A matheuristic approach for Budget Constrained Fuel Treatment Scheduling (Wildfire Conference. Day 1 - Tuesday Nov. 12th, 2019)

By Pau Costa Foundation on

2019 Wildfire Conference Adressing the Challenges of Bushfire Management

Presentations Notes 2019: DAY 1 (Tuesday Nov. 12th, 2019)

A matheuristic approach for Budget Constrained Fuel Treatment Scheduling

(Federico Della Croce, Marco Ghirardi, Rosario Scatamacchia - Department of Management and Production Engineering Politecnico di Torino, Italy)

Abstract:

One of the most studied problems in wildfire mitigation is Fuel Management. Fuel Management aims to reduce potential fire intensity and fire spread in vegetation areas. Recent interest in fire and fuel management is particularly motivated by short fire return intervals and new zones where fire was excluded during the 20th century creating a need for the long-term reduction of fuel loads. The particularity of this management problem is that it requires new and spatially explicit management science methods. The objective of Fuel Management is the modification of potential fire behavior or fire effects by undertaking a minimum action on its fuel. The main idea consists in applying a treatment on selected specific areas, either by harvesting or burning according to some ranking of risk or effectiveness. Several fuel management optimization problems were considered in recent years. We focus here on the Budget constrained Fuel Treatment Scheduling Problem (BFTS) as it was considered in [2], [3] and [4] among others.
In BFTS, a landscape constituted by a set of vegetation areas is considered and high fire risk may occur whenever two contiguous vegetation areas are old. Vegetation may actually become “young” by means of proper fuel treatment activities (e.g. controlled burning). Fuel treatment activities have a cost and for each period there is an available limited budget. The problem calls for finding a suitable selection of the areas to be treated so as to minimize the presence of old contiguous areas over the whole time horizon. Detailed computational results proving the effectiveness of the developed models and algorithms will be presented at the Conference.

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