Abstract
In this paper, we consider a job scheduling problem with random local generation, in which some jobs must be scheduled ahead of time while the others can be scheduled in a real-time fashion. To capture the randomness of the locally distributed generation, we develop a two-stage robust optimization model by considering an uncertainty set without probability information. Given that the problem is challenging, a nested column-and-constraint generation algorithm is implemented to find an optimal solution. Some computational study, along with management insights, is presented to show the effectiveness of the proposed model in dealing with the worst case situations.
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