Bayesian forecasting models provide distributional estimates for random parameters, and relative to classical schemes, have the advantage that they can rapidly capture changes in ...
This paper contributes to solve effectively stochastic resource allocation problems known to be NP-Complete. To address this complex resource management problem, the merging of tw...
Approximate dynamic programming is emerging as a powerful tool for certain classes of multistage stochastic, dynamic problems that arise in operations research. It has been applie...
Context-free grammar constraints enforce that a sequence of variables forms a word in a language defined by a context-free grammar. The constraint has received a lot of attention ...
In this paper, we present a stochastic version of the Location Model with Risk Pooling (LMRP) that optimizes location, inventory, and allocation decisions under random parameters ...
Lawrence V. Snyder, Mark S. Daskin, Chung-Piaw Teo