Applying particle swarm optimisation to dynamic lot sizing with batch ordering

This paper investigates the applicability of particle swarm optimisation (PSO) to the dynamic lot sizing problem with batch ordering. Backorders are allowed to account for discrepancies created by the batch ordering constraint. The PSO solution is compared with solutions generated using both a modified Silver-Meal (MSM) heuristic and a genetic algorithm (GA). A 23 factorial experiment is used to compare the various approaches and to examine the influence of three factors on their performance. The investigated factors include the demand pattern, the batch size, and the planning horizon. The comparisons are based on the relative frequency of obtaining the optimum solution and the percentage deviation from the optimum solution. In general, the PSO outperformed both the MSM and the GA by producing the lowest cost solution on almost all experimental runs. The planning horizon is the most significant factor affecting the performance of all heuristics. The MSM is affected by all investigated factors while the PSO is not affected by the batch size and the GA is not affected by the demand pattern. The PSO has a clear performance edge in dealing with seasonal demand patterns.