The impact of approximate evaluation on the performance of search algorithms for warehouse scheduling

The Coors warehouse scheduling problem involves finding a permutation of customer orders that minimizes the average time that customers’ orders spend at the loading docks while at the same time minimizing the running average inventory. Search-based solutions require fast objective functions. Thus, a fast low-resolution simulation is used as an objective function. A slower high-resolution simulation is used to validate solutions. We compare the performance of a constructive scheduling algorithm to a genetic algorithm and local search approach. The constructive algorithm is based on a heuristic built specifically for this application. We also tested a hybrid of the genetic algorithm and local search approaches by initializing the search using the domain-specific heuristic. This hybrid genetic algorithm was able too find the best solutions when evaluated by the high-resolution simulation. Finally, we consider the effect of using the high-resolution simulation to filter a set of solutions found by the different approaches.