A Fuzzy TODIM Approach for the Supplier Selection Problem

Abstract

Supplier evaluation and selection is a kind of problem which includes multiple criteria of qualitative and quantitative properties. From different alternatives it requires to find the best option using different criteria and opinions of the decision makers. Because of the judgments or the bias of the decision makers, sometimes classical methods cannot be precise. In this paper, a relatively new decision method called TODIM (an acronym in Portuguese for iterative multi-criteria decision-making -"Tomada de Decisão Iterativa Multicritério") is improved with fuzziness to prevent the above mentioned problems of the classical methods. A real life case study for a furniture manufacturing company is also be solved.

Managing Non-Homogeneous Information and Experts’ Psychological Behavior in Group Emergency Decision Making

“…It has been widely applied to solve different decision problems [38,39]. To cope with complex problems and uncertain information in the real world, the TODIM method has been extended to deal with fuzzy MCDM problems [16,17].…”

“…In our proposal, we will use fuzzy TODIM method [16][17][18] based on prospect theory [14] because of its advantage and capability of capturing the experts' psychological behavior under fuzzy environment.…”

“…3. To take into account experts' psychological behavior by means of the fuzzy TODIM method [16][17][18] based on prospect theory [14]. In the real world, it is common that experts with different background and knowledge might have different attitudes or opinions over different alternatives concerning different criteria.…”

“…To take into account experts' psychological behavior by means of the fuzzy TODIM method [16][17][18] based on prospect theory [14].…”

An Effective Hybrid Differential Evolution Algorithm Incorporating Simulated Annealing for Joint Replenishment and Delivery Problem with Trade Credit

“…We intend to design hybrid algorithms by taking advantage of the aforementioned algorithms in handling more complex JRDs. Moreover, decision makers often have to face vague or imprecise operational conditions in reality [28][29]. It is interesting to consider stochastic or fuzzy demand for handling more practical JRDs under uncertain supply chain environment in the network economic era [30][31][32].…”