A New Sustainable Scheduling Method for Hybrid Flow-Shop Subject to the Characteristics of Parallel Machines

• Published in: IEEE access Vol. 8; pp. 79998 - 80009
• Main Authors: Kong, Lin, Wang, Liming, Li, Fangyi, Wang, Geng, Fu, Yan, Liu, Jing
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: characteristics of parallel machines; Energy consumption; Energy conversion efficiency; Genetic algorithms; Green manufacturing; hybrid flow-shop scheduling; improved genetic algorithm; Job shop scheduling; Job shops; Machine shops; Optimization; Parallel machines; Production scheduling; Scheduling; Sustainability
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2982570

Sustainable production for hybrid flow shop scheduling problem (HFSP) has attracted growing attention due to the environment and economy pressure in industry. As the major source of energy-consumption and cost, the selection of parallel machines for various jobs quite affect the sustainability in HFSP. However, the characteristic of parallel machines in job shops have not been addressed in current research, which hinder its practical application. Thus, in this work a novel sustainable HFSP model considering the characteristics of the machines is proposed through evaluation of the power, production efficiency and cost of the parallel machines. To solve this model, an improved genetic algorithm is developed, in which the matching distance between the parallel machines and the weights of the optimization objectives is introduced and integrated into iterations to accelerate the convergence. Finally, a case study is adopted to verify the proposed model and algorithm. Based on the optimization results, three kinds of typical scheduling modes, i.e., efficiency, energy-saving, and economic, are put forward to provide guidance for the sustainable production of hybrid flow-shop scheduling.