A model for solving the prescribed burn planning problem. Rachmawati R, Ozlen M, Reinke KJ, Hearne JW - Springerplus (2015) Bottom Line: This involves long-term planning of fuel reduction activities such as prescribed burning or mechanical clearing.Both heuristic approaches can solve significantly larger problems, involving 100-year or even longer planning horizons.Furthermore there are no substantial differences in the solutions produced by the three approaches. View Article: PubMed Central - PubMed Affiliation: School of Mathematical and Geospatial Sciences RMIT University, Melbourne, Australia ; Mathematics Department, Faculty of Mathematics and Natural Sciences, University of Bengkulu, Bengkulu, Indonesia. ABSTRACTThe increasing frequency of destructive wildfires, with a consequent loss of life and property, has led to fire and land management agencies initiating extensive fuel management programs. This involves long-term planning of fuel reduction activities such as prescribed burning or mechanical clearing. In this paper, we propose a mixed integer programming (MIP) model that determines when and where fuel reduction activities should take place. The model takes into account multiple vegetation types in the landscape, their tolerance to frequency of fire events, and keeps track of the age of each vegetation class in each treatment unit. The objective is to minimise fuel load over the planning horizon. The complexity of scheduling fuel reduction activities has led to the introduction of sophisticated mathematical optimisation methods. While these approaches can provide optimum solutions, they can be computationally expensive, particularly for fuel management planning which extends across the landscape and spans long term planning horizons. This raises the question of how much better do exact modelling approaches compare to simpler heuristic approaches in their solutions. To answer this question, the proposed model is run using an exact MIP (using commercial MIP solver) and two heuristic approaches that decompose the problem into multiple single-period sub problems. The Knapsack Problem (KP), which is the first heuristic approach, solves the single period problems, using an exact MIP approach. The second heuristic approach solves the single period sub problem using a greedy heuristic approach. The three methods are compared in term of model tractability, computational time and the objective values. The model was tested using randomised data from 711 treatment units in the Barwon-Otway district of Victoria, Australia. Solutions for the exact MIP could be obtained for up to a 15-year planning only using a standard implementation of CPLEX. Both heuristic approaches can solve significantly larger problems, involving 100-year or even longer planning horizons. Furthermore there are no substantial differences in the solutions produced by the three approaches. It is concluded that for practical purposes a heuristic method is to be preferred to the exact MIP approach. No MeSH data available. Related in: MedlinePlus © Copyright Policy - OpenAccess Related In: Results  -  Collection License getmorefigures.php?uid=PMC4627981&req=5 .flowplayer { width: px; height: px; } Fig2: Fuel treatment outcomes, for a 5 % treatment level (40 treatment units) Mentions: Consider a landscape divided into 40 treatment units. The area of each treatment unit, vegetation type and age are described in Table 1. The data regarding the minimum and the maximum TFI and the fuel type of each ecological vegetation class (EVC), can be seen in column two to five in Table 2. Figure 1 represents the fuel curve for each age of the certain vegetation type. Based on this data, some computational experiments were conducted to demonstrate three approaches: the exact MIP, the exact single-period and the approximate single-period problem. For the three approaches, we ran 5 and 10 % treatment levels, with and without TFI requirements. Figure 2 represents the fuel treatment schedule for the 5-year planning horizon with TFI requirements. The total fuel load resulting from the experiments for the 5-year planning horizon is represented in Fig. 3.

A model for solving the prescribed burn planning problem.

Rachmawati R, Ozlen M, Reinke KJ, Hearne JW - Springerplus (2015)

Related In: Results  -  Collection

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Fig2: Fuel treatment outcomes, for a 5 % treatment level (40 treatment units)
Mentions: Consider a landscape divided into 40 treatment units. The area of each treatment unit, vegetation type and age are described in Table 1. The data regarding the minimum and the maximum TFI and the fuel type of each ecological vegetation class (EVC), can be seen in column two to five in Table 2. Figure 1 represents the fuel curve for each age of the certain vegetation type. Based on this data, some computational experiments were conducted to demonstrate three approaches: the exact MIP, the exact single-period and the approximate single-period problem. For the three approaches, we ran 5 and 10 % treatment levels, with and without TFI requirements. Figure 2 represents the fuel treatment schedule for the 5-year planning horizon with TFI requirements. The total fuel load resulting from the experiments for the 5-year planning horizon is represented in Fig. 3.

Bottom Line: This involves long-term planning of fuel reduction activities such as prescribed burning or mechanical clearing.Both heuristic approaches can solve significantly larger problems, involving 100-year or even longer planning horizons.Furthermore there are no substantial differences in the solutions produced by the three approaches.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematical and Geospatial Sciences RMIT University, Melbourne, Australia ; Mathematics Department, Faculty of Mathematics and Natural Sciences, University of Bengkulu, Bengkulu, Indonesia.

ABSTRACT
The increasing frequency of destructive wildfires, with a consequent loss of life and property, has led to fire and land management agencies initiating extensive fuel management programs. This involves long-term planning of fuel reduction activities such as prescribed burning or mechanical clearing. In this paper, we propose a mixed integer programming (MIP) model that determines when and where fuel reduction activities should take place. The model takes into account multiple vegetation types in the landscape, their tolerance to frequency of fire events, and keeps track of the age of each vegetation class in each treatment unit. The objective is to minimise fuel load over the planning horizon. The complexity of scheduling fuel reduction activities has led to the introduction of sophisticated mathematical optimisation methods. While these approaches can provide optimum solutions, they can be computationally expensive, particularly for fuel management planning which extends across the landscape and spans long term planning horizons. This raises the question of how much better do exact modelling approaches compare to simpler heuristic approaches in their solutions. To answer this question, the proposed model is run using an exact MIP (using commercial MIP solver) and two heuristic approaches that decompose the problem into multiple single-period sub problems. The Knapsack Problem (KP), which is the first heuristic approach, solves the single period problems, using an exact MIP approach. The second heuristic approach solves the single period sub problem using a greedy heuristic approach. The three methods are compared in term of model tractability, computational time and the objective values. The model was tested using randomised data from 711 treatment units in the Barwon-Otway district of Victoria, Australia. Solutions for the exact MIP could be obtained for up to a 15-year planning only using a standard implementation of CPLEX. Both heuristic approaches can solve significantly larger problems, involving 100-year or even longer planning horizons. Furthermore there are no substantial differences in the solutions produced by the three approaches. It is concluded that for practical purposes a heuristic method is to be preferred to the exact MIP approach.

No MeSH data available.

Related in: MedlinePlus