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A genetic algorithm to solve the general multi-level lot-sizing problem with time-varying costs

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Date
2000
Dewey
Intelligence artificielle
Sujet
genetic algorithm
Journal issue
International Journal of Production Research
Volume
38
Number
5
Publication date
2000
Article pages
241-257
Publisher
Taylor & Francis
URI
https://basepub.dauphine.fr/handle/123456789/2667
Collections
  • LAMSADE : Publications
Metadata
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Author
Jonard, Nicolas
Dellaert, Nico
Jeunet, Jully
Type
Article accepté pour publication ou publié
Abstract (EN)
We develop a genetic algorithm (GA) to solve the uncapacitated multilevel lotsizing problem in material requirements planning (MRP) systems. The major drawback of existing approaches is undoubtedly their inability to provide costefficient solutions in a reasonable computation time for realistic size problems involving general product structures. By contrast, the proposed GA can easily handle large product structures (more than 500 items) with numerous common parts, a problem type for which standard optimization software memory becomes rapidly insufficient. Based upon several hybrid operators and an original way to build up the initial population, the resultant GA provides in a moderate execution time high cost-effectiveness solutions compared with other techniques, in the extensive tests we performed.

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