A New Multiple Criteria Decision Making Methodology for Environmental Decision Support

Public sector planning and management problems are challenging and complex. Specifically, decision makers (DMs) responsible for environmental control, policy and management problems are faced with a vast array of alternatives from which one must be identified for implementation. In addition to cost-effectiveness, consideration must to be given to public health, social acceptability, political feasibility, equity among all affected parties and environmental performance such as emissions and energy consumption. The first objective of this dissertation research was to develop a Multiple Criteria Decision Making (MCDM) method to help a DM identify the best compromise solution. Improved techniques for characterizing preferences and incorporating them into decision analysis were demonstrated. The second objective was to model and analyze a real public sector planning case study. A large-scale solid waste management (SWM) planning case study was conducted for the Delaware Solid Waste Authority (DSWA) using a multiobjective optimization modeling approach. An array of SWM strategies for the State of Delaware was generated and analyzed considering cost, environmental emissions and energy consumption. The third objective was to demonstrate the applicability of the new MCDM method for the Delaware case study by working with a decision maker. Based on the illustrative case study with a single DM, the MCDM method was found to be sufficiently flexible overall to adapt to the decision making process. The machine learning algorithms used in this procedure were shown to successfully generate appropriate decision rules and capture the preference information. The decision rules were verified to reflect the implicit preferences of the DM. The DM indicated that the experience was pleasant and useful, suggesting that the DM would be receptive to using this approach for other problems and would encourage others to utilize this MCDM approach.