Distributionally robust optimization for fire station location under uncertainties

Jinke Ming, Jean Philippe P. Richard, Rongshui Qin, Jiping Zhu

Research output: Contribution to journalArticlepeer-review

Abstract

Emergency fire service (EFS) systems provide rescue operations for emergencies and accidents. If properly designed, they can decrease property loss and mortality. This paper proposes a distributionally robust model (DRM) for optimizing the location of fire stations, the number of fire trucks, and demand assignment for long term planning in an EFS system. This is achieved by minimizing the worst-case expected total cost, including fire station construction cost, purchase cost for fire trucks, transportation cost, and penalty cost for not providing adequate service. The ambiguity in demands and travel durations distributions are captured through moment information and mean absolute deviation. A cutting plane method is used to solve the problem. Due to fact that it is computationally intensive for larger problems, two approximate methods are introduced; one that uses linear decision rules (LDRs), and another that adopts three-point approximations of the distributions. The results show that the heuristic method is especially useful for solving large instances of DRM. Extensive numerical experiments are conducted to analyze the model’s performance with respect to different parameters. Finally, data obtained from Hefei (China) demonstrates the practical applicability and value of the model in designing an EFS system in a large metropolitan setting.

Original languageEnglish (US)
Article number5394
JournalScientific reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

Bibliographical note

Funding Information:
This work is sponsored by the National Key Research and Development Plan (Grant No. 2020YFC1522805). The authors (not including J.-P. Richard) are also funded by the National Natural Science Foundation of China (NSFC 51936011) and the Central University Basic Scientific Research Business Expenses Special Funds (WK2320000040). The authors are grateful to the Fire Bureau of China’s Anhui Province that provided historical fire records.

Funding Information:
This work is sponsored by the National Key Research and Development Plan (Grant No. 2020YFC1522805). The authors (not including J.-P. Richard) are also funded by the National Natural Science Foundation of China (NSFC 51936011) and the Central University Basic Scientific Research Business Expenses Special Funds (WK2320000040). The authors are grateful to the Fire Bureau of China?s Anhui Province that provided historical fire records.

Publisher Copyright:
© 2022, The Author(s).

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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