Resource location and relocation models with rolling horizon forecasting for wildland fire planning

Joseph Y.J. Chow, Amelia C. Regan

Research output: Contribution to journalArticlepeer-review


A location and relocation model are proposed for air tanker initial attack basing in California for regional wildland fires that require multiple air tankers that may be co-located at the same air base. The Burning Index from the National Fire Danger Rating System is modeled as a discrete mean-reverting process and estimated from 2001-2006 data for select weather stations at each of 12 California Department of Forestry's units being studied. The standard p-median formulation is changed into a k-server p-median problem to assign multiple servers to a node. Furthermore, this static problem is extended into the time dimension to obtain a chance-constrained dynamic relocation problem. Both problems are solved using branch and bound in the numerical example. The relocation model is shown to perform better than the static location model by as much as 20-30% when using fire weather data to forecast short term future demand for severe fires, whereas relocating without rolling horizon forecasting can be less cost-effective than a static location model. The results suggest that state fire agencies should identify the threshold beyond which it would be more cost-effective to adopt a regional relocation model with forecasting from fire weather data, especially in a global warming environment.

Original languageEnglish (US)
Pages (from-to)31-43
Number of pages13
Issue number1
StatePublished - Feb 1 2011


  • Fire resource location
  • constraint programming
  • mean reversion
  • relocation
  • rolling horizon
  • stochastic process

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Computer Science Applications


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