A multiple criteria decision model with ordinal preference data

1991 ◽  
Vol 54 (2) ◽  
pp. 191-198 ◽  
Author(s):  
Wade D. Cook ◽  
Moshe Kress
Author(s):  
Vishal Mahale ◽  
Jayashree Bijwe ◽  
Sujeet Sinha

Good friction materials should satisfy diverse and contradictory performance requirements such as adequate friction ( µ ≈ 0.35–0.45), resistance to wear, fade, squeal, judder, etc. in consort with good recovery and less noise producing tendency. To achieve center point of all these conflicting criteria and selection of best overall performing friction material is multiple criteria decision making (MCDM) problem and very difficult task. Decision maker can easily make decision with single criteria without the help of any optimization tool by maximizing beneficial criteria and minimizing non-beneficial criteria. However, it is extremely challenging task if decision making involves several number of conflicting criteria. Few techniques are reported in the literature such as ‘multiple criteria decision model’, ‘Multi-attribute decision model’, ‘extension evaluation method’ (EEM), etc. for performance ranking of friction materials. However, the simplicity, reliability, applicability, time devoted for the analysis, etc. are always most important aspects of selecting a right tool for the analysis. In this paper application of a technique ‘multiple objective optimization on the basis of ratio analysis’ (MOORA) has been first time employed for performance ranking of friction materials. A comparative study of MOORA and currently used methods MCDM and EEM are also presented. MOORA proved to be the best tool based on the criteria such as simple to use, fast, flexible, and efficient one.


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.


1993 ◽  
Vol 4 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Kin-Nam Lau ◽  
Gerald Post ◽  
Karuna Selvaraj

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