Contingency Planning in Project Selection Using Multiobjective Optimization and Chance Constraints

Abstract

We present a multiobjective optimization model for determining an efficient budget allocation for a portfolio of infrastructure projects. The model takes into account both the cost and the priority rank of each project while considering probabilistic constraints related to the available budget. A zero-one multiobjective optimization problem with chance constraints is developed and solved. This approach helps to more accurately take into account competing objectives such as cost and value of the funded projects, as well as budget risk to find a Pareto optimal set of project selection alternatives. We present numerical results based on infrastructure projects from a United States governmental agency in which we analyze the Pareto optimal set of solutions for this problem and test the working of the model with several cases that highlight budget contingencies needed in the face of random project costs.