Simulation-based optimization methods for setting production planning parameters

This paper refers to a hierarchical production planning system in a make-to-order environment. A challenging task in this context is to determine good production parameter settings in order to benefit from established planning methods. We present a framework for hierarchical production planning which we use to identify good settings for three planning parameters, namely planned leadtimes, safety stock, and lotsizes. Within a discrete-event simulation which mimics the production system we use a mathematical optimization model for replicating the decision problem. This mathematical model is solved to optimality using a standard optimization engine. We use data referring to four different demand market situations in order to derive general statements concerning the quality and sensitivity of the three analyzed planning parameters. For exploring the parameter space we follow the concept of simulation-based optimization. We compare the performance of six different optimization methods to a kind of systematic enumeration of parameter combinations. We show that among these a search procedure based on the idea of Variable Neighborhood Search (VNS) leads to the best results in this context. (C) 2013 Elsevier B.V. All rights reserved.