Chance Constrained Planning and Scheduling under Uncertainty using Robust Optimization Approximation

Robust optimization can provide safe and tractable analytical approximation for the chance constrained optimization problem. In this work, we studied the application of robust optimization approximation in solving chance constrained planning and scheduling problem under uncertainty. Four different robust optimization approximation methods for improving the quality of robust solution were investigated. The methods include the traditional a priori probability bound based solution method, the a posteriori probability bound based method, the iterative method, and the recently proposed optimal robust optimization approximation algorithm. Applications of the different methods were demonstrated in a process scheduling problem and a production planning problem. Solution quality and computational effectiveness were also compared for the various methods.