Comparison of first aggregation and last aggregation in fuzzy group TOPSIS

The Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), one of the major multi attribute decision making (MADM) techniques, ranks the alternatives according to their distances from the ideal and the negative ideal solution. In real evaluation and decision making problems, it is vital to involve several people and experts from different functional areas in decision making process. Also under many conditions, crisp data are inadequate to model real-life situations, since human judgments including preferences are often vague and cannot estimate his preference with an exact numerical value. Therefore aggregation of fuzzy concept, group decision making and TOPSIS methods that we denote “fuzzy group TOPSIS” is more practical than original TOPSIS.     

Using TODIM to evaluate green supply chain practices under uncertainty

This study approaches the problem of exploring the importance and performance levels in green supply chain practices (GSCP) under conditions of uncertainty. This contribution creates a mechanism that assists in the process of analyzing and selecting the alternatives aligned with the proposed criteria on both the qualitative and quantitative scales. This analysis uses the acronym from the interactive and multi-criteria decision-making method (known as TODIM in Portuguese), a discrete multi-criteria method based on prospect theory. However, the criteria weights and alternatives are described as linguistic preferences and involve quantitative data. The qualitative preferences transformed into crisp values and the quantitative data converts into comparable scale. The results indicate the discrepancies between the importance and performance levels of the GSCP. This study identified set of principal criteria that can influence the recommendations for strategic direction in the most systematic and wide-ranging manner.     

Application of fuzzy optimization to a supply chain network design: A case study of an edible vegetable oils manufacturer

This study applies fuzzy sets to integrate the supply chain network of an edible vegetable oils manufacturer. The proposed fuzzy multi-objective linear programming model attempts to simultaneously minimize the total transportation costs. The first part of the total transportation costs is between suppliers and silos; and rest one is between manufacturer and warehouses. The approach incorporates all operating realities and actual flow patterns at production/distribution network with reference to demands of warehouses, capacities of tin and pet packaging lines. The model has been formulated as a multi objective linear programming model where data are modeled by triangular fuzzy numbers. Finally, the developed fuzzy model is applied for the case study, compiled the results and discussed.     

Selecting IS personnel use fuzzy GDSS based on metric distance method

In this paper we propose a new approach to rank fuzzy numbers by metric distance. For showing our method is a good ranking method, we give two examples to compare with other methods. The paper also developes a computer-based group decision support system, FMCGDSS, to increase the recruiting productivity and to easily compare our method with other fuzzy number ranking methods. The FMCGDSS includes three ranking methods: intuition ranking, Lee and Li's fuzzy mean/spread and our metric distance method to help manager make better decision under fuzzy circumstance. The result indicates that the new method is coincident with the intuition ranking and the Lee and Li's fuzzy mean/spread method on each type weight.