The Impact of Approximate Evaluation on the Performance of Search Algorithms for Warehouse Scheduling

The Coors warehouse scheduling problem involves nding 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 speciically for this application. We also tested a hybrid of the genetic algorithm and local search approaches by initializing the search using the domain-speciic heuristic. This hybrid genetic algorithm was able to nd the best solutions when evaluated by the high-resolution simulation. Finally, we consider the eeect of using the high-resolution simulation to lter a set of solutions found by the diierent approaches.