Energy-efficient flexible flow shop scheduling with worker flexibility

• Published in: Expert systems with applications Vol. 141; p. 112902
• Main Authors: Gong, Guiliang, Chiong, Raymond, Deng, Qianwang, Han, Wenwu, Zhang, Like, Lin, Wenhui, Li, Kexin
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.03.2020; Elsevier BV
• Subjects: Benchmarks; Energy consumption; Energy efficiency; Evolutionary algorithms; Flexibility; Flexible flow shop scheduling; Green production; Human factors; Hybrid evolutionary algorithm; Job shop scheduling; Job shops; Multi-objective optimization; Green production; Multi-objective optimization; Hybrid evolutionary algorithm; Flexible flow shop scheduling; Human factors
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2019.112902

•Energy-efficient flexible flow shop scheduling with worker flexibility is studied.•Green production, processing time and human related objectives are considered.•A hybrid evolutionary algorithm (HEA) is proposed to solve the problem.•A total of 54 benchmarks are used to test the performance of the HEA.•Results show that the HEA is superior in terms of solution quality and efficiency. The classical flexible flow shop scheduling problem (FFSP) only considers machine flexibility. Thus far, the relevant literature has not studied FFSPs with worker flexibility, which is widely seen in practical manufacturing systems. Worker flexibility may greatly affect production efficiency and productivity. Furthermore, with the increase of environmental pollution and energy consumption, manufacturers require innovative methods to improve energy efficiency. In this paper, we propose an energy-efficient FFSP with worker flexibility (EFFSPW), in which the flexibility of machines and workers as well as the processing time, energy consumption and worker cost related factors are considered simultaneously. A hybrid evolutionary algorithm (HEA) is then presented to solve the proposed EFFSPW, where some effective operators and a new variable neighborhood search approach are designed. Comprehensive experiments including 54 benchmark instances of the EFFSPW are carried out, and Taguchi analysis is used to determine the best combination of key parameters for the HEA. Experimental results show that the proposed HEA can obtain better solutions for most of these benchmark instances compared to two other well-known algorithms, demonstrating its superior performance in terms of both solution quality and computational efficiency.