A humanitarian logistics model to minimize losses aftermath of an earthquake in large-scale and actual size

Document Type : Research Paper

Authors

1 M.Sc graduated. industrial engineering- logistics and supply chain engineering, Faculty of industrial engineering and management systems, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

2 Assoc. prof., Faculty of industrial engineering and management systems, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.

3 M.Sc Graduated, department of industrial engineering, faculty of engineering, Bu-Ali Sina University, Hamedan, Iran.

Abstract

In recent decades, natural disasters imposed financial and humanitarian costs to the governments and societies. One of the most important activities which is done in every relief operation and encompass roughly 80 percent of all activities is logistics operations. Thus, a new area and subject with title of humanitarian logistics are stated in literature in recent years. Humanitarian logistics aims to minimize loss of lives by using common models in commercial logistics and considering specific situation and issues such as stochastic demand, lack of precise information aftermath of a crisis, transportation network failure, and very short and limited relief time. This study aims to utilize a mix integer linear humanitarian logistics model. Transportation network failure aftermath of an earthquake, standard relief time and online datawere obtained by the geographic information systems (GIS).These data were used in the model to locate distribution centers of relief supplies. Moreover, this study indicated vehicle routing for the response time.  Then, the developed model was implemented in San Francisco city as a real life and large-scale case study by GIS's data. To solve the model, variable neighborhood search (VNS) was used. Variable neighborhood search (VNS) is used for the first time to solve a large-scale humanitarian logistics model in this study.

Keywords

Files

History

  • Receive Date: 22 December 2013
  • Revise Date: 25 January 2014
  • Accept Date: 17 February 2014

Share

How to cite

Statistics