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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Vydané v:	SN computer science Ročník 4; číslo 5; s. 651
Hlavní autori:	Chaubey, Punit Kumar, Sundar, Shyam
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Singapore Springer Nature Singapore 01.09.2023 Springer Nature B.V
Predmet:	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
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Abstract	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.
AbstractList	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 NP-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. 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.
ArticleNumber	651
Author	Chaubey, Punit Kumar Sundar, Shyam
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Title	A Steady-State Genetic Algorithm for the Single Machine Scheduling Problem with Periodic Machine Availability
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