A hybrid Jaya algorithm for solving flexible job shop scheduling problem considering multiple critical paths. (July 2021)

• Record Type: Journal Article
• Authors: Fan, Jiaxin; Shen, Weiming; Gao, Liang; Zhang, Chunjiang; Zhang, Ze
• Is Part Of: Journal of manufacturing systems. Volume 60(2021)
• Page Start: 298
• Page End: 311
• Publication Date: 2021-07
• Subjects: Flexible job shop scheduling -- Hybrid Jaya algorithm -- Tabu search -- Multiple critical paths -- Neighborhood structures; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2021.05.018
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Two operators are designed for FJSP according to the basic idea of Jaya algorithm. Three approaches are proposed for the multiple critical paths issue. Best overall performance on BCdata benchmark. Good optimality and stability shown by benchmarks and engineering problems. Abstract: As an extension of the classical job shop scheduling problem, flexible job shop scheduling problem (FJSP) is considered as a challenge in manufacturing systems for its complexity and flexibility. Meta-heuristic algorithms are shown effective in solving FJSP. However, the multiple critical paths issue, which has not been formally discussed in the existing literature, is discovered to be a primary obstacle for further optimization by meta-heuristics. In this paper, a hybrid Jaya algorithm integrated with Tabu search is proposed to solve FJSP for makespan minimization. Two Jaya operators are designed to improve solutions under a two-vector encoding scheme. During the local search phase, three approaches are proposed to deal with multiple critical paths and have been evaluated by experimental study and qualitative analyses. An incremental parameter setting strategy and a makespan estimation method are employed to speed up the searching process. The proposed algorithm is compared with several state-of-the-art algorithms on three well-known FJSP benchmark sets. Extensive experimental results suggest its superiority in both optimality and stability. Additionally, a real world scheduling problem, including six instances with different scales, is applied to further prove its ability in handling large-scale scheduling problems.