A multi-criteria decision making for prioritizing potential alternatives truck management strategies

The objective of this dissertation is to develop a decision-making framework for prioritizing potential alternatives of truck management strategies using Multi-Criteria Decision-Making (MCDM) method. The motivation is drawn from the need for investigating and evaluating all likely impacts, resulting from the implementation of alternative truck strategies. The conventional evaluation methods such as the cost-benefit analysis can be addressed impacts involving monetary costs, but we believe these are insufficient to investigate all likely impacts. Our decision-making framework is developed to deal with all impacts that can transformable and non-transformable into monetary costs as well as to reflect decision-makers judgments. Two main objectives of this study are accomplished. The first is to explore all likely impacts, resulting from the implementation of alternatives truck management strategies, by performing a specific case study of before and after cases using traffic simulation models. A key feature of this part is to analyze various performance measures. They include both measures that can transformable and non-transformable into monetary costs as well as can reflect the standpoints of the public and the private sectors. Secondly, our framework is developed based on the Analytical Hierarchy Process (AHP), one of popular multi-criteria decision-making (MCDM) methods. This method enables the judgments and preferences of decision-maker to be quantified based on the relative importance of their own criteria, and to allow a quantitative interpretation from others. Another important contribution is to develop a 100-score conversion formula, a standard normalization technique. Since quantitative measurements have different scales, we need to incorporate these measurements into a single value. The formulas allow decision-makers to facilitate comparisons across potential alternatives. Final decision scores can be produced by multiplying the sum of scores of sub-criteria by estimated weight of the criteria. We believe that these final scores provide the argument to prioritize potential alternatives.