- Title
- Multiobjective Optimization of Energy-Efficient JOB-Shop Scheduling with Dynamic Reference Point-Based Fuzzy Relative Entropy
- Creator
- He, Lijun; Chiong, Raymond; Li, Wenfeng; Dhakal, Sandeep; Cao, Yulian; Zhang, Yu
- Relation
- IEEE Transactions on Industrial Informatics Vol. 18, Issue 1, p. 600-610
- Publisher Link
- http://dx.doi.org/10.1109/TII.2021.3056425
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2022
- Description
- 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.
- Subject
- energy-efficient job scheduling; fitness evaluation mechanism (FEM); fuzzy relative entropy (FRE); genetic algorithm (GA); mulitobjective optimization; sequence-dependent setup time (SDST); SDG 7; SDG 9; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1452923
- Identifier
- uon:44541
- Identifier
- ISSN:1551-3203
- Language
- eng
- Hits: 1244
- Visitors: 1243
- Downloads: 0
Collections
Goal 7: Ensure access to affordable, reliable, sustainable and modern energy