A Steady-State Genetic Algorithm for the Single Machine Scheduling Problem with Periodic Machine Availability

This paper presents an evolutionary algorithm-based steady-state grouping genetic algorithm (SSGGA) for the single-machine scheduling problem with periodic machine availability (SinMSPMA problem) whose objective is to minimize the makespan. This problem is N P -hard which arises in several real prod...

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Veröffentlicht in:SN computer science Jg. 4; H. 5; S. 651
Hauptverfasser: Chaubey, Punit Kumar, Sundar, Shyam
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.09.2023
Springer Nature B.V
Schlagworte:
Availability
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Evolutionary algorithms
Genetic algorithms
Heuristic
Information Systems and Communication Service
Integer programming
Linear programming
Machinery
Original Research
Packing problem
Pattern Recognition and Graphics
Production scheduling
Research Trends in Computational Intelligence
Scheduling
Software Engineering/Programming and Operating Systems
Steady state
Vision
Production scheduling
Makespan
Steady-state genetic algorithm
Evolutionary algorithm
ISSN:2661-8907, 2662-995X, 2661-8907
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Zusammenfassung:This paper presents an evolutionary algorithm-based steady-state grouping genetic algorithm (SSGGA) for the single-machine scheduling problem with periodic machine availability (SinMSPMA problem) whose objective is to minimize the makespan. This problem is N P -hard which arises in several real production scenarios, where industries are giving importance of maintenance activities in their production scheduling systems due to not only improving the efficiency and safety of production, but also increasing the productivity. The SinMSPMA problem belongs to a class of grouping problems. Due to its grouping-aspect structure, the proposed SSGGA encodes each chromosome as a set of periods (groups) and relies on combining specialized genetic operators with a problem-specific repair operator in order to generate an offspring. On available benchmark instances, computational results of SSGGA indicate that SSGGA outperforms the best three approaches out of 19 existing approaches.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-023-02042-2