A heuristic approach to schedule reoptimization in the context of interactive optimization
Autor(en): | Meignan, D. | Stichwörter: | Artificial intelligence; Decision support systems; Heuristic; Heuristic methods; Interactive optimization; Optimization models; Optimization procedures; Optimization system; Optimization, Heuristic; Planning and scheduling systems; Reoptimization; Shift scheduling; Shift scheduling, Scheduling | Erscheinungsdatum: | 2014 | Herausgeber: | Association for Computing Machinery | Journal: | GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference | Startseite: | 461 | Seitenende: | 468 | Zusammenfassung: | Optimization models used in planning and scheduling systems are not exempt from inaccuracies. These optimization systems often require an expert to assess solutions and to adjust them before taking decisions. However, adjusting a solution computed by an optimization procedure is difficult, especially because of the cascading effect. A small modification in a candidate solution may require to modify a large part of the solution. This obstacle to the adjustment of a solution can be overcome by interactive reoptimization. In this paper we analyze the impact of the cascading effect on a shift-scheduling problem and propose an efficient heuristic approach for reoptimizing solutions. The proposed approach is a local-search metaheuristic that has been adapted to the reoptimization. This approach is evaluated on a set of problem instances on which additional preferences are generated to simulate desired adjustments of a decision maker. Experimental results indicate that, even with a small perturbation, the cascading effect is manifest and cannot be efficiently tackled by applying recovery actions. Moreover, results show that the proposed reoptimization method provides significant cost gains within a short time while keeping a level of simplicity and modularity adequate for an implementation in a decision support system. © 2014 is held by the owner/author(s). |
Beschreibung: | Conference of 16th Genetic and Evolutionary Computation Conference, GECCO 2014 ; Conference Date: 12 July 2014 Through 16 July 2014; Conference Code:106779 |
ISBN: | 9781450326629 | DOI: | 10.1145/2576768.2598213 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905686848&doi=10.1145%2f2576768.2598213&partnerID=40&md5=6d3386c05c832f39988a1b1323aa80a7 |
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geprüft am 19.05.2024