An optimisation modelling approach to seasonal resource allocation for planned burning
Andrew Higgins A D , Stuart Whitten B , Alen Slijepcevic C , Liam Fogarty C and Luis Laredo AA CSIRO Ecosystems Sciences, 41 Boggo Road, Dutton Park, QLD 4102, Australia. Email: luis.laredo@csiro.au
B CSIRO Ecosystems Sciences, GPO Box 284, Canberra, ACT 2601, Australia. Email: stuart.whitten@csiro.au
C Department of Sustainability and Environment, Level 4, 8 Nicholson Street, East Melbourne, VIC 3002, Australia. Email: alen.slijepcevic@dse.vic.gov.au; liam.fogarty@dse.vic.gov.au
D Corresponding author. Email: andrew.higgins@csiro.au
International Journal of Wildland Fire 20(2) 175-183 https://doi.org/10.1071/WF09103
Submitted: 21 September 2009 Accepted: 16 July 2010 Published: 30 March 2011
Abstract
Burning of fine fuels is a crucial activity in Australia as part of reducing the severity of bushfires. Seasonal planning of such planned burning is a very complex task owing to the large number of practical considerations and uncertainty of burn conditions, as well as personnel and equipment resource constraints. Practical considerations include the small number of suitable burn days for different types of burns, as well as different fuel hazard and burn types. This requires careful management of very high resource requirements during the available days. We developed a tool that will use all of the above variables to estimate the resource requirements for different levels of planned burning program. We provide a mathematical programming approach to help plan burning crews and equipment resource requirements in each district by month to minimise likely personnel requirements, under seasonal uncertainty. A key feature is that it accommodates maximum daily resource demands given uncertainty in available burn days and overlap between geographical districts. We implemented the model on a real-world problem of public land across Victoria, and solved it to optimality using GAMS/Cplex 9.
Additional keywords: mathematical programming, resource scheduling.
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