How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy

Abstract

Decision making is a complex task that involves a multitude of perspectives, constraints, and variables. Multiple Criteria Decision Analysis (MCDA) is a process that has been used for several decades to support decision making. It includes a series of steps that systematically help Decision Maker(s) (DM(s)) and stakeholders in structuring a decision making problem, identifying their preferences, and building a decision recommendation consistent with those preferences. Over the last decades, many studies have demonstrated the conduct of the MCDA process and how to select an MCDA method. Until now, there has not been a review of these studies, nor a proposal of a unified and comprehensive high-level representation of the MCDA process characteristics (i.e., features), which is the goal of this paper. We introduce a review of the research that defines how to conduct the MCDA process, compares MCDA methods, and presents Decision Support Systems (DSSs) to recommend a relevant MCDA method or a subset of methods. We then synthesize this research into a taxonomy of characteristics of the MCDA process, grouped into three main phases, (i) problem formulation, (ii) construction of the decision recommendation, and (iii) qualitative features and technical support. Each of these phases includes a subset of the 10 characteristics that helps the analyst implementing the MCDA process, while also being aware of the implication of these choices at each step. By showing how decision making can be split into manageable and justifiable steps, we reduce the risk of overwhelming the analyst, as well as the DMs/stakeholders during the MCDA process. A questioning strategy is also proposed to demonstrate how to apply the taxonomy to map MCDA methods and select the most relevant one(s) using real case studies. Additionally, we show how the DSSs for MCDA method recommendation can be grouped into three main clusters. This proposal can enhance a traceable and categorizable development of such systems.

MCDA Index Tool: an interactive software to develop indices and rankings

“…At this point, the analyst has to select the preference model to aggregate the input information. Preference models represent the different philosophies of modeling in MCDA, which include (i) scoring functions, (ii) binary relations, and (iii) decision rules (Cinelli et al 2020 ; Słowiński et al 2002 ). CI belong to the first group (i.e., scoring functions), where the decision recommendation consists in a score for each alternative that can be used to rank the alternatives from the best to the worst one.…”

“…This comprehensive evaluation can be reached using methods that belong to the multiple criteria decision analysis (MCDA) domain (Greco et al 2016a ). MCDA is a formal process that supports decision-making by leading the development/identification of the alternatives, the selection of the evaluation criteria (called also indicators) and the aggregation of the preferences of the stakeholders (Bouyssou et al 2006 ; Cinelli 2017 ; Cinelli et al 2020 ). There is a wide and increasing number of MCDA methods (Bisdorff et al 2015 ; Greco et al 2016a ) and a main family is represented by composite indicators (CI), or indices (Diaz-Balteiro et al 2017 ; El Gibari et al 2019 ; Greco et al 2019 ), which lead to a score of the alternatives that can then be easily ranked.…”

MCDM Evaluation of Asset-Based Road Freight Transport Companies Using Key Drivers That Influence the Enterprise Value

“…Often, scientists select which MCDM method to use intuitively. In [14], authors presented a comprehensive taxonomy to support the application of multiple criteria decision analysis (MCDA). Authors distinguish three main phases: problem formulation, construction of the decision recommendation, and qualitative features and technical support.…”

“…The SAW method uses the commonly used additive weighing full-level aggregation tool [14] that encompasses levels of attractiveness of each alternative expressed by normalized values proportionally to weights of drivers. The COPRAS method even uses the same aggregation tool, has a distinct proprietary normalization that automatically deals with the minimizing drivers without the necessity of transforming them to the maximising ones.…”

Análise De Decisão Multicritério Para a Gestão De Concessionárias De Distribuição De Energia Elétrica

“…A pesquisa fundamentou-se na necessidade de identificação do fluxo de informação para as tomadas de decisão nas concessionárias energéticas.3 Métodos e técnicas de pesquisa O presente trabalho é uma pesquisa aplicada, descritiva e quali-quantitativa. O procedimento técnico utilizado foi a modelagem multicritério, proveniente da Pesquisa Operacional, a qual prevê alguns passos: i) Definição do problema; ii) Definição de critérios; iii) Definição de alternativas; iv) Definição dos parâmetros do modelo; v) Definição do método a ser utilizado; vi) Aplicação do método; vii) Análise de sensibilidade(Guarnieri, 2015;Cinelli et al, 2020). A coleta de dados, que na modelagem multicritério é denominada 'elicitação', e ocorre a fim de verificar a percepção dos decisores e o julgamento dos valores das alternativas mediante um conjunto de critérios, ocorreu por meio de entrevista com 4 gestores de superintendências da empresa ANEEL que atuam na regulação de concessionárias de energia elétrica, conforme descrito no Quadro 2.…”