In this paper, we consider a job scheduling problem with random local generation, in which some jobs must be scheduled day-ahead while the others can be scheduled in a real time fashion. To capture the randomness of the local distributed generation, we develop a two-stage robust optimization model by assuming an uncertainty set without probability information. Given that the problem is challenging, a nested primal cut algorithm is implemented to exactly solve it. A preliminary computational study, along with management insights, is presented to show the effectiveness of the proposed model.
Submitted, Unversity of South Florida, FL, 07/2011