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Optimal Job Scheduling with Day-ahead Price and Random Local Distributed Generation: A Two-stage Robust Approach

Anna Danandeh(annadanandeh***at***mail.usf.edu)
Long Zhao(longzhao***at***mail.usf.edu)
Bo Zeng(bzeng***at***usf.edu)
Mehrnaz Abdollahian(mehrnaz***at***mail.usf.edu)

Abstract: 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.

Keywords: Job Scheduling, Day-ahead Price, Local Geneations, Two-stage Robust Model, Nested Primal Cut

Category 1: Robust Optimization

Category 2: Applications -- OR and Management Sciences (Scheduling )

Citation: Submitted, Unversity of South Florida, FL, 07/2011

Download: [PDF]

Entry Submitted: 08/17/2011
Entry Accepted: 08/17/2011
Entry Last Modified: 08/17/2011

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