Optimization ofWarehouse Operations with Genetic Algorithms

We present a complete, fully automatic solution based on genetic algorithms for theoptimization of discrete product placement and of order picking routes in a warehouse. The solutiontakes as input the warehouse structure and the list of orders and returns the optimized productplacement, which minimizes the sum of the order picking times. The order picking routes areoptimized mostly by genetic algorithms with multi-parent crossover operator, but for some casesalso permutations and local search methods can be used. The product placement is optimized byanother genetic algorithm, where the sum of the lengths of the optimized order picking routes isused as the cost of the given product placement. We present several ideas, which improve andaccelerate the optimization, as the proper number of parents in crossover, the caching procedure,multiple restart and order grouping. In the presented experiments, in comparison with the randomproduct placement and random product picking order, the optimization of order picking routesallowed the decrease of the total order picking times to 54%, optimization of product placement withthe basic version of the method allowed to reduce that time to 26% and optimization of productplacement with the methods with the improvements, as multiple restart and multi-parent crossoverto 21%.