He, Lijun

Chiong, Raymond

Li, Wenfeng

Dhakal, Sandeep

Cao, Yulian

Zhang, Yu

2022

<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>

IEEE Transactions on Industrial Informatics, 18(1), p. 600-610

1941-0050

0278-0046

https://hdl.handle.net/1959.11/61371

en

Institute of Electrical and Electronics Engineers

Multiobjective Optimization of Energy-Efficient JOB-Shop Scheduling with Dynamic Reference Point-Based Fuzzy Relative Entropy

Journal Article

Publication