An Optimization Modeling Approach to Awarding Large Fire Support Wildfire Helicopter Contracts from the US Forest Service

The US Forest Service used contracted helicopter services as part of its wildfire suppression strategy. An optimization decision-modeling system was developed to assist in the contract selection process. Three contract award selection criteria were considered: cost per pound of delivered water, total contract cost, and quality ratings of the aircraft and vendors. Discrete optimization decision models were developed and solved to optimize each of these objectives or contract selection criteria independently. These solutions provided the basis for a weighted goal programming model that minimized the percent deviation from each of the three, incorporating relative preference or priority weights on deviations from the goals. Managers chose to assign a greater relative importance to the quality rating than the two cost measures, resulting in a solution with higher quality aircraft, but at a cost of $55 million more than if total cost had been the only contract selection criteria. These optimization models show promise as a means of supporting agency decisionmaking regarding firefight- ing helicopter contracts. FOR .S CI. 58(2):130 -138.

[1]  J. Keith Gilless,et al.  Analysing initial attack on wildland fires using stochastic simulation , 2006 .

[2]  Costas P. Pappis,et al.  Scheduling fire-fighting tasks using the concept of deteriorating jobs , 2006 .

[3]  David J. Strauss,et al.  Minimizing the cost of wildland fire suppression: a model with uncertainty in predicted flame length and fire-line width produced , 1994 .

[4]  Douglas B. Rideout,et al.  An Integer Programming Model to Optimize Resource Allocation for Wildfire Containment , 2003 .

[5]  J. Goldberg Operations Research Models for the Deployment of Emergency Services Vehicles , 2004 .

[6]  James H. Bookbinder,et al.  Time-Dependent Queueing Approach to Helicopter Allocation for Forest Fire Initial-Attack , 1979 .

[7]  William,et al.  Optimum use of air tankers in initial attack: selection, basing, and transfer rules , 1982 .

[8]  Geoffrey H. Donovan,et al.  Determining the optimal mix of federal and contract fire crews: A case study from the Pacific Northwest , 2006 .

[9]  Omer Saat A Multi-attribute Assignment Goal-programming Model with Incentives , 1987 .

[10]  Jared L. Cohon,et al.  Multiobjective programming and planning , 2004 .

[11]  John von Neumann,et al.  1. A Certain Zero-sum Two-person Game Equivalent to the Optimal Assignment Problem , 1953 .

[12]  M. Flannigan,et al.  Climate change and forest fires. , 2000, The Science of the total environment.

[13]  George M. Parks,et al.  Development and Application of a Model for Suppression of Forest Fires , 1964 .

[14]  Marc J. Schniederjans,et al.  Goal Programming: Methodology and Applications , 2010 .

[15]  David L. Martell,et al.  Basing Airtankers for Forest Fire Control in Ontario , 1996, Oper. Res..

[16]  Cheryl R. Renner,et al.  Goals, obstacles and effective strategies of wildfire mitigation programs in the Wildland-Urban Interface , 2005 .

[17]  P. S. Dwyer Solution of the personnel classification problem with the method of optimal regions , 1954 .

[18]  Robert G. Haight,et al.  Deploying Wildland Fire Suppression Resources with a Scenario-Based Standard Response Model , 2007, INFOR Inf. Syst. Oper. Res..

[19]  Harold W. Kuhn,et al.  The Hungarian method for the assignment problem , 1955, 50 Years of Integer Programming.

[20]  Susan I. Stewart,et al.  The wildland-urban interface in the United States based on 125 million building locations. , 2005, Ecological applications : a publication of the Ecological Society of America.

[21]  Gholam Reza Jahanshahloo,et al.  Goal programming in the context of the assignment problem and a computationally effective solution method , 2008, Appl. Math. Comput..

[22]  R. Faure,et al.  Introduction to operations research , 1968 .