Location and emergency inventory pre-positioning for disaster response operations: Min-max robust model and a case study of Yushu earthquake

Wenjun Ni, Jia Shu, Miao Song

Research output: Journal article publicationConference articleAcademic researchpeer-review


Pre-positioning emergency inventory in selected facilities is commonly adopted to prepare for potential disaster threat. In this paper, we simultaneously optimize the decisions of facility location, emergency inventory pre-positioning, and relief delivery operations within a single-commodity disaster relief network. A min-max robust model is proposed to capture the uncertainties in both the left- and right-hand-side parameters in the constraints. The former corresponds to the proportions of the pre-positioned inventories usable after a disaster attack, while the latter represents the demands of the inventories and the road capacities in the disaster-affected areas. We study how to solve the robust model efficiently and analyze a special case that minimizes the humanitarian cost. The application of the model is illustrated by a case study of the 2010 earthquake attack at Yushu County in Qinghai Province of PR China. The advantage of the min-max robust model is demonstrated through comparison with the deterministic model and the two-stage stochastic model for the same problem. Experiment variants also show that the robust model outperforms the other two approaches for instances with significantly larger scales.

Original languageEnglish
Pages (from-to)215
Number of pages1
JournalProceedings of the International Conference on Industrial Engineering and Operations Management
Issue numberJUL
Publication statusPublished - 1 Jan 2017
EventEuropean International Conference on Industrial Engineering and Operations Management.IEOM 2017 -
Duration: 24 Jul 201725 Jul 2017


  • Disaster relief
  • Facility location
  • Inventory pre-positioning
  • Min-max Robust optimization
  • Network flow

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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