Abstract
This paper discusses an uncertain job-shop scheduling problem with the makespan as the performance criterion. Uncertain processing times are described by discrete scenarios. A robust optimization model is established for the job-shop scheduling problem based on a set of bad scenarios to hedge against the risk of achieving substandard performances among these bad scenarios. To solve the established problem, a problem-specific neighborhood structure is constructed by uniting multiple single-scenario neighborhoods. The constructed neighborhood structure is applied in a hybrid local-search algorithm of combining the simulated-annealing search and the tabu technique. An extensive computational experiment was conducted. The developed algorithm was compared with two possible alternative algorithms. The computational results show the efficiency of the defined neighborhood structure and the competitiveness of the developed hybrid local-search algorithm for the established model.
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