A simple and effective hybrid genetic search for the job sequencing and tool switching problem

•A hybrid genetic algorithm with diversity management is proposed.•We introduce a secondary objective for breaking ties in local search.•Extensive experiments are conducted and confirm the method quality performance.•A new set of large sized instances is proposed to encourage future research. The jo...

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Vydáno v:Computers & operations research Ročník 127; s. 105153
Hlavní autoři: Mecler, Jordana, Subramanian, Anand, Vidal, Thibaut
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.03.2021
Pergamon Press Inc
Témata:
Algorithms
Combinatorial optimization
Hybrid genetic search
Job sequencing
Job shops
Metaheuristics
Operations research
Searching
Switches
Switching
Tool switches
Hybrid genetic search
Tool switches
Job sequencing
Combinatorial optimization
Metaheuristics
ISSN:0305-0548, 1873-765X, 0305-0548
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Shrnutí:•A hybrid genetic algorithm with diversity management is proposed.•We introduce a secondary objective for breaking ties in local search.•Extensive experiments are conducted and confirm the method quality performance.•A new set of large sized instances is proposed to encourage future research. The job Sequencing and tool Switching Problem (SSP) has been extensively studied in the field of operations research, due to its practical relevance and methodological interest. Given a machine that can load a limited amount of tools simultaneously and a number of jobs that require a subset of the available tools, the SSP seeks a job sequence that minimizes the number of tool switches in the machine. To solve this problem, we propose a simple and efficient hybrid genetic search based on a generic solution representation, a tailored decoding operator, efficient local searches and diversity management techniques. To guide the search, we introduce a secondary objective designed to break ties. These techniques allow to explore structurally different solutions and escape local optima. As shown in our computational experiments on classical benchmark instances, our algorithm significantly outperforms all previous approaches while remaining simple to apprehend and easy to implement. We finally report results on a new set of larger instances to stimulate future research and comparative analyses.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2020.105153