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Civil-Comp Proceedings
ISSN 1759-3433 CCP: 97
PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: Y. Tsompanakis, B.H.V. Topping
Paper 19
A Hybrid Metaheuristic for the Resource-Constrained Project Scheduling Problem with Fuzzy Activity Durations S. Danka
University of Pécs, Hungary S. Danka, "A Hybrid Metaheuristic for the Resource-Constrained Project Scheduling Problem with Fuzzy Activity Durations", in Y. Tsompanakis, B.H.V. Topping, (Editors), "Proceedings of the Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 19, 2011. doi:10.4203/ccp.97.19
Keywords: resource-constrained project scheduling, fuzzy project scheduling, robust scheduling, metaheuristics, hybrid methods.
Summary
This paper presents a hybrid metaheuristic for the resource-constrained project scheduling problem with fuzzy activity durations. The heuristic algorithm produces "robust" resource-feasible schedules which are totally immune against uncertainties in the activity durations. Theoretically the robust schedule searching process is formulated as a mixed integer linear programming problem based on the "forbidden set" concept. The output of the model is an optimal (robust) conflict repairing relation set. The model developed by Csébfalvi [1] is insensitive to the "real meaning" of the input parameters, so the originally probabilistic (density function oriented) approach can be replaced by a possibilistic (membership function oriented) approach without essential modifications. The hybrid algorithm presented is based on the "sounds of silence" harmony search metaheuristic developed by Csébfalvi et al. [2,3]. In order to illustrate the essence and applicability of the proposed approach detailed computational results for two problems are presented. The first problem is a small motivating example, the second problem is a larger project instance presented firstly by Golenko-Ginzburg and Gonic [4] and discussed by several authors in the literature. To generate the optimal solutions a state-of-the-art MILP solver (CPLEX) was used.
References
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