Prioritization via stochastic optimization
We take a novel approach to decision problems involving binary activity-selection decisions competing for scarce resources. The literature approaches such problems by forming an optimal portfolio of activities. However, often practitioners instead form a rank-ordered list of activities and select those with the highest priority. We account for both viewpoints. We rank activities considering both the uncertainty in the problem parameters and the optimal portfolio that will be obtained once the uncertainty is revealed. We use stochastic integer programming as a modeling framework, and we apply our approach to a facility location problem and a multidimensional knapsack problem. We develop two sets of cutting planes to improve computation.