Multicriteria Decision Aid Research Articles

Improving on Half-Lightweight Male Judokas' High Performance by the Application of the Analytic Network Process.

Judo is a multifactorial sport where many variables or key performance indicators (KPIs) such as force-velocity profile, bioenergetic capacity, technical and tactical skills, and cognitive and emotional competence play a role and influence the final result. While there have been many academic studies of these variables, usually in isolation, none have examined KPIs holistically or analyzed their impact on strategic performance. The main objective of the present study, therefore, is to apply a novel and easily replicable methodology to identify and prioritize the main KPIs affecting performance in professional judo. Such a methodology was applied to the High-Performance Judo Centre of Valencia, using a multi-criteria decision aid technique: the analytic network process (ANP). The ANP is used to identify and quantify cause and effect relationships not only between KPIs but also between KPIs and performance objectives. Further, the ANP offers effective results when there is a lack of historical KPI data, because it is based on experts' opinions and judgments. A judo expert group (JEG) comprising elite judo coaches and half-lightweight (under 66 kg) male judokas applied the methodology to discriminate between the characteristics required when aiming to reach high-level strategic objectives (such as participating in the Olympic Games or winning a medal in a Grand Slam/Gran Prix). The JEG, which consisted of five elite judokas, national and international champions, and three Olympic coaches—including a former European champion and silver medalist in a world championship—provided high added value to the study. The main findings were that the KPIs that made the most difference were mostly psychological, specifically motivation, stress, and team cohesion. Of these, motivation was by far the most important KPI for success in our case study, so judokas should make sure that they analyze it properly. Motivation is usually intrinsic to the competitor and should be maintained at a high level, not only during tournaments but also during daily training and lifestyle activities. Physical and other specific forms of training, as well as lifestyle KPIs, are very important for the elite competitor but are not sufficient to reach high-level objectives. The most important of these KPIs were Kumi-Kata (grip work), dual career, focus and concentration level, scouting, nutrition, and basic technique. Power and strength were the most important physical KPIs. In general, these are essential for meeting strategic objectives, but they do not make the crucial difference. This suggests that professional psychological support should be provided in daily training and that international team composition and internships should be fostered.

The use of the success‐based decision rule to support the holistic evaluation process in FITradeoff

AbstractThe flexible and interactive tradeoff (FITradeoff) method is a multi‐criteria decision‐making/aiding (MCDM/A) method in the context of the multi‐attribute value theory. The implementation of the FITradeoff method is supported by a decision support system (DSS), which includes an elicitation procedure for scaling constants with the flexibility of including a holistic evaluation. The holistic evaluation has graphics that can be used to assist decision‐makers (DMs) during the FITradeoff decision process. In this context, three neuroscience experiments have been conducted to investigate how a DM uses the graphics to evaluate an MCDM/A problem. Thus, the main contribution of this paper is to propose the success‐based decision rule. Based on this rule, recommendations are made concerning the holistic evaluation. The first is that this rule can assist the analyst to advise DMs with information on the visualization's probability of success. Second, this rule suggests improvements that can be made to the design of the FITradeoff DSS.

Multi-criteria approach to stochastic and fuzzy uncertainty in the selection of electric vehicles with high social acceptance

In recent years, a number of legal solutions have been adopted in Poland aimed at reaching the number of 1 million electric cars in use in 2025. Therefore, an important research issue is the assessment of electric vehicles available on the Polish market and the identification of vehicles that meet consumers' expectations to the greatest extent. The assessment of electric vehicles is a multi-criteria issue, characterized by a number of uncertainties related to their operational parameters, as well as the preferences of individual users and, more broadly, the preferences of society. A novelty of the article is the application of the fuzzy multi-criteria decision aid method called NEAT F-PROMETHEE (New Easy Approach To Fuzzy PROMETHEE), combined with the Monte Carlo method and elements of the SMAA (Stochastic Multicriteria Acceptability Analysis) method for the assessment of vehicles under uncertainty conditions. The theoretical contribution of this research therefore includes the synthesis of a fuzzy and stochastic approach to decision-making support, supported by outranking and incomparability relationships. Such a set of approaches to uncertainty makes it possible to improve the accuracy of decision-making, because the approaches indicated verify each other's results. Moreover, the application of the NEAT F-PROMETHEE method has solved the problem of evaluating electric vehicles from the perspective of a single decision-maker, while the combination of NEAT F-PROMETHEE and the stochastic approach has made it possible to simulate preferences of the society. Although there are many uncertainties in the decision-making problem, the approach has allowed to identify almost unambiguously the electric vehicle that is likely to gain the highest acceptance. As a result of the conducted research it was found that the approaches to uncertainty based on fuzzy sets, outranking relations and stochastic analysis complement each other, allowing the decision-maker to conduct a wider analysis of the imprecision of the obtained solution.

Chemical product design integrating MCDA: Performance prediction and human preferences modelling

AbstractComputation‐based techniques and modelling of human knowledge and preferences by using multi‐criteria decision aid (MCDA) methods are integrated in a multi‐scale and multi‐disciplinary approach for the chemical product and process design. The proposed methodology has four main stages: (a) construction of a molecular model for predicting product performance, (b) validation of product performance, (c) selection of alternatives integrating preferences of manufacturers and consumers, and (d) process optimization implementing MCDA methods. The methodology is oriented to find new products that can replace components of formulations, whose performance is already known. It was applied to an exploratory study about the use of glycerol as raw material to produce plasticizers for polyvinyl chloride (PVC), replacing 2‐ethylhexyl phthalate (DEHP). The results of the case study and the proposed process design offer promising prospects regarding their application in other chemical products.

Performance evaluation system to support the coordination of bidding processes management in a University Hospital

Purpose: The purpose of this research is to structure a model to support the management of the Coordination of Bidding Processes at the Professor Polydoro Ernani de São Thiago University Hospital of Universidade Federal de Santa Catarina (Federal University of Santa Catarina). The main purpose is to build, in its manager, knowledge about the management context, allowing the visualization of the aspects deemed as necessary and sufficient to be taken into account in this process.
 Methodology: The Multicriteria Decision Aid - Constructivist (MCDA-C) methodology is used to structure a specific performance evaluation system considering the particularities of the context and its manager. Thus, the research uses a qualitative approach with primary data collection, in the form of a case study, based on interviews with the manager of the Coordination.
 Results: The structuring of the system allows the development of a hierarchical structure of value and qualitative performance indicators. The development of performance indicators enables the effectively monitoring of activities developed by the sector, leading to efficient management and better application of public resources. It also allows the visualization of aspects at compromising, market and excellence levels, highlighting the relevant factors for management.
 Contributions of the Study: The study contributes to filling the demonstrated gap based on the analysis of the literature on the topic. The system is considered to provide the manager with specific information to make decisions and promotes a discussion, not yet found in the literature, regarding the use of constructivist approaches for the bidding sector in a university hospital.

Assessment of decision-making method

The method presented in Multi Criteria Decision Aiding (MCDA) focuses on the formulation of choice and is expressed by decision-making law "if…then…. " Basic premise facts as well as the quest for clear rules to encourage its judgements. This approach has a significant benefit in handling contradictions as a consequence of hesitations. The method suggested is based on the fundamental, natural and logical concept of dominance. It states something else in a particular family, and then it in general, according to all requirements. The gathering of judgements based on preferred facts using an information discovery method adapted to reflect the dominance principle. In order to demonstrate how Dominance Rough Set Approach (DRSA) may be used clearly in various MCDA situations, the authors’ Dominances-based Rough Set Methodius acts as a mathematical basis for the MCDA decision-making approach, along with teaching examples.

Development of performance indicators for Occupational Health and Safety: a constructivist multicriteria approach for PPE

Abstract Paper aims This study aims to develop indicators, using the Multi-Criteria Decision Aid - Constructivist methodology (MCDA-C), to assess the occupational prevention activities in a psychiatric hospital. Originality The application to the health sector of MCDA-C, in a case study during the supply of medicines to patients. Research method We use the ProKnow-C. Then, MCDA-C was applied in a case study. Main findings Four interest areas were defined, then the critical situations were identified to construct 126 specific indicators and finally the intervention strategies were defined. 26 indicators focused on PPE, 4 lagging and 22 leading. The model enabled the mapping of performance indicator levels. For that, 48 recommendations, and intervention strategies for each situation were defined. Implications for theory and practice The use of specific indicators, obtained through a participatory process, allowed the identification of accurate and reliable metrics to measure the actual performance of activities, critical situations, and strategies.

A net-flow based approach to detect outliers in multicriteria decision problems

Detecting outliers in multicriteria decision aid (MCDA) field is a new research direction that has not been enough explored. This paper tackles this problem by proposing a statistical approach based on PROMETHEE method net-flow. To consider the multicriteria character of the problem, the net-flow of each object is computed by applying PROMETHEE method. According to the normal distribution of the net-flow values, the standard deviation (SD) or interquartile range (IQR) statistical methods are used to detect outliers. To prove its applicability, the proposed approach is evaluated on real life problem.

ELICITATION OF CRITERIA WEIGHTS FOR MULTICRITERIA MODELS: BIBLIOMETRICS, TYPOLOGIES, CHARACTERISTICS AND APPLICATIONS

Goal: This study aims to review and classify the main methods of criteria weights definition.
 Design / Methodology / Approach: From a systematized search were articles selected and analyzed, covering a timeline between 1949 and 2020.
 Results: Fifty sex methods were identified and discussed. Most (49) are subjective, eleven are objectives and three are hybrids. Considering the aggregation procedure, 33 are compensatory and 23 are non-compensatory. Most of the methods are for a single decision maker and just eleven are multidecision makers. The main contributions are published on European Journal of Operational Research journal.
 Limitations of the investigation: This work does not make a thorough technical analysis of the presented methods and it is not a notebook to indicate to researchers which method is the best for each type of situation. Some methods may not have been included.
 Practical implications: The selection and the definition of criteria weight is a fundamental question in the Multicriteria Decision Aid (MDCA) approach. The MCDA has a wide application on operation and production management. The diversity of published papers on this subject, the comprehension of its characteristics and applicability is a complex task for researchers and professionals who demand these techniques.
 Originality / Value: This work contributes to identify the state of the art of the models applied to the weight of the criteria, as well as to identify its characteristics and functionality regarding MCDA modeling.

An intuitionistic fuzzy extension of the CODAS-SORT method

Currently, an important issue in multi-criteria decision-making (MCDM) problems are vagueness and lack of precision of decision- -making information because of insufficient data and incapability of the decision maker to process the information. Intuitionistic fuzzy sets (IFS) are a solution to eliminate the vagueness and the uncertainty. While fuzzy sets (FS) deal with ambiguity and vagueness problem, IFSs have more advantages. Moreover, the CODAS-SORT method cannot handle the uncertainty and ambiguity of information provided by human judgments. The aim of this study is to develop an IF extension of CODAS-SORT combining this method with the IFS theory. To achieve this, we use the fuzzy weighted Euclidean distance and fuzzy weighted Hamming distance instead of the crisp distances. A case study of a supplier selection assessment is used to clarify the details of our proposed method. Keywords: multicriteria decision aid, sorting methods, CODAS-SORT, intuitionistic fuzzy set.

Using Constructivist Multi Criteria Decision Aid to Evaluate the Workforce Sizing in Public Organizations: The case of a Brazilian University

Using Constructivist Multi Criteria Decision Aid to Evaluate the Workforce Sizing in Public Organizations: The case of a Brazilian University

MULTI-CRITERIA DECISION SUPPORT TO CRIMINOLOGY BY GRAPH THEORY AND COMPOSITION OF PROBABILISTIC PREFERENCES

This study associates graph theory and a multi-criteria decision aid technique, presenting a different process for doing the investigation of criminal networks. In the criminal subject, privacy concerns limit identification. For this reason, the database composed of 110 actors, involving criminals and peripheral characters to the network, was identified by numbers, without names and penalties. The discrimination of critical actors in criminal networks can help law enforcement officers to conduct a more detailed investigation for their disruption. Communication between drug traffickers was transformed into different centrality indices for each actor in their social network. Centralities and actors compose a decision matrix, analyzed by the Composition of Probabilistic Preferences to identify the most relevant actors in the criminal network. Results indicated that the five actors highlighted in the real investigation have a clear distinction of importance in the network, which in a way have been ratified.

A PROMETHEE II-BELIEFapproach for multi-criteria decision-making problems with incomplete information

Multi-criteria decision aid methods consider decision problems in which many alternatives are evaluated on several criteria. These methods are used to deal with perfect information. However, in practice, it is obvious that this information requirement is too strict. In fact, the imperfect data provided by more or less reliable decision makers usually affect decision results, since any decision is closely linked to the quality and availability of information. In this paper, a PROMETHEE II-BELIEF approach is proposed to help multi-criteria decisions based on incomplete information. This approach solves problems with incomplete decision matrix and unknown weights within PROMETHEE II method. On the basis of belief function theory, our approach first determines the distributions of belief masses based on PROMETHEE II’s net flows, and then calculates weights. Subsequently, it aggregates the distribution masses associated with each criterion using Murphy’s modified combination rule in order to infer a global belief structure. The final alternative ranking is obtained via pignistic probability transformation. A case study of a real-world application concerning the location of a treatment center of waste from healthcare activities with infectious risk in the center of Tunisia is studied to illustrate the detailed process of the PROMETHEE II-BELIEF approach. Keywords: multiple criteria aid, incomplete information, PROMETHEE II method, belief function theory.

Multicriteria decision aid approach for sorting countries based on their energy performance

Multicriteria decision aid approach for sorting countries based on their energy performance

Multicriteria decision aid approach for sorting countries based on their energy performance

Climate change has led countries to alter their energy policies, pave way for greener environment, and enhance their performance regarding their energy sustainability. Energy sustainability is one ...

Evaluating the Performance of NIST’s Framework Cybersecurity Controls Through a Constructivist Multicriteria Methodology

This paper aims to show how creating a risk plan can be solved with the help of the constructivist multicriteria method. A case study using Multicriteria Decision Aid Constructivist (MCDA-C) was applied, with cybersecurity framework’s controls as a reference. The study was conducted in a large Brazilian bank in Brazil. The relevance of this work is the need to show that the application of multicriteria methods can be applied in the context of information security, which recommends the use of such methods to assist in risk analysis. The methodology used in this study was both quantitative and qualitative, obtaining primary data through brainstorming with decision-makers and forms answered by experts. The secondary data were obtained through the Framework for Improving Critical Infrastructure Cybersecurity, created by NIST - the National Institute of Standards and Technology of the United States. The problem was structured according to the constructivist method, and the data collected were processed and calculated. The study concluded that the category of Security Continuous Monitoring controls stood out compared to other categories. It also shows the importance of applying the constructivist method for the management of cyber risks by unravelling a problem and providing a basis for decision making. Our work contributes to a better understanding of risk management, encouraging the adoption of the constructivist method as a form of risk management best practice.

The multicriteria group decision making FlowSort method under uncertainty

Crisp values are insufficient to model real-life situations and imprecise ideas are frequently represented in multicriteria decision aid analysis. In fact, it is difficult to treat the evaluation criteria precisely and to fix exact preferences rating. The triangular intuitionistic fuzzy numbers succeeded to treat this kind of ambiguity in a great deal of research than other forms of fuzzy representation functions. The field of sorting issues is an active research topic in the multiple criteria decision aid (MCDA). This study extended one of the sorting methods, FLOWSORT, for solving multiple criteria group decision-making problems. This extension described the preferences rating of alternatives as linguistic terms which can be easily expressed in triangular intuitionistic fuzzy numbers. To validate our extension, an illustrative example as well as an empirical comparison with other multi-criteria decision making methods is presented. Keywords: multicriteria group decision making, sorting problematic, intuitionistic fuzzy set, FlowSort method

APPLICATION OF THE ELECTRE TRI METHOD FOR SUPPLIER CLASSIFICATION IN SUPPLY CHAINS

ABSTRACT The present research aims to propose a Supplier Selection Model that creates collaborative relationships in Supply Chains, so that suppliers can be previously categorized into the cooperation, the coordination and the collaboration levels. Applied, quantitative, exploratory and descriptive research methods were used. A bibliographical research, a questionnaire, and the quantitative modeling were adopted as methodological procedures. The managers responsible for the Supplier Selection process in the Brazilian Wind Energy companies participated in this research. First, a criteria framework for the Selection of Supplier of goods and services was developed. Second, a Multicriteria Decision Aiding Model was created and validated, enabling the classification of the suppliers according to the relationship levels in a systematic way in terms of their performance from a set of criteria by implementing the ELECTRE TRI method. Firms can use the Model periodically in order to revise the supplier assessment and, if needed, direct them to either an inferior or superior class.

Implementation of the new easy approach to fuzzy multi-criteria decision aid in the field of management

Decision-making is one of the most important management functions and a critical task for managers. The tools that support decision makers in making decisions are Multi-criteria Decision Making/Aid/Analysis (MCDM/MCDA) methods. Since most decisions are made under conditions of uncertainty, the fuzzy MCDM/MCDA methods are particularly important as they allow capturing the uncertainty and imprecision of the information used in making decisions. This method is the Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy PROMETHEE), and its extension in the form of New Easy Approach to Fuzzy PROMETHEE (NEAT F-PROMETHEE). However, the unavailability of software using the NEAT F-PROMETHEE method significantly reduces its ease of use and may discourage potential users and researchers considering using it in their studies. Therefore, to facilitate the use of this MCDA method, the article presents the implementation of NEAT F-PROMETHEE in the MATLAB environment. Moreover, the verification of the developed implementation and its application in the management decision-making problem is presented, together with the analysis of the operation of the mapping correction function used in NEAT F-PROMETHEE. The results obtained with NEAT F-PROMETHEE were compared with the results of the Fuzzy PROMETHEE method which did not apply correction. The analysis shows that the correction applied in NEAT F-PROMETHEE allows obtaining results with a smaller error than the non-corrected implementations of PROMETHEE Fuzzy. Therefore, a more accurate solution of the decision problem is obtained.•improving the process of mapping fuzzy numbers in the Fuzzy PROMETHEE method•implementing a correction mechanism while mapping trapezoidal fuzzy numbers

Introducing Sentiment Analysis of Textual Reviews in a Multi-Criteria Decision Aid System

Nowadays, most decision processes rely not only on the preferences of the decision maker but also on the public opinions about the possible alternatives. The user preferences have been heavily taken into account in the multi-criteria decision making field. On the other hand, sentiment analysis is the field of natural language processing devoted to the development of systems that are capable of analysing reviews to obtain their polarity. However, there have not been many works up to now that integrate the results of this process with the analysis of the alternatives in a decision support system. SentiRank is a novel system that takes into account both the preferences of the decision maker and the public online reviews about the alternatives to be ranked. A new mechanism to integrate both aspects into the ranking process is proposed in this paper. The sentiments of the reviews with respect to different aspects are added to the decision support system as a set of additional criteria, and the ELECTRE methodology is used to rank the alternatives. The system has been implemented and tested with a restaurant data set. The experimental results confirm the appeal of adding the sentiment information from the reviews to the ranking process.