Abstract
To stay competitive in the new dynamic market having large fluctuations in product demand, manufacturing companies must use systems that not only produce their goods with high productivity but also allow for rapid response to market changes. Reconfigurable manufacturing system (RMS) is a new paradigm that enables manufacturing systems to respond quickly and cost effectively to market demand. In other words, RMS is a system designed from the outset, for rapid changes in both hardware and software components, in order to quickly adjust its production capacity to fluctuations in market demand and adapt its functionality to new products. The effectiveness of an RMS depends on implementing its key characteristics and capabilities in the design as well as utilization stage. This paper focuses on the utilization stage of an RMS and introduces a methodology to effectively adjust scalable production capacities and the system functionalities to market demands. It is supposed that arrival orders of product families follow the Poisson distribution. The orders are lost if they are not met immediately. Considering these assumptions, a mixed integer nonlinear programming model is developed to determine optimum sequence of production tasks, corresponding configurations, and batch sizes. A genetic algorithm-based procedure is used to solve the model. The model is also applied to make decision on how to improve the performance of an RMS. Since there is no practical RMS, a numerical example is used to validate the results of the proposed model and its solution procedure.
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References
Koren Y (2006) General RMS characteristics. Comparison with dedicated and flexible systems. Chapter 3 in the reconfigurable manufacturing systems and transformable factories. Springer, Netherlands
Mehrabi MG, Ulsoy AG, Koren Y (2000) Reconfigurable manufacturing systems: key to future manufacturing. J Intell Manuf 11:403–419
Chick SE, Olsen TL, Sethuraman K, Stecke KE, White CC (2000) A descriptive multi-attribute model for reconfigurable machining system selection examining buyer–supplier relationships. Int J Agil Manag Syst 2(1):33–48
Zhao X, Wang J, Zhenbi L (2000) A stochastic model of a reconfigurable manufacturing system, part 1: a frametask. Int J Prod Res 38(10):2273–2285
Zhao X, Wang J, Zhenbi L (2000) A stochastic model of a reconfigurable manufacturing system, part 2: optimal configurations. Int J Prod Res 38(12):2829–2842
Zhao X, Wang J, Zhenbi L (2001) A stochastic model of a reconfigurable manufacturing system, part 3: optimal selection policy. Int J Prod Res 39(4):747–758
Zhao X, Wang J, Zhenbi L (2001) A stochastic model of a reconfigurable manufacturing system, part 4: performance measure. Int J Prod Res 39(6):1113–1126
Yang T, Peters BA (1998) Flexible machine layout design for dynamic and uncertain production environments. Eur J Oper Res 108:49–64
Kochhar JS, Hwragu SS (1999) Facility layout design in a changing environment. Int J Prod Res 37:2429–2446
Lee GH (1997) Reconfigurability consideration design of components and manufacturing systems. Int J Adv Manuf Technol 13:376–386
Takahashi K, Morikawa K, Myreshka, Ohiro T, Takuba A (2006) A stochastic model for deciding an optimal production order and its corresponding configuration in a reconfigurable manufacturing system with multiple product groups. Chapter 30 in the reconfigurable manufacturing systems and transformable factories. Springer, Netherlands
Abbasi M, Houshmand M (2009) Production planning of reconfigurable manufacturing systems with stochastic demands using tabu search. Int J Manuf Technol Manage 17(1/2):125–148
Gross D, Harris CM (1985) Fundamentals of queuing theory. Wiley, New York
Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor
Gen M, Cheng R (2000) Genetic algorithms and engineering optimizations. Wiley, New York
Tay JC, Wibowo D (2004) An effective chromosome representation for evolving flexible job shop schedules. GECCO. In: Lecture Notes in Computer Science, vol. 3103. Springer, Berlin, pp. 210–21
Pezzella F, Morganti G, Ciaschetti G (2007) A genetic algorithm for the flexible job-shop scheduling problem. Comput Oper Res 35:3202–3212. doi:10.1016/j.cor.2007.02.014
Iyer SK, Saxena B (2004) Improved genetic algorithm for the permutation flow shop scheduling problem. Comput Oper Res 31:593–606
Liaw CF (2000) A hybrid genetic algorithm for the open shop scheduling problem. Eur J Oper Res 124:28–42
Chu PC, Beasley JE (1997) A genetic algorithm for the generalized assignment problem. Comput Oper Res 24(1):17–23
Abdi MR, Labib AW (2004) Grouping and selecting products: the design key of reconfigurable manufacturing systems (RMSs). Int J Prod Res 42(3):521–546
Abdi M, Labib AW (2003) A design strategy for reconfigurable manufacturing systems (RMSs) using analytical hierarchical process (AHP): a case study. Int J Prod Res 41(10):2273–2299
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Abbasi, M., Houshmand, M. Production planning and performance optimization of reconfigurable manufacturing systems using genetic algorithm. Int J Adv Manuf Technol 54, 373–392 (2011). https://doi.org/10.1007/s00170-010-2914-x
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DOI: https://doi.org/10.1007/s00170-010-2914-x