Home > Archive > 2015 > Volume 5 Number 1 (Feb. 2015) >
IJMLC 2015 Vol. 5(1): 40-43 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.480

An Efficient Imperialism Competitive Algorithm for a Reliable Hub Covering Location Problem

Marjan Sadeghi and Reza Tavakkoli-Moghaddam

Abstract—Hubs are critical elements that benefit networks by switching, distributing and concentrating flows. A hub location problem is common in a lot of industries, such as transportation networks, telecommunication networks, postal delivery networks and express shipment. Furthermore, a transportation network plays a vital role in each country. Nonetheless, during a day many factors such as traffic incidence, natural disasters cause some degradation in the transportation network. As a result, in this paper, we study a reliable hub covering location problem in a degradable transportation network to minimize the total transportation cost. For achieving near-optimal solutions for a real-sized problem an imperialism competitive algorithm (ICA) is proposed. Also the effectiveness of the proposed ICA is shown by some computational experiments. At last, conclusion is provided.

Index Terms—Hub location, road, capacity reliability, chance-constraint method, imperialism competitive algorithm.

The authors are with School of Industrial Engineering, Collage of Engineering, University of Tehran, Tehran, Iran (email: marjan_sadeghi@ut.ac.ir, tavakoli@ut.ac.ir).


Cite: Marjan Sadeghi and Reza Tavakkoli-Moghaddam, "An Efficient Imperialism Competitive Algorithm for a Reliable Hub Covering Location Problem," International Journal of Machine Learning and Computing vol. 5, no. 1, pp. 40-43, 2015.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

Article Metrics