An Exploratory Analysis of Two Iterative Linear Programming-Simulation Approaches for Production Planning

Production planning models that aim at determining optimal release schedules for production facilities face a fundamental circularity. In order to match supply to demand in an optimal manner, they must recognize the cycle time that elapses between material being released into the plant and its emergence as finished product. However, it is well known from queuing models that the mean cycle time increases nonlinearly with resource utilization, which is determined by the release schedule. To address this circularity, a number of authors have suggested algorithms which iterate between a linear programming model that determines releases for a set of flow time estimates, and a simulation model that evaluates the production realized from that release schedule. We present computational experiments examining the behavior of two such algorithms. We find that the convergence behavior of one is significantly more consistent than that of the other, and explore insights that may lead to improved algorithms.