Journal
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 44, Issue 1, Pages 43-76Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/13528160500245772
Keywords
fuzzy goal-programming model; machine-tool selection; operation allocation; FMS; simulated annealing
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Fuzzy set theory has been widely accepted in modelling of some of the vague phenomena and relationships that are non-stochastic in nature. The problem of machine-tool selection and operation allocations in a flexible manufacturing system usually involves parameters that are non-deterministic and imprecise in nature. This paper adopts a fuzzy goal-programming model having multiple conflicting objectives and constraints pertaining to the machine-tool selection and operation allocation problem, and a new random search optimization methodology termed Quick Converging Simulated Annealing (QCSA) is being used to resolve the underlying issues. The main feature of the proposed QCSA algorithm is that it outperforms genetic algorithm and simulated annealing approaches as far as convergence to the near optimal solution is concerned. Moreover, it is also capable of eluding local optima. Extensive experiments are performed on a problem involving real-life complexities, and some of the computational results are reported to validate the efficacy of the proposed algorithm.
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