A Multicriteria Decision Model Application for Managing Group Decisions

1994 ◽  
Vol 45 (1) ◽  
pp. 47-58 ◽  
Author(s):  
Mark A. P. Davies
2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Marcelo H. Alencar ◽  
Adiel T. de Almeida

This paper proposes a multicriteria decision model based on MAUT (Multiattribute Utility Theory) incorporated into an RCM (Reliability Centered Maintenance) approach in order to provide a better assessment of the consequences of failure, allowing a more effective maintenance planning. MAUT provides an evaluation of probability distributions on each attribute as well as trade-offs involving lotteries. The model proposed takes advantage of such evaluations and it also restructures consequence groups established in an RCM approach into new five dimensions. As a result, overall indices of utility are computed for each failure mode analyzed. With these values, the ranking of the alternatives is established. The decision-maker’s preferences are taken into account so that the final result for each failure mode incorporates subjective aspects based on the decision-maker’s perceptions and behavior.


1976 ◽  
Vol 58 (3) ◽  
pp. 466-474 ◽  
Author(s):  
Steven T. Sonka ◽  
Earl O. Heady ◽  
P. Fred Dahm

Author(s):  
SERAFIM OPRICOVIC ◽  
GWO-HSHIUNG TZENG

In many cases, criterion values are crisp in nature, and their values are determined by economic instruments, mathematical models, and/or by engineering measurement. However, there are situations when the evaluation of alternatives must include the imprecision of established criteria, and the development of a fuzzy multicriteria decision model is necessary to deal with either "qualitative" (unquantifiable or linguistic) or incomplete information. The proposed fuzzy multicriteria decision model (FMCDM) consists of two phases: the CFCS phase - Converting the Fuzzy data into Crisp Scores, and the MCDM phase - MultiCriteria Decision Making. This model is applicable for defuzzification within the MCDM model with a mixed set of crisp and fuzzy criteria. A newly developed CFCS method is based on the procedure of determining the left and right scores by fuzzy min and fuzzy max, respectively, and the total score is determined as a weighted average according to the membership functions. The advantage of this defuzzification method is illustrated by some examples, comparing the results from three considered methods.


2020 ◽  
Author(s):  
Talita Dias Chagas Frazão ◽  
Ana FA Santos ◽  
Deyse GG Camilo ◽  
João FC Junior ◽  
Ricardo Pires Souza

Abstract Background: Multicriteria Decision Analysis is a tool capable of supporting decisions with multiple criteria. Notwithstanding its con rmed value in the health area; so far, no studies have been found to help prioritize victims in the Emergency Medical Service, EMS. Since decision making within EMS involves multiple criteria, it is essential to nd techniques and tools that encompass such elements, as to reduce errors. As to address this gap, the current research developed a multicriteria decision model to help prioritizing victims in the Brazilian EMS, which are still managed as a manual task. Methods: To reach such endeavour, it was formed an expert panel and a discussion group, tasked to de ne the limits of the problem, and to identify the evaluation criteria for choosing a victim, amongst four alternatives derived from clinical and traumatic diseases scenarios of absolute priority in emergency situations occurrences. For prioritization, an additive mathematical method was utilized, aggregating criteria in a exible and interactive version - FiTradeoff. Results: The present work contributed to victims' prioritization by using the multicriteria decision support methodology which led to the identi cation of twenty- ve evaluation criteria to guide the decision. It was noted that the protocols to guide regulating physicians do not consider all the criteria for prioritizing victims in an environment of resource scarcity. In the prioritization simulation composed of four demanding victims and only one available ambulance, the proposed model supported the decision by suggesting the prioritization of Victim 2. Conclusions: From the identi ed improvement points, the developed decision model was able to improve the regulatory action of medical professionals. The elicitation procedure enabled the identi cation of criteria that, albeit well known, were not formalized by the current guidance protocols, which could contribute to contradictions and conicts across the decision chain. Last, but not least, the proposed model could support decision making under the guarantee of a rational and transparent decision-making process that could be applied in other EMS.


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