A Hybrid Multiobjective Evolutionary Approach for Flexible Job-Shop Scheduling Problems. (15th August 2012)

• Record Type: Journal Article
• Authors: Xiong, Jian; Tan, Xu; Yang, Ke-wei; Xing, Li-ning; Chen, Ying-wu
• Other Names: Elias-Zuniga Alex Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2012(2012)
• Publication Date: 2012-08-15
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2012/478981
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : This paper addresses multiobjective flexible job-shop scheduling problem (FJSP) with three simultaneously considered objectives: minimizing makespan, minimizing total workload, and minimizing maximal workload. A hybrid multiobjective evolutionary approach (H-MOEA) is developed to solve the problem. According to the characteristic of FJSP, a modified crowding distance measure is introduced to maintain the diversity of individuals. In the proposed H-MOEA, well-designed chromosome representation and genetic operators are developed for FJSP. Moreover, a local search procedure based on critical path theory is incorporated in H-MOEA to improve the convergence ability of the algorithm. Experiment results on several well-known benchmark instances demonstrate the efficiency and stability of the proposed algorithm. The comparison with other recently published approaches validates that H-MOEA can obtain Pareto-optimal solutions with better quality and/or diversity.