Hub Location and thep-Hub Median Problem

The present paper deals with a variant of hub location problems (HLP): the uncapacitated single allocation p-Hub median problem (USApHMP). This problem consists to jointly locate hub facilities and to allocate demand nodes to these selected facilities. The objective function is to minimize the routing of demands between any origin and destination pair of nodes. This problem is known to be NP-hard. Based on the artificial immune systems (AIS) framework, this paper develops a new approach to efficiently solve the USApHMP. The proposed approach is in the form of a clonal selection algorithm (CSA) that uses appropriate encoding schemes of solutions and maintains their feasibility. Comprehensive experiments and comparison of the proposed approach with other existing heuristics are conducted on benchmark from civil aeronautics board, Australian post, PlanetLab and Urand data sets. The results obtained allow to demonstrate the validity and the effectiveness of our approach. In terms of solution quality, the results obtained outperform the best-known solutions in the literature.

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Model Hub Median Problem Dengan Batasan Travel Time

Jurnal Teknik Industri ◽

10.22219/jtiumm.vol18.no1.1-8 ◽

2017 ◽

Vol 18 (1) ◽

pp. 1

Author(s):

Faisal Ibrahim ◽

Ahmad Rusdiansyah

Keyword(s):

Numerical Experiments ◽

Cost Minimization ◽

Computational Time ◽

Hub Location ◽

Median Problem ◽

Problem Model ◽

Excel Solver ◽

Hub Model ◽

Spread Sheet ◽

Total Cost Minimization

This article describes the development of an uncapacitated p-hub median problem model. The established model will be applied in determining the location of the hub. Generally in the median p-hub model problem, it is known that the number of hubs built with the total cost minimization function. In this developed model, there is no limit to the number of hubs and the function of the intended destination is how many hubs are built. The model also looks for where the hub location will be built and which nodes are allocated to each hub. By eliminating the limitation of the number of hubs built, the model adds a total total timeout limit. The numerical experiments are dealing with the model. The solution to solve the model using excel solver. So that it will be designed spread sheet excel appropriate model. In numerical experiments, dummy data representing real systems will be used with the aim of shortening computational time. Numerical experiments are performed in several conditions scenarios. Computational results generate the location of hubs and nodes allocated to each hub with various experimental scenarios.

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Artificial Immune Systems for Solving the Uncapacitated Single-Allocation p-Hub Median Problem

10.5753/eniac.2018.4454 ◽

2018 ◽

Author(s):

Gabriel L. Nobrega ◽

Vinicius J. Tasso ◽

Allan G. Souza ◽

Stephanie A. Fernandez ◽

Daniel G. Silva

Keyword(s):

Network Design ◽

Optimization Problems ◽

Artificial Immune Systems ◽

Research Interest ◽

Artificial Immune ◽

Hub Location ◽

Median Problem ◽

Immune Systems ◽

Single Allocation

Optimization problems such as the Uncapacitated Single-Allocation p-Hub Median Problem represent good models for real network design issues, hence an increasing research interest has emerged. A good hub location reduces costs and improves the quality of delivered services on network-based systems. In this work, two artificial immune systems are employed in order to address the problem, where the numerical results indicate good quality of solutions.

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