Improving the efficiency of helicopter operations on large wildland fires by including helicopter performance information in the decision process

Extreme fire seasons and rising fire suppression costs have made cost containment and suppression effectiveness a major concern. Fire suppression efforts must achieve their objectives at a minimum cost. This can be achieved by ensuring that the appropriate fire suppression resources are used. Since helicopter operations often account for a significant portion of the suppression costs on large wildland fires it is especially important that deployment decisions are made so the most efficient helicopters are deployed.;Helicopter performance, i.e., lifting capability, is a unique characteristic of individual helicopters because it depends not only on the make and model of the helicopter but also the weight of the equipped helicopter, fuel, and pilots. Helicopter performance changes with the altitude and temperature of operation. Hence it is not easy to include helicopter performance when deciding which helicopter will be most efficient at a fire.;A comparison index, which summarizes helicopter performance and cost information, is developed so the efficiency of individual helicopters can be compared at representative altitude and temperature conditions. By using the index to deploy helicopters to a fire significant savings can be achieved.;Finding the most efficient way to deploy helicopters to multiple fires requires a more sophisticated technique. The optimization problem is solved using mixed integer programming to assign helicopters to fires so the cost per pound delivered at each fire is minimized while the travel time to the fire is constrained. A genetic algorithm is also developed to solve the optimization problem with the multiple objectives of minimizing the cost and travel time and maximizing the amount delivered for each fire. The genetic algorithm finds a set of optimal solutions to the deployment problem that describes the tradeoffs between the competing objectives.;A comparison of the mixed integer programming and genetic algorithm shows that either method can be used to solve the problem, but their solutions provide different types of information. With mixed integer programming a single optimal solution is found, while the genetic algorithm describes the solution space and provides additional information that can be used in the decision making process.