Open Access
ARTICLE
A Novel Collaborative Evolutionary Algorithm with Two-Population for Multi-Objective Flexible Job Shop Scheduling
Cuiyu Wang, Xinyu Li, Yiping Gao*
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology,
Wuhan, 430074, China
* Corresponding Author: Yiping Gao. Email:
(This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)
Computer Modeling in Engineering & Sciences 2023, 137(2), 1849-1870. https://doi.org/10.32604/cmes.2023.028098
Received 29 November 2022; Accepted 13 February 2023; Issue published 26 June 2023
Abstract
Job shop scheduling (JS) is an important technology for modern manufacturing. Flexible job shop scheduling (FJS)
is critical in JS, and it has been widely employed in many industries, including aerospace and energy. FJS enables
any machine from a certain set to handle an operation, and this is an NP-hard problem. Furthermore, due to the
requirements in real-world cases, multi-objective FJS is increasingly widespread, thus increasing the challenge of
solving the FJS problems. As a result, it is necessary to develop a novel method to address this challenge. To achieve
this goal, a novel collaborative evolutionary algorithm with two-population based on Pareto optimality is proposed
for FJS, which improves the solutions of FJS by interacting in each generation. In addition, several experimental
results have demonstrated that the proposed method is promising and effective for multi-objective FJS, which has
discovered some new Pareto solutions in the well-known benchmark problems, and some solutions can dominate
the solutions of some other methods.
Keywords
Cite This Article
APA Style
Wang, C., Li, X., Gao, Y. (2023). A novel collaborative evolutionary algorithm with two-population for multi-objective flexible job shop scheduling. Computer Modeling in Engineering & Sciences, 137(2), 1849-1870. https://doi.org/10.32604/cmes.2023.028098
Vancouver Style
Wang C, Li X, Gao Y. A novel collaborative evolutionary algorithm with two-population for multi-objective flexible job shop scheduling. Comput Model Eng Sci. 2023;137(2):1849-1870 https://doi.org/10.32604/cmes.2023.028098
IEEE Style
C. Wang, X. Li, and Y. Gao "A Novel Collaborative Evolutionary Algorithm with Two-Population for Multi-Objective Flexible Job Shop Scheduling," Comput. Model. Eng. Sci., vol. 137, no. 2, pp. 1849-1870. 2023. https://doi.org/10.32604/cmes.2023.028098