Energy-Aware Scheduling of Two-Stage Flexible Flow Shop Based on Group Technology: Consideration of the Inconsistency of Process Differences

• Published in: International journal of computational intelligence systems Vol. 15; no. 1; pp. 1 - 20
• Main Authors: Zeng, Zhiqiang, Gao, Le, Hong, Zhiyong
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 10.08.2022; Springer Nature B.V; Springer
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Decoding; Energy consumption; Energy management; Energy-aware scheduling; Engineering; Flexible flow shop; Group technology; Mathematical Logic and Foundations; Mechatronics; Multiple objective analysis; Optimization; Production costs; Research Article; Robotics; Scheduling; Search algorithms; Energy-aware scheduling; Group technology; Flexible flow shop
• ISSN: 1875-6883; 1875-6891; 1875-6883
• DOI: 10.1007/s44196-022-00124-2

The diversity of consumer demand makes the manufacturing industry shift from the production mode of small variety and large batch to the production mode of multi-variety and small batch, which leads to the increase of production cost and the decrease of production efficiency, especially the increase of energy consumption. In this paper, the energy-aware scheduling problem of two-stage flexible flow shop based on group technology is studied, in which the process differences of jobs in the two stages are inconsistent. To solve the problem of unbalanced load of machines caused by group technology, this paper uses a dynamic splitting strategy to balance the load of machines on the basis of increasing the setup energy consumption as little as possible. Considering that the research problem is a multi-objective optimization problem, this study develops an improved multi-objective scatter search algorithm. According to the characteristics of the problem, the coding scheme, decoding scheme, diversity generation method and combination method are designed. The experimental results show that the group technology can effectively reduce energy consumption, and the dynamic split strategy can balance the load of machines effectively. Moreover, the results show the proposed method is better than the existing methods in terms of obtaining better solutions.