A Self-Adaptive Multiobjective Differential Evolution Algorithm for the Unrelated Parallel Batch Processing Machine Scheduling Problem

In this paper, the unrelated parallel batch processing machine (UPBPM) scheduling problem is addressed to minimize the total energy consumption (TEC) and makespan. Firstly, a mixed-integer line programming model (MILP) of the UPBPM scheduling problem is presented. Secondly, a self-adaptive multiobje...

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Bibliographic Details
Published in:Mathematical problems in engineering Vol. 2022; pp. 1 - 16
Main Author: Song, Cunli
Format: Journal Article
Language:English
Published: New York Hindawi 15.09.2022
John Wiley & Sons, Inc
Subjects:
Adaptive algorithms
Adaptive control
Batch processing
Control methods
Convergence
Electricity
Employment
Energy conservation
Energy consumption
Engineering
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Heuristic
Job shops
Literature reviews
Mixed integer
Mutation
Optimization
Parameters
Scheduling
ISSN:1024-123X, 1563-5147
Online Access:Get full text
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Summary:In this paper, the unrelated parallel batch processing machine (UPBPM) scheduling problem is addressed to minimize the total energy consumption (TEC) and makespan. Firstly, a mixed-integer line programming model (MILP) of the UPBPM scheduling problem is presented. Secondly, a self-adaptive multiobjective differential evolution (AMODE) algorithm is put forward. Since the parameter value can affect the performance of the algorithm greatly, an adaptive parameter control method is proposed according to the convergence index of the individual and the evolution degree of the population to improve the exploitation and exploration ability of the algorithm. Meanwhile, an adaptive mutation strategy is proposed to improve the algorithm’s convergence and the solutions’ diversity. Finally, to verify the effectiveness of the algorithm, comparative experiments are carried out on 20 instances with 5 different scales. Numerical comparisons indicate that the proposed method can achieve high comprehensive performance.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2022/5056356