Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms

Hao-Chin Chang () and Tung-Kuan Liu ()
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Hao-Chin Chang: National Kaohsiung First University of Science and Technology
Tung-Kuan Liu: National Kaohsiung First University of Science and Technology

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 8, No 14, 1973-1986

Abstract: Abstract In contrast to traditional job-shop scheduling problems, various complex constraints must be considered in distributed manufacturing environments; therefore, developing a novel scheduling solution is necessary. This paper proposes a hybrid genetic algorithm (HGA) for solving the distributed and flexible job-shop scheduling problem (DFJSP). Compared with previous studies on HGAs, the HGA approach proposed in this study uses the Taguchi method to optimize the parameters of a genetic algorithm (GA). Furthermore, a novel encoding mechanism is proposed to solve invalid job assignments, where a GA is employed to solve complex flexible job-shop scheduling problems (FJSPs). In addition, various crossover and mutation operators are adopted for increasing the probability of finding the optimal solution and diversity of chromosomes and for refining a makespan solution. To evaluate the performance of the proposed approach, three classic DFJSP benchmarks and three virtual DFJSPs were adapted from classical FJSP benchmarks. The experimental results indicate that the proposed approach is considerably robust, outperforming previous algorithms after 50 runs.

Keywords: Distributed and flexible job-shop scheduling problem; Flexible job-shop scheduling problem; Encoding; Genetic algorithms; Taguchi method (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10845-015-1084-y

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