Integrating location and network restoration decisions in relief networks under uncertainty

Ece Sanci, Mark S. Daskin

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Prepositioning emergency relief items in emergency response facilities before an anticipated disaster is a common strategy to increase the effectiveness of relief distribution. In this paper, we assume that relief distribution activities are hampered due to damaged roads, which can be restored by repair teams using restoration equipment. We propose a two-stage stochastic programming model integrating facility location and network restoration decisions. Our integrated model decides on the location of restoration equipment prior to the disaster in addition to the facility location decisions. Moreover, decisions related to relief item distribution and network restoration are made jointly after the disaster for each disaster scenario. We capture uncertainty in the network availability by incorporating the repair times required to restore the damaged roads. To solve our integrated model efficiently, we develop a sample average approximation method with concentration sets motivated by Rosing and ReVelle's (1997) Heuristic Concentration. These concentration sets are comprised of promising locations identified by information obtained from disaster scenarios. We limit our solution space in the first stage to concentration sets to reduce the problem size without sacrificing the solution quality significantly. Our computational results show significant improvement in unmet demand and cost measures by integrating location and network restoration models.

Original languageEnglish
Pages (from-to)335-350
Number of pages16
JournalEuropean Journal of Operational Research
Volume279
Issue number2
Early online date8 Jun 2019
DOIs
Publication statusPublished - 1 Dec 2019

Keywords

  • Concentration sets
  • Humanitarian logistics
  • Network restoration
  • Prepositioning
  • Two-stage stochastic programming

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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