Parallel processors for planning under uncertainty
Annals of Operations Research
Stochastic decomposition: an algorithm for two-state linear programs with recourse
Mathematics of Operations Research
Finite master programs in regularized stochastic decomposition
Mathematical Programming: Series A and B
Accelerating the convergence of the stochastic ruler method for discrete stochastic optimization
Proceedings of the 29th conference on Winter simulation
Optimization via simulation: a combined procedure for optimization via simulation
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Simulation optimization with mathematical programming representation of discrete event systems
Proceedings of the 40th Conference on Winter Simulation
An open-source population indifference zone-based algorithm for simulation optimization
Proceedings of the Winter Simulation Conference
Investigating the use of multi meta-heuristics in simulation optimization
Proceedings of the Winter Simulation Conference
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Because of its applicability, as well as its generality, research in the area of simulation-optimization continues to attract significant attention. These methods, most of which rely on the statistically motivated search techniques, are at their best when very little is known about the structure of the function (e.g., function evaluations are treated as "blackbox" function-calls). In some applications such as the one discussed in this paper, objective function values may be obtained through linear/network flow optimization models. In such cases, the objective function may be convex, and in such circumstances, very large instances can be solved using stochastic programming techniques. This paper presents a computational case for using such techniques, whenever applicable.