Advances in Robust Optimization Approaches for Scheduling under Uncertainty

The problem of scheduling under uncertainty is addressed. We propose a novel robust optimization methodology, which when applied to Mixed-Integer Linear Programming (MILP) problems produces “robust” solutions that are, in a sense, immune against uncertainty. The robust optimization approach is applied to the scheduling under uncertainty problem. Based on a novel and effective continuous-time short-term scheduling model proposed by Floudas and coworkers (Ierapetritou and Floudas 1998a, 1998b; Ierapetritou et al. 1999; Janak et al. 2004; Lin and Floudas 2001; Lin et al. 2002, 2003), three of the most common sources of uncertainty in scheduling problems can be addressed, namely processing times of tasks, market demands for products, and prices of products and raw materials. Computational results on a small example with uncertainty in the processing times of tasks are presented to demonstrate the effectiveness of the proposed approach.