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Work-in-process scheduling by evolutionary tuned fuzzy controllers

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In this paper, an evolutionary algorithm (EA) strategy for the optimization of generic work-in-process (WIP) scheduling fuzzy controllers is presented. The EA strategy is used to tune a set of fuzzy control modules that are used for distributed and supervisory WIP scheduling. The distributed controllers objective is to control the rate in each production stage in a way that satisfies the demand for final products while reducing WIP within the production system. The EA identifies those sets of parameters for which the fuzzy controller performs optimal with respect to WIP and backlog minimization. The proposed EA strategy is compared with known heuristically tuned distributed and supervised fuzzy control approaches. Extensive simulation results show that the EA strategy significantly improves the system’s performance.

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Correspondence to N. Tsourveloudis.

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Tsourveloudis, N., Doitsidis, L. & Ioannidis, S. Work-in-process scheduling by evolutionary tuned fuzzy controllers. Int J Adv Manuf Technol 34, 748–761 (2007).

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