JOURNAL ARTICLE
A Decision Framework for Evaluating the Rocky Mountain Area Wildfire Dispatching System in Colorado.
Published In: Decision Analysis (INFORMS), 2023, v. 20, n. 4. P. 276 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Belval, Erin J.; Thompson, Matthew P. 3 of 3
Abstract
This article focuses on the multiyear effort by the Rocky Mountain Coordinating Group (RMCG) and the Rocky Mountain Area Fire Executive Council (RMA-FEC) to reorganize Colorado’s wildfire dispatching system in response to increasing wildfire activity and associated challenges. Using a structured decision-making framework called PrOACT (Problem, Objectives, Alternatives, Consequences, Trade-offs), the project engaged multiple agencies and stakeholders to redefine the problem beyond recruitment and retention issues, develop clear objectives, generate and evaluate alternatives for dispatch zone boundaries and center locations, and apply an integer programming model to identify efficient solutions. The decision-makers ultimately selected a three-zone dispatch system with centers in Grand Junction, Fort Collins, and Colorado Springs, balancing employee well-being, operational efficiency, and cost considerations. The study highlights the value of structured decision analysis, stakeholder involvement, and visualization tools in addressing complex, multiagency wildfire management challenges and suggests the approach may be applicable to similar efforts elsewhere in the United States.
Additional Information
- Source:Decision Analysis (INFORMS). 2023/12, Vol. 20, Issue 4, p276
- Document Type:Article
- Subject Area:Forestry
- Publication Date:2023
- ISSN:1545-8490
- DOI:10.1287/deca.2022.0047
- Accession Number:174178969
- Copyright Statement:Copyright of Decision Analysis (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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