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A cooperative coevolutionary algorithm with problem-specific knowledge for energy-efficient scheduling in seru system
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DOI：10.1016/j.knosys.2023.110663.png)
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With the increasingly prominent issues about the global environment, green manufacturing has become a research hotspot. To cope with the turbulent market environment, seru production system (SPS) as an innovative manufacturing mode has attracted much attention, yet energy consumption is still rarely considered in SPS. This paper addresses an energy-efficient scheduling problem in seru system (EESPSS) with the minimization of energy consumption and makespan simultaneously. It contains three coupled subproblems, i.e., worker-seru assignment, batch-seru assignment, and worker -task assignment. To tackle the problem effectively, we build a mathematical model and design a cooperative coevolutionary algorithm (CCA) with three phases: hybrid initialization, multi-population cooperation exploration with feedback, and knowledge-guided greedy search. Firstly, a knowledge -based rule is designed to produce high-quality initial solutions. Secondly, multi-population cooperation is proposed to achieve subproblem co-optimization. Specifically, two populations are constructed for adjusting worker-seru assignment and batch-seru assignment and an elite population is applied for information sharing by individual migration. Moreover, to enhance algorithm search capability, a feedback strategy is designed for operator selection and population size adjustment. Thirdly, to exploit the nondominated solutions effectively, several properties are derived as the problem-specific knowledge to design a greedy search for the batch-seru assignment and worker-task assignment. Numerical tests and statistical comparisons are carried out, which demonstrate the effectiveness of the specific designs of the CCA and its superiority to the existing algorithms in solving the EESPSS. & COPY; 2023 Elsevier B.V. All rights reserved.
Keyword:
Energy -efficient scheduling
Seru system
Multi -population cooperation
Feedback strategy
Knowledge -guided greedy search
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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