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Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems with additional constraints
- Source :
- Operations Research Perspectives, Vol 8, Iss, Pp 100180-(2021), Operations Research Perspectives, 8
- Publication Year :
- 2021
- Publisher :
- Elsevier, 2021.
-
Abstract
- Automatic design of stochastic local search algorithms has been shown to be very effective in generating algorithms for the permutation flowshop problem for the most studied objectives including makespan, flowtime and total tardiness. The automatic design system uses a configuration tool to combine algorithmic components following a set of rules defined as a context-free grammar. In this paper we use the same system to tackle two of the most studied additional constraints for these objectives: sequence dependent setup times and no-idle constraint. Additional components have been added to adapt the system to the new problems while keeping intact the grammar structure and the experimental setup. The experiments show that the generated algorithms outperform the state of the art in each case.
- Subjects :
- Statistics and Probability
Control and Optimization
Combinatorial optimization
Computer science
Strategy and Management
Tardiness
0211 other engineering and technologies
02 engineering and technology
Management Science and Operations Research
Set (abstract data type)
03 medical and health sciences
Permutation
0302 clinical medicine
ddc:330
QA1-939
Local search (optimization)
No-idle
Structure (mathematical logic)
Automatic algorithm design
021103 operations research
Job shop scheduling
business.industry
Stochastic local search algorithms
Constraint (information theory)
Sequence dependent setup times
030221 ophthalmology & optometry
State (computer science)
Permutation flowshop problem
business
Recherche opérationnelle
Algorithm
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 22147160
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- Operations Research Perspectives
- Accession number :
- edsair.doi.dedup.....ef7b24fe2ebc71d6f001d75fb93ea194