Solving the Flexible Job Shop Scheduling Problem with Makespan Optimization by Using a Hybrid Taguchi-Genetic Algorithm

Abstract

Enterprises exist in a competitive manufacturing environment. To reduce production costs and effectively use production capacity to improve competitiveness, a hybrid production system is necessary. The flexible job shop (FJS) is a hybrid production system, and the FJS problem (FJSP) has drawn considerable attention in the past few decades. This paper examined the FJSP and, like previous studies, aimed to minimize the total order completion time (makespan). We developed a novel method that involves encoding feasible solutions in the genes of the initial chromosomes of a genetic algorithm (GA) and embedding the Taguchi method behind mating to increase the effectiveness of the GA. Two numerical experiments were conducted for evaluating the performance of the proposed algorithm relative to that of the Brandimarte MK1-MK10 benchmarks. The first experiment involved comparing the proposed algorithm and the traditional GA. The second experiment entailed comparing the proposed algorithm with those presented in previous studies. The results demonstrate that the proposed algorithm is superior to those reported in previous studies (except for that of Zhang et al.: the results in experiment MK7 were superior to those of Zhang, the results in experiments MK6 and MK10 were slightly inferior to those of Zhang, and the results were equivalent in other experiments) and effectively overcomes the encoding problem that occurs when a GA is used to solve the FJSP.