Abstract
AbstractMotivated by a real‐world problem in an automobile manufacturing firm, we study the multi‐level, multi‐stage lot‐sizing and scheduling problem with demand information updating. The objective is to determine the quantities and sequences of production on different production stages to minimize the total costs of production and inventory. We employ the martingale model for forecast evolution to model the evolving demand over time and build a mixed‐integer programing (MIP) model for the problem using a hybrid period approach (i.e., combining micro‐ and macro‐periods). To solve this NP‐hard problem, we propose three heuristic algorithms based on the idea of “relax‐and‐fix” within the rolling horizon framework. We conduct computational experiments to test the performance of the heuristic algorithms as well as compare the performance of the proposed heuristics with the benchmark procedure (truncated procedure for MIP) for both small‐ and large‐scale problem instances. The computational results show that Heuristic 1 performs the best among the three heuristic algorithms.
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