 1st Euro Workshop on Holonic Manufacturing Systems, HMS Consortium, Hannover, Germany, December 1 (1994)
Clearwater, S.H.: Market-based control: a paradigm for distributed resource allocation. World Scientific Publishing Co., Inc, River Edge (1996)
Gaalman, G.: Bullwhip reduction for ARMA demand: The proportional order-up-to policy versus the full-state-feedback policy. Automatica 42, 1283–1290 (2006)
Held, M., Wolfe, P., Crowder, H.D.: Validation of subgradient optimization. Math. Prog. 6, 62–88 (1974)
Helms, E., Schraft, R.D., Haegele, M.: Rob work: Robot assistant in industrial environments. In: Proc. 11th IEEE workshop ROMAN 2002, pp. 399–404 (2002)
Huang, P.Y., Chen, C.S.: Flexible manufacturing systems: An overview and bibliography. Prod. and Inv. Mng. 27, 80–90 (1986)
Kraus, D.S.: Strategic negotiation in multiagent environments. MIT Press, Cambridge (2001)
Kurose, J.F., Simha, R.: A microeconomic approach to opt. resource allocation in distributed comp. systems. IEEE Transactions on Computers 38, 705–717 (1989)
Kutanoglu, E., Wu, S.D.: Incentive compatible, collaborative production scheduling with simple communication among distributed agents. Int. J. of Prod. Res. 44, 421–446 (2006)
Roundy, R.O., Maxwell, W.L., Herer, Y.T., Tayur, S.R., Getzler, A.W.: A price-directed approach to real-time scheduling of production operations. IIE Transactions 23, 149–160 (1991)
Schneider, J., Apfelbaum, D., Bagnell, D., Simmons, R.: Learning opportunity costs in multi-robot market based planners. In: Proc. 2005 IEEE International Conference on Robotics and Automation, pp. 1151–1156 (2005)
Selim, H.M., Askin, R.G., Vakharia, A.J.: Cell formation in group technology: review, evaluation and directions for future research. Comp. and Ind. Eng. 34, 3–20 (1998)
Sycara, K.P.: Multiagent Systems. AI Magazine 19(2), 79–92 (1998)
Vulkan, N., Jennings, N.R.: Efficient mechanisms for the supply of services in multi-agent environments. Decision Support Systems 28(1-2), 5–19 (2000)
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Giordani, S., Lujak, M., Martinelli, F. (2009). A Decentralized Scheduling Policy for a Dynamically Reconfigurable Production System. In: Mařík, V., Strasser, T., Zoitl, A. (eds) Holonic and Multi-Agent Systems for Manufacturing. HoloMAS 2009. Lecture Notes in Computer Science(), vol 5696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03668-2_10
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DOI: https://doi.org/10.1007/978-3-642-03668-2_10
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