Genetic Algorithm Based Storage and Retrieval System Optimization Considering Operational Constraints in a Multidimensional Warehouse

Abstract

Efficient use of warehouse resources is an important issue that makes them more manageable and useful, also helps product flow faster. In multidimensional warehouses with many constraints such as weight, volume, product compatibility, etc., storage and retrieval processes are complex optimization problems that need to be solved. Considering the number of constraints, the solution to the storage and retrieval problems with traditional algorithms take a long time. Meta-heuristic algorithms are frequently used in the solution of many complex optimization problems as they can provide acceptable solutions in a short time. In this study, the Genetic algorithm which is one of the popular meta-heuristic methods was used to solve this problem, and the A-star algorithm was used to travel the shortest path between the shelves. A three-dimensional warehouse with operational constraints was designed. Storage and retrieval orders containing a different number of pallets were produced randomly to perform warehouse product flow, and some of these orders were assumed as storage requests and the remainder were retrieval requests. Results show that the proposed approach is capable of finding effective solutions for storage and retrieval problems with operational constraints in a short time.

Keywords

• A-star algorithm
• Genetic algorithm
• Operational constraints
• Storage and retrieval system

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