A New Multi-Objective Hybrid Flow Shop Scheduling Method to Fully Utilize the Residual Forging Heat

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Cheng, Qiang, Liu, Chenfei, Chu, Hongyan, Liu, Zhifeng, Zhang, Wei, Pan, Junjie
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Billets; Completion time; Constraint modelling; Energy consumption; Energy efficiency; Energy saving scheduling; Evolutionary algorithms; Forging; Forging planning; Furnaces; Heat; Heat treatment; Heating furnaces; Heating systems; Job shop scheduling; Job shops; Multi-objective optimization; Multiple objective analysis; Optimization; Production scheduling; Scheduling; Temperature constraint
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3017239

This paper aims to solve the problem of high energy consumption in forging production through energy-saving scheduling. By analyzing the flow shop characteristics of a forging workshop, an energy-efficient hybrid flow shop scheduling problem with forging tempering (EEHFSP-FT) is proposed. An energy-efficient scheduling model is established to simultaneously minimize both the completion time and energy consumption. In the scheduling model, constraints such as heating furnace capacity, required forging temperature, and required quenching temperature are taken into consideration. An energy-saving strategy of heat treatment with residual forging heat is adopted to address the problem of energy underutilization after forging. In order to use multi-objective optimization algorithms to solve the scheduling problems of charging and machine selection in forging production, encoding and decoding rules and evolutionary search strategies are designed. Finally, a case study on the flow shop of an automated forging center is analyzed. The validity of the proposed model is demonstrated by testing cases of different scales in conjunction with three different evolutionary algorithms. By analyzing the performance of the three algorithms, the algorithm suitable for solving the proposed model is determined.