An Effective Cooperative Co-Evolutionary Algorithm for Distributed Flowshop Group Scheduling Problems

• Published in: IEEE transactions on cybernetics Vol. 52; no. 7; pp. 1 - 14
• Main Authors: Pan, Quan-Ke, Gao, Liang, Wang, Ling
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Automation; Collaboration; Computational modeling; Cooperation; Cooperative co-evolutionary algorithm (CCEA); distributed flowshop; Evolutionary algorithms; Genetic algorithms; Heuristic methods; Integer programming; Job shop scheduling; Job shops; Linear programming; makespan; Manufacturing; Mixed integer; Optimization; Particle swarm optimization; Processor scheduling; Scheduling
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2020.3041494

This article addresses a novel scheduling problem, a distributed flowshop group scheduling problem, which has important applications in modern manufacturing systems. The problem considers how to arrange a variety of jobs subject to group constraints at a number of identical manufacturing cellulars, each one with a flowshop structure, with the objective of minimizing makespan. We explore the problem-specific knowledge and present a mixed-integer linear programming model, a counterintuitive paradox, and two suites of accelerations to save computational efforts. Due to the complexity of the problem, we consider a decomposition strategy and propose a cooperative co-evolutionary algorithm (CCEA) with a novel collaboration model and a reinitialization scheme. A comprehensive and thorough computational and statistical campaign is carried out. The results show that the proposed collaboration model and reinitialization scheme are very effective. The proposed CCEA outperforms a number of metaheuristics adapted from closely related scheduling problems in the literature by a significantly considerable margin.