Scatter Search Based Particle Swarm Optimization Algorithm for Earliness/Tardiness Flowshop Scheduling with Uncertainty

• Published in: International journal of automation and computing Vol. 13; no. 3; pp. 285 - 295
• Main Authors: Geng, Jia-Can, Cui, Zhe, Gu, Xing-Sheng
• Format: Journal Article
• Language: English
• Published: Beijing Institute of Automation, Chinese Academy of Sciences 01.06.2016; Springer Nature B.V
• Subjects: Algorithms; CAE) and Design; Computer Applications; Computer simulation; Computer-Aided Engineering (CAD; Control; Engineering; Evolutionary computation; Job shops; Lateness; Mechatronics; Optimization; Particle swarm optimization; Performance enhancement; Production scheduling; PSO算法; Research Article; Robotics; Scattering; Scheduling; Searching; Windows; Windows (intervals); 不确定性; 分散; 提前/拖期; 搜索; 流水车间调度; 粒子群优化算法; scheduling; scatter search (SS); fuzzy modeling; particle swarm optimization (PSO); Earliness/tardiness (E/T)
• ISSN: 1476-8186; 2153-182X; 1751-8520; 2153-1838
• DOI: 10.1007/s11633-016-0964-8

Considering the imprecise nature of the data in real-world problems, the earliness/tardiness （E/T） fiowshop scheduling problem with uncertain processing time and distinct due windows is concerned in this paper. A fuzzy scheduling model is established and then transformed into a deterministic one by employing the method of maximizing the membership function of middle value. Moreover, an effective scatter search based particle swarm optimization （SSPSO） algorithm is proposed to minimize the sum of total earliness and tardiness penalties. The proposed SSPSO algorithm incorporates the scatter search （SS） algorithm into the frame of particle swarm optimization （PSO） algorithm and gives full play to their characteristics of fast convergence and high diversity. Besides, a differential evolution （DE） scheme is used to generate solutions in the SS. In addition, the dynamic update strategy and critical conditions are adopted to improve the performance of SSPSO. The simulation results indicate the superiority of SSPSO in terms of effectiveness and efficiency.