Solution of storage and retrieval problem with meta-heuristic algorithms

Efficient use of warehouse resources is one of the important factors that makes them more manageable and useful. It increases efficiency by ensuring a faster flow of product. In multi-dimensional warehouses with many restrictions such as weight, volume, product compatibility, etc., the storage and retrieval processes are complex optimization problems that need to be solved. Considering the number of constraints, the solution of storage and retrieval operations with traditional algorithms requires high effort. Meta-heuristic algorithms provide acceptable solutions in less time than traditional algorithms. For this reason, they are often preferred in solving complex optimization problems. In this thesis, the genetic algorithm, the artificial bee colony algorithm, which are the popular meta-heuristic methods, and a hybrid algorithm by adding the crossover and elitism processes in the genetic algorithm to the artificial bee colony were used in order to solve the storage and retrieval problem. The A-star algorithm was used to choose the optimum route between shelves, and a three-dimensional warehouse with operational constraints was designed to simulate the warehouse environment. In order to model the product flow in the warehouse, some of the orders containing different numbers of products were determined as storage demands and others as retrieval demands. The results show that meta-heuristic methods can quickly produce effective solutions to the storage and retrieval problem in a multidimensional warehouse with operational constraints.