A Decision Tool to Optimally Select Pollution Prevention Projects within a Constrained Budget.

Abstract

Pollution prevention managers need to select the best environmental projects for an installation within a constrained budget but have no standard way of selecting the optimal mix of projects. This thesis proposes a decision tool to aid decision makers in choosing this optimal mix. The model was built using decision analysis theory which provides a framework to aid the decision maker. Criteria sed in the model for selection was determined using a questionnaire sent to base-level pollution prevention managers. The model ses DPL, a software package designed to build, analyze, and conduct sensitivity analysis of decision problems to perform the quantitative analysis. Built in functions of DPL(TM) allow the decision maker to see the optimal decision policy based on the values entered into the model and to run sensitivity analysis to determine which values are the most critical to the outcome of the model. Decision analysis can be used to create a dominance curve that shows all optimal strategies based on the willingness of the decision maker to make tradeoffs between attributes. This model provides analytical data that can be used to justify decisions made by the pollution prevention manager when selecting the optimal mix of pollution prevention projects for implementation. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1995
Accession Number
ADA303536

Entities

People

  • Charlotte Hudson

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Budgets
  • Case Studies
  • Contractors
  • Economic Analysis
  • Economics
  • Environment
  • Environmental Protection
  • Hazardous Waste
  • Health
  • Law
  • Materials
  • Natural Resources
  • Procurement
  • Standards
  • United States
  • Waste Disposal Facilities

Readers

  • Computational Modeling and Simulation
  • Life Cycle Cost Analysis
  • Organizational Psychology.