Author(s) |
He, Lijun
Chiong, Raymond
Li, Wenfeng
Dhakal, Sandeep
Cao, Yulian
Zhang, Yu
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Publication Date |
2022
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Abstract |
<p>Energy-efficient production scheduling research has received much attention because of the massive energy consumption of the manufacturing process. In this article, we study an energy-efficient job-shop scheduling problem with sequence-dependent setup time, aiming to minimize the makespan, total tardiness and total energy consumption simultaneously. To effectively evaluate and select solutions for a multiobjective optimization problem of this nature, a novel fitness evaluation mechanism (FEM) based on fuzzy relative entropy (FRE) is developed. FRE coefficients are calculated and used to evaluate the solutions. A multiobjective optimization framework is proposed based on the FEM and an adaptive local search strategy. A hybrid multiobjective genetic algorithm is then incorporated into the proposed framework to solve the problem at hand. Extensive experiments carried out confirm that our algorithm outperforms five other well-known multiobjective algorithms in solving the problem.</p>
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Citation |
IEEE Transactions on Industrial Informatics, 18(1), p. 600-610
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ISSN |
1941-0050
0278-0046
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Link | |
Publisher |
Institute of Electrical and Electronics Engineers
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Title |
Multiobjective Optimization of Energy-Efficient JOB-Shop Scheduling with Dynamic Reference Point-Based Fuzzy Relative Entropy
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Type of document |
Journal Article
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Entity Type |
Publication
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