Energy-Efficient Scheduling of Distributed Flow Shop With Heterogeneous Factories: A Real-World Case From Automobile Industry in China

• Published in: IEEE transactions on industrial informatics Vol. 17; no. 10; pp. 6687 - 6696
• Main Authors: Lu, Chao, Gao, Liang, Yi, Jin, Li, Xinyu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automobile industry; Camshafts; Criteria; Distributed shop scheduling; Energy consumption; energy-efficient scheduling; Factories; Greedy algorithms; heterogeneous factories; Industrial plants; Informatics; iterated greedy (IG) algorithm; Job shop scheduling; Job shops; Machine shops; Machining; Mathematical model; multiobjective optimization; Multiple objective analysis; Optimization; Optimization algorithms; Permutations; Production facilities; Scheduling; China
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.3043734

Distributed flow shop scheduling of a camshaft machining is an important optimization problem in the automobile industry. The previous studies on distributed flow shop scheduling problem mainly emphasized homogeneous factories (shop types are identical from factory to factory) and economic criterion (e.g., makespan and tardiness). Nevertheless, heterogeneous factories (shop types are varied in different factories) and environment criterion (e.g., energy consumption and carbon emission) are inevitable because of the requirement of practical production and life. In this article, we address this energy-efficient scheduling of distributed flow shop with heterogeneous factories for the first time, where contains permutation and hybrid flow shops. First, a new mathematical model of this problem with objectives of minimization makespan and total energy consumption is formulated. Then, a hybrid multiobjective optimization algorithm, which integrates the iterated greedy (IG) and an efficient local search, is designed to provide a set of tradeoff solutions for this problem. Furthermore, the parameter setting of the proposed algorithm is calibrated by using a Taguchi approach of design-of-experiment. Finally, to verify the effectiveness of the proposed algorithm, it is compared against other well-known multiobjective optimization algorithms including MOEA/D, NSGA-II, MMOIG, SPEA2, AdaW, and MO-LR in an automobile plant of China. Experimental results demonstrate that the proposed algorithm outperforms these six state-of-the-art multiobjective optimization algorithms in this real-world instance.