Apply Ant Colony Optimization for Solving Uncapacitated P-Hub Median Problem
碩士 === 元智大學 === 工業工程與管理學系 === 96 === Demand of long hual transportation is increasing as a result of economic globalization. Hence, enterprises outsource the transportation function to the third party logistic, 3PL in order to elevate core competence. Hub and spoke network is adopted by transportati...
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ndltd-TW-096YZU050310322015-10-13T13:48:21Z http://ndltd.ncl.edu.tw/handle/28112079770653756554 Apply Ant Colony Optimization for Solving Uncapacitated P-Hub Median Problem 應用蟻群最佳化演算法求解無容量限制p轉運點中位問題 Kang-Ting Ma 馬綱廷 碩士 元智大學 工業工程與管理學系 96 Demand of long hual transportation is increasing as a result of economic globalization. Hence, enterprises outsource the transportation function to the third party logistic, 3PL in order to elevate core competence. Hub and spoke network is adopted by transportation industry due to the cost of international transportation is expensive. Since location of hubs would affect efficiency of entire transportation network, and high fixed cost of hub facilities are expensive, location of hub has became an important issue. It is extremely hard to solve exactly p-hub median problem, while number of nodes and hubs are increasing. Hence, in recent years, metaheuristic algorithms such as Ant Colony Optimization Algorithm, ACO or Genetic Algorithm, GA are adopted, and solve the problem in reasonable time. Especially ACO has shown outstanding performance in the combinatorial optimization problem, but few literature of p-hub median problem employs ACO. In this paper, a metaheuristic algorithm base on ACO is proposed, which contains solution construction of location of hubs and allocation of non-hubs and a local search mechanism as well. The objective of p-hub median problem is to minimize the total transportation cost. This paper not only treats uncapacitated single allocation p-hub median problem, but also treats uncapacitated multiple allocation p-hub median problem. In AP data set of single allocation problem, our algorithm obtains several novel solutions and the objective value in three out of eight non-optimal solution instances. In multiple allocation problem, our algorithm obtains optimal solutions in both CAB or AP data set. As the computational results, the proposed algorithm is not only effective, but also efficient. Hence, it should be a feasible method to deal with uncapacitated p-hub median problem. Ching-Jung Ting 丁慶榮 2008 學位論文 ; thesis 91 zh-TW |
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碩士 === 元智大學 === 工業工程與管理學系 === 96 === Demand of long hual transportation is increasing as a result of economic globalization. Hence, enterprises outsource the transportation function to the third party logistic, 3PL in order to elevate core competence. Hub and spoke network is adopted by transportation industry due to the cost of international transportation is expensive. Since location of hubs would affect efficiency of entire transportation network, and high fixed cost of hub facilities are expensive, location of hub has became an important issue. It is extremely hard to solve exactly p-hub median problem, while number of nodes and hubs are increasing. Hence, in recent years, metaheuristic algorithms such as Ant Colony Optimization Algorithm, ACO or Genetic Algorithm, GA are adopted, and solve the problem in reasonable time. Especially ACO has shown outstanding performance in the combinatorial optimization problem, but few literature of p-hub median problem employs ACO. In this paper, a metaheuristic algorithm base on ACO is proposed, which contains solution construction of location of hubs and allocation of non-hubs and a local search mechanism as well. The objective of p-hub median problem is to minimize the total transportation cost. This paper not only treats uncapacitated single allocation p-hub median problem, but also treats uncapacitated multiple allocation p-hub median problem. In AP data set of single allocation problem, our algorithm obtains several novel solutions and the objective value in three out of eight non-optimal solution instances. In multiple allocation problem, our algorithm obtains optimal solutions in both CAB or AP data set. As the computational results, the proposed algorithm is not only effective, but also efficient. Hence, it should be a feasible method to deal with uncapacitated p-hub median problem.
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Ching-Jung Ting |
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Ching-Jung Ting Kang-Ting Ma 馬綱廷 |
author |
Kang-Ting Ma 馬綱廷 |
spellingShingle |
Kang-Ting Ma 馬綱廷 Apply Ant Colony Optimization for Solving Uncapacitated P-Hub Median Problem |
author_sort |
Kang-Ting Ma |
title |
Apply Ant Colony Optimization for Solving Uncapacitated P-Hub Median Problem |
title_short |
Apply Ant Colony Optimization for Solving Uncapacitated P-Hub Median Problem |
title_full |
Apply Ant Colony Optimization for Solving Uncapacitated P-Hub Median Problem |
title_fullStr |
Apply Ant Colony Optimization for Solving Uncapacitated P-Hub Median Problem |
title_full_unstemmed |
Apply Ant Colony Optimization for Solving Uncapacitated P-Hub Median Problem |
title_sort |
apply ant colony optimization for solving uncapacitated p-hub median problem |
publishDate |
2008 |
url |
http://ndltd.ncl.edu.tw/handle/28112079770653756554 |
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