Journal
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
Volume 30, Issue 6, Pages 616-640Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/0951192X.2016.1187301
Keywords
decomposition; job shop scheduling; multi-objective optimisation; multi-objective evolutionary algorithm based on decomposition
Categories
Funding
- National Natural Science Foundation of China [51365030]
- General and Special Program of the Postdoctoral Science Foundation of China
- Science Foundation for Distinguished Youth Scholars of Lanzhou University of Technology, Lanzhou Science Bureau project [2012M521802, 2013T60889, J201405, 2013-4-64]
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In this article, an improved multi-objective evolutionary algorithm, which is based on decomposition (IMOEA/D) for multiobjective job shop scheduling problem, is proposed to solve multiple objectives job shop scheduling problems. Three minimisation objectives - the maximum completion time (makespan), the total flow time and the tardiness time are considered simultaneously. In the proposed algorithm, several prior rules are presented to construct the initial population with a high level of quality. Meanwhile, according to the contribution of each operator to the external archive, an adaptive mechanism is adopted to select corresponding operators to generate new solutions, which can accelerate convergence speed. Simulation results on the standard test instances show that IMOEA/D has a better convergence performance compared with multi-objective evolutionary algorithms based on Pareto dominance.
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