The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems
Abstract
Traditionally, assignment and scheduling decisions are made separately at different levels of the production management framework. The combining of such decisions presents additional complexity and new problems. We present two new approaches to solve jointly the assignment and job-shop scheduling problems (with total or partial flexibility). The first one is the approach by localization (AL). It makes it possible to solve the problem of resource allocation and build an ideal assignment model (assignments schemata). The second one is an evolutionary approach controlled by the assignment model (generated by the first approach). In such an approach, we apply advanced genetic manipulations in order to enhance the solution quality. We also explain some of the practical and theoretical considerations in the construction of a more robust encoding that will enable us to solve the flexible job-shop problem by applying the genetic algorithms (GAs). Two examples are presented to show the efficiency of the two suggested methodologies.
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Cites
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,010
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
Genetic Algorithms + Data Structures = Evolution Programs
Michalewicz, Z. (1992) · Artificial Intelligence · doi:10.1007/978-3-662-02830-8 · Cited by 11,553
Introduction to Operations Research
Leimkuhler, F. F. (1968) · Technometrics · doi:10.1080/00401706.1968.10490578 · Cited by 3,405
An Algorithm for Solving the Job-Shop Problem
Carlier, J., & Pinson, É. (1989) · Management Science · doi:10.1287/mnsc.35.2.164 · Cited by 824
Introduction to Operations Research
(1968) · Mathematics in Science and Engineering/Mathematics in science and engineering · doi:10.1016/s0076-5392(08)x6025-4 · Cited by 793
Constraint-directed search : a case study of job-shop scheduling
Fox, M. S. (2018) · doi:10.1184/r1/6552371 · Cited by 526
Genetic algorithms and tabu search: Hybrids for optimization
Glover, F., Kelly, J. P., & Laguna, M. (1995) · Computers & Operations Research · doi:10.1016/0305-0548(93)e0023-m · Cited by 306
Cited by
Comparison of Three Evolutionary Algorithms: GA, PSO, and DE
Kachitvichyanukul, V. (2012) · Industrial Engineering and Management Systems · doi:10.7232/iems.2012.11.3.215 · Cited by 355
Metadata
- Authors
- Kacem, I., Hammadi, S., & Borne, P.
- Venue
- IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)
- Published
- 2002-02
- Volume
- 32
- Issue
- 1
- Pages
- 1-13
- ISSN
- 1094-6977 (ISSN-L)
- OpenAlex
- W2166900052
- Cited by
- 788
- References
- 8
- FWCI
- 36.03
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kacem, I., Hammadi, S., & Borne, P. (2002). Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews). https://doi.org/10.1109/tsmcc.2002.1009117
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