A unified view of the IPA, SF, and LR gradient estimation techniques
Management Science
Mathematics of Operations Research
The Sample Average Approximation Method for Stochastic Discrete Optimization
SIAM Journal on Optimization
Several Results on the Design of Queueing Systems
Operations Research
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
A java library for simulating contact centers
WSC '05 Proceedings of the 37th conference on Winter simulation
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We study the problem of approximating a subgradient of a convex (or concave) discrete function that is evaluated via simulation. This problem arises, for instance, in optimization problems such as finding the minimal cost staff schedule in a call center subject to a service level constraint. There, subgradient information can be used to significantly reduce the search space. The problem of approximating subgradients is closely related to the one of approximating gradients and we suggest and compare how three existing methods for computing gradients via simulation, i. e., finite differences, the likelihood ratio method and infinitesimal perturbation analysis, can be applied to approximate subgradients when the variables are discrete. We provide a computational study to highlight the properties of each approach.