Simulation Optimization for the Stochastic Economic Lot Scheduling Problem

We study simulation optimization methods for the stochastic economic lot scheduling problem. In contrast to prior research, we focus on methods that treat this problem as a black box. Based on a large-scale numerical study, we compare approximate dynamic programming with a global search for parameters of simple control policies. We propose two value function approximation schemes based on linear combinations of piecewiseconstant functions as well as control policies that can be described by a small set of parameters. While approximate value iteration worked well for small problems with three products, it was clearly outperformed by the global policy search as soon as problem size increased. The most reliable choice in our study was a globally optimized fixed-cycle policy. An additional analysis of the response surface of model parameters on optimal average cost revealed that the cost effect of product diversity was negligible.