Jozef Kratica
This paper investigates the applicability of the improvement of simple genetic algorithm (SGA) method for solving the uncapacitated warehouse location problem (UWLP). Function for computing the item�s objective value is improved depending upon the number of established warehouses. It is efficiently implemented, giving excellent results in specified environment. Mutation rate is also changed and now depends on test problem size. Duplicate item strings in population are discarded, which makes the population more diversified.
Overall performance of implementation is finally tuned by caching SGA. Through caching technique relatively smaller profit in performance is obtained when compared to previous techniques, but it is a general technique, and can be directly applied to other problems, not only to UWLP.
Computational experience with given problem examples indicates that, when compared to code in [1], the implementation of the modified algorithm is faster several times. For large size test problems the increase of computational speed may even exceed factor 10, and quality of obtained solutions is also significantly better.
Keywords: Genetic algorithms, Uncapacitated warehouse problem
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