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Solving large unconstrained multilevel lot-sizing problems using a hybrid genetic algorithm

delete2010-11-14
delete73
PRE
AI
N
Nico Dellaert *
J
Jully Jeunet
DOI：10.1080/002075400189031delete
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Abstract

Abstract

En 中文
We develop a genetic algorithm (GA) to solve the uncapacitated multilevel lot-sizing problem in material requirements planning (MRP) systems. The major drawback of existing approaches is undoubtedly their inability to provide cost-efficient 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.
Keywords:
REQUIREMENTS PLANNING SYSTEMS
PERFORMANCE
HEURISTICS
INVENTORY
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Journal

International Journal of Production Research cover
International Journal of Production Research
IF:
7.3
Papers:
1.1W
Citations:
3.7W

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No organization information available