Generalized TODIM by Incremental Analysis and Its Extension

• Main Authors: Yuan-Sheng Lee, 李元生
• Other Authors: Hsu-Shih Shih
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/74702566709101719483

博士 === 淡江大學 === 管理科學學系博士班 === 104 === This study aims to generalize TODIM (an acronym in Portuguese of Interactive and Multi-Criteria Decision Making) by incremental analysis (IA) for a risky decision making and extending it to a group decision-making environment. For generalization, two types of scaling effects are overcome. One effect is due to the defect of original TODIM formulation in losses part of the two parts value function, and the criteria with smaller weights will contribute larger dominance values in the computing process. The other effect is derived from aggregating partial dominance measurements among different characteristics of criteria in TODIM while the criteria can be divided into two categories, benefits and costs, for effective resource allocation. In such a way, different types of value functions are considered for reflecting the decision maker’s risk preference. IA is then employed to rank alternatives according to the given cutoff benefit-cost ratio for two accumulated dominance measurements. A fuel buses example is illustrated. The proposed method is extended to a group decision making environment in which multiple decision makers (DMs) execute the model. The ordinal rank of each DM can be converted to cardinal value through rank sum and regression-like function. In addition, the relative decision power of each DM is taken into account based on the eigenvector of their ranking distance comparison matrix. Later, sensitivity analyses are employed on the different values of the parameters in the value function, the cutoff benefit-cost ratios, and different weights of DMs to demonstrate the robustness of the proposed model for group decision. Furthermore, a number of other MCDM techniques have been compared. The results show that the proposed model is feasible and effective for the demonstrated example under risk.