Pareto and Niche Genetic Algorithm for Storage Location Assignment Optimization Problem

Class-based storage and storage location assignment implementation decisions have significant impact on the required storage space and product picking efficiency in an automated warehouse. A multiobjective mathematical model was proposed for storage location assignment to capture the above. The rack stability and order picking frequency were incorporated based on the class strategy. A genetic algorithm with Pareto-optimization and niche technique was developed to solve the problem. The algorithm included two arithmetic operators: Pareto solution sets filter and niche technique besides selection, crossover and mutation operators. Computational experience with randomly generated data sets and an industrial case shows that the policies are more effective than class-based storage policy only, and enhance the operational efficiency of an automated storage/retrieval system, as well as a CIMS system. The improved genetic algorithm can be applied to handle large real life problems efficiently.