Self-adaptive memetic algorithms for multi-objective single machine learning-effect scheduling problems with release times

This paper proposes a single machine scheduling problem with learning-effect and release times by considering two objectives requiring minimization of makespan and total tardiness, simultaneously. Due to the NP-hardness of this problem, two memetic algorithms with meme variants are presented for sol...

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Vydané v:Flexible services and manufacturing journal Ročník 34; číslo 3; s. 748 - 784
Hlavný autor: Deliktaş, Derya
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.09.2022
Springer Nature B.V
Predmet:
Adaptive algorithms
Algorithms
Engineering
Machine learning
Machines
Manufacturing
Operations Management
Operations Research/Decision Theory
Processes
Scheduling
Memetic algorithms
Hyper-heuristics
Learning effect
Single machine scheduling
Scalarization methods
ISSN:1936-6582, 1936-6590
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Shrnutí:This paper proposes a single machine scheduling problem with learning-effect and release times by considering two objectives requiring minimization of makespan and total tardiness, simultaneously. Due to the NP-hardness of this problem, two memetic algorithms with meme variants are presented for solving the bi-objective problem and applied by combining three different scalarization methods, including weighted sum, conic, and tchebycheff. The performance of all memetic algorithms with the meme is investigated across randomly generated twenty-seven test problems ranging from ‘small’ to ‘large’ size. The experimental results indicate that the Multimeme Memetic Algorithm using the tchebycheff outperforms the other algorithms producing the best-known results for almost all bi-objective single machine scheduling instances with learning-effects. All algorithms perform effectively in solving large-sized problems with up to 200 jobs.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1936-6582
1936-6590
DOI:10.1007/s10696-021-09434-7