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Study on the emergency material allocation from the perspective of cross-regional rescue

  • School of Management, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Emergency material allocation is essential to improve emergency rescue effectiveness and reduce disaster losses, which has become a research focus for emergency response. However, it is seldom given due consideration in disaster response from cross-regional rescue perspective. For this reason, the paper breaks the traditional regional emergency rescue mode and proposes a cross-regional emergency material allocation model with the goal of the lowest transportation costs, the shortest delivery time and the maximum satisfaction of the victims. Finally, Numerical experiment and analysis are conducted to demonstrate the feasibility of the proposed model. The results show that the proposed model can make the emergency material allocation more efficient and fair, and can provide method and decision support for the cross-regional emergency rescue.

Original languageEnglish
Title of host publication2018 International Conference on Management Science and Engineering - 25th Annual Proceedings, ICMSE 2018
EditorsMa Tao, Shao Zhen, Mengyue Li
PublisherIEEE Computer Society
Pages331-337
Number of pages7
ISBN (Electronic)9781538684719
DOIs
StatePublished - Aug 2018
Externally publishedYes
Event25th Annual International Conference on Management Science and Engineering, ICMSE 2018 - Frankfurt, Germany
Duration: 17 Aug 201820 Aug 2018

Publication series

NameInternational Conference on Management Science and Engineering - Annual Conference Proceedings
Volume2018-August
ISSN (Print)2155-1847

Conference

Conference25th Annual International Conference on Management Science and Engineering, ICMSE 2018
Country/TerritoryGermany
CityFrankfurt
Period17/08/1820/08/18

Keywords

  • Cross-regional rescue
  • Efficiency-fairness principle
  • Emergency material allocation
  • Multiple objective optimization model

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