A Genetic Algorithm for Multicriteria Inventory Classification
One of application areas of the genetic algorithms is parameter optimization. This paper addresses the problem of optimizing a set of parameters that represent the weights of criteria, where the sum of all weights is 1. A chromosome represents the values of the weights, possibly along with some cut-off points. A new crossover operation, called continuous uniform crossover, is proposed, such that it produces valid chromosomes given that the parent chromosomes are valid. The new crossover technique is applied to the problem of multicriteria inventory classification. The results are compared with the classical inventory classification technique using Analytical Hierarchy Process.
KeywordsGenetic Algorithm Analytic Hierarchy Process Weight Vector Crossover Operation Pairwise Comparison Matrix
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- D.E. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning, Reading, Massachusetts, Addison-Wesley, 1989.Google Scholar