Multi-period Maximal Covering Location Problem with Modular Facilities for Locating Emergency Facilities with Back-up Services

Roghayyeh Alizadeh, Tatsushi Nishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, an extension of Maximal Covering Location Problem (MCLP) has been developed for locating emergency facilities, composed of discrete structural components. These components are called modules of facilities. In the developed model, demand nodes are assigned to modules first and then modules are allocated to facilities. As the demands in emergency cases vary in different time periods, the problem is studied in multi-time periods. The problem is formulated as an integer programming model. We utilize a genetic algorithm to solve the problem because of this metaheuristic's strength to solve binary optimization problems and other extension of MCLP. Computational experiments are conducted to derive managerial insights.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
PublisherIEEE Computer Society
Pages76-79
Number of pages4
ISBN (Electronic)9781538667866
DOIs
Publication statusPublished - Jan 9 2019
Externally publishedYes
Event2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018 - Bangkok, Thailand
Duration: Dec 16 2018Dec 19 2018

Publication series

NameIEEE International Conference on Industrial Engineering and Engineering Management
Volume2019-December
ISSN (Print)2157-3611
ISSN (Electronic)2157-362X

Conference

Conference2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
Country/TerritoryThailand
CityBangkok
Period12/16/1812/19/18

Keywords

  • Back-up service
  • Genetic algorithm
  • MCLP
  • Modular facilities

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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