Applying simulated annealing for the maximal covering location problem with an enhanced algorithm

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Applying simulated annealing for the maximal covering location problem with an enhanced algorithm

 

Author: Mok, Man-ho
Title: Applying simulated annealing for the maximal covering location problem with an enhanced algorithm
Degree: M.Sc.
Year: 1999
Subject: Land use -- Planning -- Mathematical models
Simulated annealing (Mathematics)
Combinatorial optimization
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Applied Mathematics
Pages: ix, 92, [10] leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1477342
URI: http://theses.lib.polyu.edu.hk/handle/200/2185
Abstract: Being a major class of Location problems, the Maximal Covering Location (MCL) Problem, first proposed by Church and ReVelle, involves the location of a fixed number of facilities in such a fashion that acceptable coverage within some distance standard is provided to the largest amount of demand possible. The MCL problem has been applied to the location of a wide range of emergency facilities, especially fire stations. Over the past couple of decades many algorithms have been developed for solving the MCL problem, including the Greedy Addition, Greedy Addition with Substitution, Linear Programming methods with Lagrangian Relaxation, Branch-and-Bound, and etc. Until 1996, Murray and Church developed an entirely different type of heuristic employing a stochastic approach to searching, known as Simulated Annealing (SA). In this dissertation, I develop an enhanced SA algorithm for solving the MCL problem. The algorithm is then implemented in C language and implemented on a microcomputer. Afterwards a series of computational experiments has been carried out on two test data with problem size of 55 and 88 respectively. It is found that the algorithm can produce desirable results, with an average solution deviate from the optimal by less than 3.6%. In terms of Hit Ratio and Mean Program Time, the performance of this enhanced algorithm is also proven satisfactory. Through the process of literature review, I have also studied many models for siting fire stations, all based on the MCL problem. For the sake of completeness, all of these Deterministic and Probabilistic models are stated in the concluding chapter for reference purpose.

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