Coauthor: Julius Atlason and Shane Henderson

Title: Symbiosis of Simulation and Optimization Algorithms in the Context of the Optimal Staffing Problem

We consider iterative algorithms for optimization problems where the objective function and/or the constraints are expressed in terms of expected values or probability bounds on random functions of decision variables. The motivation for these problems arises, for example, in the minimal-cost call center staffing problems. Simulation is employed to approximate function values and subgradients, which are incorporated into cutting plane optimization procedures. We analyze quality of such approximations and resulting theoretical convergence properties. We also discuss some challenges and open questions that arise in devising computationally sound implementations of these algorithms.