A comparative study between a genetic algorithm and a simulated annealing algorithm for solving the order batching problem

Abstract: Optimizing warehouse automation requires finding efficient routes for pickingup items. Dividing the orders into batches is a realistic requirement for warehouses to have. This problem, known as the order batching problem, is an NP-hard problem. This thesis implements and compares two meta-heuristics to the order batching problem, simulated annealing (SA) and a genetic algorithm(GA). SA was found to perform equal to or better than GA on all occasions in terms of minimizing traveling distance. The algorithms were tested on 6 different warehouses with various layouts. The algorithms performed similarly on the smallest problem size, but in the largest problem size SA managed to find 17.1 % shorter solutions than GA. SA tended to find shorter solutions in a smaller amount of time as well.