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Research on Some Models and Algorithms in Network Location Field

Author: XuJinPeng
Tutor: JiangJianLin
School: Nanjing University of Aeronautics and Astronautics
Course: Operational Research and Cybernetics
Keywords: Network location problem Set cover problem Vertex p- center problem Multi - target anti- p- center problem Generalized minimum spanning tree problem NP-hard Metaheuristic
CLC: O221.4
Type: Master's thesis
Year: 2010
Downloads: 87
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Abstract


The location problem is one of the classic problems in operations research in production and life , even in the military have a very wide range of applications . Network is most of the site the main location decisions carrier , so the research site on the network are often more practical significance . Thesis research with the metaheuristic improved to solving a certain model of the network location , these models are NP- hard problem . The specific content of the paper is as follows : The first chapter introduces the research background and siting Research , and explains the main work of this paper . The second chapter describes some of the classic network location problem . The third chapter describes some metaheuristic . Chapter 4 , for the large-scale collection of covers , proposed an improved genetic algorithm to solve . The improvements include the generation of the initial population , genetic algorithm proposed infeasible solutions and repeat individual treatment , as well as a new crossover and variation method . Finally , numerical experiments with other algorithms compare and analyze the effectiveness of the improved algorithm . Chapter V , for larger vertex p-center problem , through the advantages of comprehensive genetic algorithms and simulated annealing algorithm , proposed a single genetic and simulated annealing hybrid algorithm for solving design adaptive selection method and self- adaptation of recombinant operation, and finally in the numerical experiments were compared with the other three algorithms , the results show that the chapter more efficient algorithm . Chapter proposed a multi- target anti - p- center problem into single objective problem , and then create the integer programming model , and try to use a single parent genetic simulated annealing algorithm to solve linear weighted sum method . The final weight for different weights , numerical experiments were carried out , and analyze the effectiveness of the algorithm . Chapter VII , for generalized minimum spanning tree problem , design a two improved metaheuristic to solve . Improved tabu search algorithm search in two neighborhoods to avoid falling into the local optimum . Finally , through numerical experiments compare these two algorithms , and verify the effectiveness of the algorithm . Finally, some concluding remarks , and pointed out that the future research directions .

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CLC: > Mathematical sciences and chemical > Mathematics > Operations Research > Planning Theory ( mathematical programming) > Integer Programming
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