Scheduling with uncertain durations: generating β-robust schedules using constraint programming

Many real-world scheduling problems are subject to change, and scheduling solutions should be robust to those changes. We consider a single-machine scheduling problem where the processing time of each activity is characterized by a normally-distributed random variable, and we attempt to minimize flowtime. We develop an initial constraint model for generating the β-robust schedule the schedule that has highest probability of producing a flowtime less than a stated bound. Experiments with this initial model show that a constraint-based approach is feasible, but that better propagation methods will be required.