Derivative estimates from simulation of continuous-time Markov chains
Operations Research
Sample-path optimization of convex stochastic performance functions
Mathematical Programming: Series A and B
An optimal policy for a two depot inventory problem with stock transfer
Management Science
Analysis of sample-path optimization
Mathematics of Operations Research
Accelerating the convergence of random search methods for discrete stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Convergence properties of two-stage stochastic programming
Journal of Optimization Theory and Applications
Introduction to Linear Optimization
Introduction to Linear Optimization
The Sample Average Approximation Method for Stochastic Discrete Optimization
SIAM Journal on Optimization
A Two-Location Inventory Model with Transshipment and Local Decision Making
Management Science
Integrated inventory pooling for firms servicing both on-line and store demand
Computers and Operations Research
Who Benefits from Transshipment? Exogenous vs. Endogenous Wholesale Prices
Management Science
Gradient-based simulation optimization
Proceedings of the 38th conference on Winter simulation
Balanced Explorative and Exploitative Search with Estimation for Simulation Optimization
INFORMS Journal on Computing
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We consider a supply chain, which consists of N stocking locations and one supplier. The locations may be coordinated through replenishment strategies and lateral transshipments, i.e., transfer of a product among locations at the same echelon level. The supplier has limited production capacity. Therefore, the total amount of product supplied to the N locations is limited in each time period. When total replenishment orders exceed total supply, not all locations will be able to attain their base stock values. Therefore, different allocation rules are considered to specify how the supplier rations its limited capacity among the locations. We team up the modeling flexibility of simulation with sample path optimization to address the multi-location transshipment problem. We solve the sample average approximation problem by random search and by gradient search. With this numerical approach, we can study problems with non-identical costs and correlated demand structures.