Simple Multi-Attribute Rating Technique for Renewable Energy Deployment Decisions (SMART REDD)

Authors

James M. Taylor Jr, The Peter Kiewit Institute, University of NebraskaFollow
Betty Love, University of Nebraska at OmahaFollow

Date of this Version

2014

Document Type

Article

Citation

Published in Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 11:3 (2014), pp. 227–232; doi: 10.1177/1548512914525516

Comments

Copyright © 2014 The Society for Modeling and Simulation International; published by Sage Publications. Used by permission.

Abstract

In the effort to provide electrical power service and the sustaining fuel required to run generators at forward-deployed bases in Afghanistan and Iraq over more than 10 years, the US military spent billions of dollars and a paid a heavy toll in terms of human casualties. The green energy linear program for optimizing deployments (GELPOD) proof-of-concept model showed that a linear program could be used to optimize combat deployment of energy generation systems to minimize cost and casualties. Results indicated that reduction in both cost and casualties for renewable energy sources was highly dependent on fuel cost and deployment length. Neglected in the decision making process, however, were factors that impact the operational success of the mission. When deploying combat units, commanders must not only consider potential costs and casualties, they must also contend with battlefield mobility requirements, maintenance capability (or lack thereof), weather, and anticipated hostile action that could affect operational performance. This paper leverages the simple multi-attribute rating technique (SMART), pioneered by Edwards, to attempt to address this deficiency. The resulting simple multi-attribute rating technique for renewable energy deployment decisions (SMART REDD) model allows commanders to take mission attributes into consideration when making decisions on which energy source is most appropriate for the mission as well as providing information on operations costs, expected transportation requirements, and expected casualties.