Adaptive genetic algorithm for lot-sizing problem with self-adjustment operation rate

This paper presents a new adaptive genetic algorithm (GA) to escape local optimum solutions of the traditional lot-sizing rules. In this GA, the timing of replenishment is encoded as a string of binary digits (a chromosome). Each gene in that chromosome stands for a period. Standard GA operators are...

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Bibliographic Details
Published in:International journal of production economics 2005-11, Vol.98 (2), p.129-135
Main Authors: Hop, Nguyen Van, Tabucanon, Mario T.
Format: Article
Language:English
Subjects:
Algorithms
Chromosomes
Genetic algorithm
Genetic algorithms
Lot-sizing
Self-adjustment operation rate
Studies
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Summary:This paper presents a new adaptive genetic algorithm (GA) to escape local optimum solutions of the traditional lot-sizing rules. In this GA, the timing of replenishment is encoded as a string of binary digits (a chromosome). Each gene in that chromosome stands for a period. Standard GA operators are used to generate new populations. These populations are evaluated by a fitness function using the replenishment scheme of solution based on the total cost. Through this evaluation, the rates of GA operators for the next generation are automatically adjusted based on the rate of survivor offsprings, which are generated by corresponding operators. The oriented search procedure using these self-adjustment rates of operator schemes can give faster and better solutions. Some experimental results confirm the theoretical judgment.
ISSN:0925-5273
1873-7579
DOI:10.1016/j.ijpe.2004.05.016