Abstract
This paper deals with the flexible job-shop scheduling problem (FJSP): an amount of jobs have to be executed by a limited number of resources that can be exchanged for some tasks. Solving such a schedule consists in allocating a resource for each task in the jobs. But one must be able to cope with unexpected changes in the model, i.e. uncertainties such as a modification of the duration of some tasks, or an additional job, or a resource that is added or removed... Yet, for operational reasons, the change in the schedule must remain little. We propose a domain-independent plan adaptation algorithm satisfying those requirements, which principle is to move tasks within the plan like sliding puzzle pieces. This algorithm is also able to cope with uncertainties on the tasks duration. It does not need the initial solver. This local search approach is compared to another, a classical tabu search [7] in which we introduced several criteria.
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Soubaras, H. (2015). Reactive Multiobjective Local Search Schedule Adaptation and Repair in Flexible Job-Shop Problems. In: Le Thi, H., Pham Dinh, T., Nguyen, N. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. Advances in Intelligent Systems and Computing, vol 359. Springer, Cham. https://doi.org/10.1007/978-3-319-18161-5_21
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DOI: https://doi.org/10.1007/978-3-319-18161-5_21
Publisher Name: Springer, Cham
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