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

Title
Multiobjective Optimization of Energy-Efficient JOB-Shop Scheduling with Dynamic Reference Point-Based Fuzzy Relative Entropy
Publication Date
2022
Author(s)
He, Lijun
Chiong, Raymond
( author )
OrcID: https://orcid.org/0000-0002-8285-1903
Email: rchiong@une.edu.au
UNE Id une-id:rchiong
Li, Wenfeng
Dhakal, Sandeep
Cao, Yulian
Zhang, Yu
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
Institute of Electrical and Electronics Engineers
Place of publication
United States of America
DOI
10.1109/TII.2021.3056425
UNE publication id
une:1959.11/61371
Abstract

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.

Link
Citation
IEEE Transactions on Industrial Informatics, 18(1), p. 600-610
ISSN
1941-0050
0278-0046
Start page
600
End page
610

Files:

NameSizeformatDescriptionLink