Evaluation of joint replenishment lot-sizing procedures in rolling horizon planning systems

Joint replenishment problems are commonly encountered in purchasing, manufacturing, and transportation planning. Literature evaluates various algorithmic approaches for solving the joint replenishment problem in a static environment, but their relative performance in a dynamic rolling horizon system is unknown. This research experimentally evaluates nine joint replenishment lot-sizing heuristics and policy design variables when implemented in a dynamic rolling schedule environment. The findings indicate that a single algorithm does excel on both dimensions of schedule cost and stability. Hence, management must trade off these two performance metrics when choosing the best approach for their specific problem. Generally, metaheuristics provide the best cost replenishment schedule, but forward pass based heuristics yield the most stable schedules. The results also indicate that the choice of lot-sizing heuristic is the major cost performance driver in rolling planning systems, with policy design variables (frozen interval and planning horizon length) having little impact. While the simulated annealing heuristic of Robinson et al. (2007a) is the most effective solution procedure for the static joint replenishment problem, the perturbation metaheuristic of Boctor et al. (2004) produces lower schedule costs and greater stability in rolling schedule environments.