An Optimization Modeling Approach to Awarding Large Fire Support Wildfire Helicopter Contracts from the US Forest Service
Abstract: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 firefighting helicopter contracts. FOR. SCI. 58(2):130‐138.
Document Type: Research Article
Publication date: April 2, 2012
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- Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
Forest Science is published bimonthly in February, April, June, August, October, and December.
2015 Impact Factor: 1.702
Ranking: 16 of 66 in forestry
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