Browsing Computer Science by Author "Mišković, Stefan"
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Mišković, Stefan (Beograd , 2016)[more][less]
Abstract: In this dissertation, three NP-hard min-max discrete optimization problems are considered. The rst considered problem is multi-period emergency service location problem, the second one is dynamic maximal covering location problem with multiple covering radii, and the third one is uncapacitated multiple allocation p-hub center problem. In many practical situations, input parameters (such as user demands, transportation time or cost) often vary with unknown distributions. Therefore, it is necessary to involve these uncertainties in the deterministic variants of the problems by applying robust optimization approach. Mathematical models for the deterministic and non-deterministic variants of all three problems are developed, except for the deterministic uncapacitated multiple allocation p-hub center problem, which has already been addressed in the literature. In addition, for the rst time in the literature, it was proven that the emergency service location problem is NP-hard. The considered problems and their robust variants have numerous applications, due to the fact that in real-life situations input parameters are often subject to uncertainty. Multi-period emergency service location problem may be used when determining optimal locations for police stations, re brigades, ambulances, and other emergency units in the given region. The dynamic maximal covering location problem with multiple covering radii is useful when choosing the optimal strategy for establishing resources (service centers, suppliers, facilities, etc.) with maximal satisfaction of customer demands in a certain region, by assuming that the service e ciency directly depends on the distance between customer and service center (i.e., the selected coverage radius). The uncapacitated multiple allocation p-hub center problem has signi cant applications in designing telecommunication and transportation networks, postal delivery systems, emergency systems, supply networks, etc. Since exact methods provide optimal solutions only for problem instances of small dimensions, hybrid metaheuristic algorithms are developed to solve both deterministic and robust variants of the considered problems. The proposed hybrid algorithms are obtained by combining particle swarm optimization, with local search heuristic { classical local search or variable neighborhood search method. For dynamic maximal covering location problem with multiple covering radii, a hybridization of metaheuristic algorithm with exact method based on linear programming is developed. All elements of the proposed algorithms are adopted to the problems under consideration. Di erent strategies are implemented for improving the e ciency of proposed algorithms, especially for the calculation of the objective function value and the local search part. The in uence of di erent parameters of hybrid algorithms on the solution quality is analyzed in detail. All parameters are adjusted by using analysis of variance. For all considered problems (both deterministic and robust variant), the performance of the proposed hybrid algorithms is evaluated on adequate test data sets. The proposed algorithms are compared with existing heuristic from the literature and exact methods incorporated in commercial CPLEX solver. The obtained experimental results indicate the e ciency of proposed algorithms in obtaining high quality solutions for all considered test instances. The presented comparative analysis indicates the advantages of the proposed hybrid algorithms over existing methods in the sense of solution quality and/or required computational time, especially in the case of large problem dimensions. The results presented in this paper represent a contribution to the eld of discrete optimization, robust optimization and metaheuristic methods. URI: http://hdl.handle.net/123456789/4423 Files in this item: 1
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