A two stage stochastic programming for asset protection routing and a solution algorithm based on the Progressive Hedging algorithm  

Mahdi Bashiri, Erfaneh Nikzad, Andrew Eberhard, John Hearne, Fabricio Oliveira

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

In this paper, a two-stage stochastic programming model is developed for the asset protection routing problem (APRP) to be employed in anticipation of an escaped wildfire. In this model, strategic and tactical decisions are considered in a two-stage setting. The locations of protection depots are determined, taking into account the routing decisions under different possible scenarios. To solve the proposed model, the Frank-Wolfe Progressive Hedging decomposition approach is employed. A realistic case study set in south Hobart, Tasmania, is considered. In this study, the scenarios for uncertain parameters are generated based on real data, considering different sources of uncertainties such as wind direction and speed and total monthly rainfall. Computational experiments have been conducted to demonstrate the solution algorithm’s efficiency in solving the asset protection routing problem with a two-stage stochastic framework. The numerical results suggest that more assets with higher values can be protected by considering the proposed two-stage stochastic programming model. The value of the approach is particularly significant where resources are limited, and uncertainty levels are high. Moreover, the model and solution procedure can be applied to other disaster situations in which protection activities occur.
Original languageEnglish
Article number102480
JournalOmega
Volume104
Early online date5 May 2021
DOIs
Publication statusPublished - Oct 2021

Keywords

  • Asset protection
  • Location routing problem
  • Stochastic programming
  • Frank-Wolfe Progressive Hedging method
  • Wildfires
  • Frank–Wolfe Progressive Hedging method

ASJC Scopus subject areas

  • Information Systems and Management
  • Strategy and Management
  • Management Science and Operations Research

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