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

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,...

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Vydáno v:IEEE transactions on cybernetics Ročník 52; číslo 7; s. 1 - 14
Hlavní autoři: Pan, Quan-Ke, Gao, Liang, Wang, Ling
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2020.3041494