Perturbation analysis for production control and optimization of manufacturing systems

We use stochastic /uid models (SFM) to capture the operation of threshold-based production control policies in manufacturing systems without resorting to detailed discrete event models. By applying in4nitesimal perturbation analysis (IPA) to a SFM of a workcenter, we derive gradient estimators of throughput and bu7er over/ow metrics with respect to production control parameters. It is shown that these gradient estimators are unbiased and independent of distributional information of supply and service processes involved. In addition, based on the fact that they can be evaluated using data from the observed actual (discrete event) system, we use them as approximate gradient estimators in simple iterative schemes for adjusting thresholds (hedging points) on line seeking to optimize an objective function that trades o7 throughput and bu7er over/ow costs. ? 2004 Elsevier Ltd. All rights reserved.