Multiple Criteria Decision Aid Research Articles

A DEA-PROMETHEE approach for complete ranking of units

Data envelopment analysis (DEA) and multiple criteria decision aid (MCDA) are two well-known approaches to rank so-called decision-making units (DMUs) or alternatives. In this contribution, a two-step model is presented to completely rank units according to multiple inputs and outputs. In the first step, DEA is applied between each pair of DMUs independently to generate a pairwise comparison matrix. In the second step, the obtained matrix is exploited by means of Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) to completely rank units. We show the compatibility between the resulting ranking of DEA and DEA-PROMETHEE methods while there exist just one input and one output. We also discuss the monotonicity property of the method. We compare DEA-PROMETHEE with an integrated DEA-AHP approach on a numerical example.

Multicriteria decision aid for agile methods evaluation using fuzzy PROMETHEE

AbstractThe last decade is marked by the blossom of agile methods and their growing use in many IT projects. They shared common principles and values, such as customer collaboration, early and continuous delivery of releases, accept requirements changing, and other common principles advocated by the agile manifesto since 2001. Meanwhile, there are significant differences among the practices adopted by the agile methods to fulfill agility's principles and values. The differences could be interpreted in terms of how an agile method judges the relative importance of such principles or its orientation to a specific project viewpoint (organizational, technical, or conceptual) rather than others. This fact leads to a necessity of a deep evaluation of agile methods to choose the best‐fit method suited for a project. Moreover, vague and uncertain information used to evaluate those lightweight methodologies could represent an additional difficulty for decision makers (DM). To drawback this problem, the multiple criteria decision aid (MCDA) method fuzzy PROMETHEE is proposed to assess agile methods with regard to different conflicting criteria and handling besides uncertainty in decision making. Furthermore, a sensitivity analysis is provided to validate results and verify their stability.

Spatial multicriteria decision support for robust land-use suitability: The case of landfill site selection in Northeastern Greece

Multicriteria spatial decision support systems (MC-SDSS) have emerged as an integration of geographical information systems (GIS) and multiple criteria decision aid (MCDA) methods for incorporating conflicting objectives and decision makers’ preferences into spatial decision models. In this paper, we present spatial UTASTAR (S-UTASTAR), a raster-based MC-SDSS for land-use suitability analysis. The multicriteria component of the system is based on the UTA-type disaggregation-aggregation approach. S-UTASTAR is applied in a raster-based case study concerning land-use suitability analysis to identify appropriate municipal solid waste landfill (MSW) sites in Northeast Greece. Moreover, robustness analysis tools are implemented to guarantee robust decision support results. More specifically, during the aggregation phase, the Stochastic Multiobjective Acceptability Analysis (SMAA) is used to indicate the frequency at which a site achieves the best ranking positions within a large set of alternative landfill sites.

On the increasing importance of multiple criteria decision aid methods for portfolio selection

In 1952, Markowitz published his famous paper on portfolio selection that transformed the field of finance. Although over 65 years have passed since then, the mean-variance model remains today the predominant model in portfolio selection. Having endured many criticisms over this period, the one that has perhaps been the most persistent is the fact that mainstream mean-variance theory is unable to accommodate additional criteria beyond expected return and variance. With investment decision-making having become more complex, this is a real problem as many problems with additional criteria exist and are only increasing in number and importance. In this paper, we review the papers that have been published that apply methods and procedures in an exact (as opposed to evolutionary) sense to address problems in portfolio selection with criteria beyond mean and variance. We also analyse the methodologies that allow the solution of the problem in a multiple criteria context, thus extending the features of the mean-variance approach that have caused portfolio theory to have such impact.

Fishery policy in the European Union: A multiple criteria approach for assessing sustainable management of Coastal Development Plans in Southern Italy

Fishery planning and management require environmental, social, and economic assessment that should take into account multiple sustainability criteria as well as the preferences and priorities of decision-makers involved in these complex issues. Therefore, this research aims to identify in a straightforward way the most sustainable coastal development plan (CDP) among a set of several CDPs and to investigate in depth the effective sustainable use of the public fishery's fund among the considered CDPs. This is accomplished by implementing a multiple criteria decision aiding (MCDA) framework that has been tested by six fisheries' local action groups (FLAGs) CDPs in a territorial system strongly affected by fishery activities in the Apulia region of Southern Italy. In particular, the framework was classified into 12 criteria grouped into three sustainability dimensions according to the actions identified by the CDPs; the data collection was carried out by consulting the CDPs. After collecting the data, the MCDA framework was applied by involving the regional consultant on fishery affairs.An analysis of the results clearly showed that the best performances in the distribution of the available funding were related to the coherence of the CDPs with specific environmental, social, and economic features. The proposed MCDA framework can represent support for decision-makers and stakeholders to consider and evaluate in a systematic and robust way different possibilities for funding distribution to detect optimal alternatives within European fishery policies.

On the Choquet multiple criteria preference aggregation model: Theoretical and practical insights from a real-world application

We consider the use of the Choquet integral for evaluating projects or actions in a real-world application starting from the case of the re-qualification of an abandoned quarry. Despite the Choquet integral being a very well-known preference model for which there is a rich and well developed theory, its application in a multiple criteria decision aiding perspective requires some specific methodological developments. This led us to work out and implement in practice two new procedures: a first procedure to build interval scales with the objective of assigning utility values on a common scale to the criteria performances, and a second one to construct a ratio scale for assigning numerical values to the capacities of the Choquet integral. This article discusses the strengths and weaknesses of the Choquet integral as appearing in the case study, proposing as well insights related to the interaction of the experts within a focus group.

GAIA-SMAA-PROMETHEE for a hierarchy of interacting criteria

In this paper, we propose an extension of the PROMETHEE methods. Despite PROMETHEE are among the most applied methods in Multiple Criteria Decision Aiding (MCDA), some drawbacks can be underlined with respect to their applicability since they do not take into account few characteristics of multicriteria decision problems that are very relevant in real world applications: (i) robustness regarding the plurality of compatible preference parameters; (ii) interaction between criteria; (iii) hierarchies of criteria. Even if different extensions of the PROMETHEE methods have taken into account all these aspects singularly, we propose to deal with all of them simultaneously presenting a new version which incorporates Robust Ordinal Regression (ROR), Stochastic Multicriteria Acceptability Analysis (SMAA), bipolar Choquet integral and Multiple Criteria Hierarchy Process (MCHP). ROR and SMAA permit to consider all the instances of the considered preference model compatible with the preference information provided by the DM; the bipolar Choquet integral is able to represent the possible positive and negative interactions between criteria as well as the antagonistic effect between some of them; finally, the MCHP permits to decompose the problem in small parts so that each of them can be analyzed more in detail with respect to the problem at hand. Moreover, we also introduce an extension of the GAIA technique to handle visualization in MCDA problems presenting interactions and antagonistic effects between criteria organized in a hierarchy. Furthermore it gives the possibility to display the plurality of instances of the preference model considered by SMAA. A didactic example will illustrate the proposed methodology.

Market segmentation: A multiple criteria approach combining preference analysis and segmentation decision

We propose a new multiple criteria decision aiding approach for market segmentation that integrates preference analysis and segmentation decision within a unified framework. The approach employs an additive value function as the preference model and requires consumers to provide pairwise comparisons of some products as the preference information. To analyze each consumer’s preferences, the approach applies the disaggregation paradigm and the stochastic multicriteria acceptability analysis to derive a set of value functions according to the preference information provided by each consumer. Then, each consumer’s preferences can be represented by the distribution of possible rankings of products and associated support degrees by applying the derived value functions. On the basis of preference analysis, a new metric is proposed to measure the similarity between preferences of different consumers, and a hierarchical clustering algorithm is developed to perform market segmentation. To help firms serve consumers from different segments with targeted marketing policies and appropriate products, the approach proposes to work out a representative value function and the univocal ranking of products for each consumer so that products that rank in the front of the list can be presented to her/him. Finally, an illustrative example of a market segmentation problem details the application of the proposed approach.

Performance evaluation of Portuguese mutual fund portfolios using the value-based DEA method

The increased volatility in capital markets since the outbreak of the 2008 global financial crisis and the investor’s lack of confidence in the banking sector represented significant challenges to portfolio fund managers. The current study assesses the performance of Portuguese mutual fund portfolios considering the period 2007–2014 using the value-based DEA method. This approach combines data envelopment analysis (DEA) with multiple criteria decision aiding. A dynamic evaluation including value judgements is carried out using data from 15 Portuguese equity funds. The results unveil the impact of the global crisis in the Portuguese investment funds industry. They show that Portuguese investment funds performed better between 2011 and 2013; this suggests that equity funds investors became more confident in these vehicles due to political measures reinforcing financial markets. The methodology followed in this study contributes to help investors in the identification of the funds with the best practices according to their judgments.

Ranking corporate sustainability: a flexible multidimensional approach based on linguistic variables

AbstractCorporate sustainability implies a compromise between the present environmental, social, and economic needs of a firm's stakeholders and their future needs. Corporate sustainability is therefore a multidimensional concept. Nowadays, several independent rating agencies rate firms in terms of environmental, social, and governance (ESG) criteria. These ratings are usually used by main sustainability indices such as the Dow Jones Sustainability Index, FTSE4 Good, Stoxx Sustainability Index, or Euronext Vigeo Family to select companies to invest in. Only those firms performing better than the average of their sector are selected. However, although providing linguistic ratings about the performance of the firms in individual ESG criteria with respect to their sector, rating agencies do not usually provide overall ESG rates describing the global performance of the firms in terms of ESG. In this paper, we propose a flexible operator, linguistic ordered weighted geometric aggregating operator (LOWGA), which will allow us to define the fuzzy ESG performance of the firms based on the linguistic labels provided by the rating agencies. Once overall ESG scores have been obtained, we will use them together with financial criteria to rank the firms in terms of their sustainability using a suitable multiple criteria decision aid (MCDA) approach, namely, TOPSIS (technique for order preference by similarity to ideal solution).

Robustness analysis of a green chemistry-based model for the classification of silver nanoparticles synthesis processes

This paper proposes a robustness analysis based on Multiple Criteria Decision Aiding (MCDA). The ensuing model was used to assess the implementation of green chemistry principles in the synthesis of silver nanoparticles. Its recommendations were also compared to an earlier developed model for the same purpose to investigate concordance between the models and potential decision support synergies. A three-phase procedure was adopted to achieve the research objectives. Firstly, an ordinal ranking of the evaluation criteria used to characterize the implementation of green chemistry principles was identified through relative ranking analysis. Secondly, a structured selection process for an MCDA classification method was conducted, which ensued in the identification of Stochastic Multi-Criteria Acceptability Analysis (SMAA). Lastly, the agreement of the classifications by the two MCDA models and the resulting synergistic role of decision recommendations were studied. This comparison showed that the results of the two models agree between 76% and 93% of the simulation set-ups and it confirmed that different MCDA models provide a more inclusive and transparent set of recommendations. This integrative research confirmed the beneficial complementary use of MCDA methods to aid responsible development of nanosynthesis, by accounting for multiple objectives and helping communication of complex information in a comprehensive and traceable format, suitable for stakeholders and/or decision-makers with diverse backgrounds.

Determining the fuzzy measures in multiple criteria decision aiding from the tolerance perspective

We consider multiple criteria decision aiding (MCDA) in the case of interactions between criteria. In dealing with interactions between criteria, fuzzy measures and integrals have demonstrated great advantages. Nevertheless, the determination of fuzzy measures has proven difficult because the capacities of not only single criterion but also all subsets of criteria need to be identified. Due to the value judgment essence of MCDA, the attitudes of the decision maker (DM) are typically modeled to identify fuzzy measures. In this paper, the tolerance attitudes of the DM, which implies a direct requirement instead of partial preference, are modeled with regard to the determination of fuzzy measures for the first time. With two scales developed in this paper, the DM can directly express the tolerance attitudes to certain criteria other than providing partial preference through pairwise comparison. As a result, it requires less prior knowledge and is more efficient to some extent. Further, the inherent interacting mechanism of criteria under different tolerance attitudes is explored. At last, the tolerance attitudes are applied to the process of multiple criteria analysis using a Choquet integral. A classic student evaluation problem is given as an example. The evaluation results are compared with additive models. This paper not only provides a new inspiration to the determination of fuzzy measures but also improves the descriptive capacity of fuzzy measures to the real world.

Expressiveness and robustness measures for the evaluation of an additive value function in multiple criteria preference disaggregation methods: An experimental analysis

An additive value function is one of the prevailing preference models in Multiple Criteria Decision Aiding (MCDA). Its indirect elicitation through pairwise questions is often applied due to lowering the cognitive effort on the part of a Decision Maker (DM). A practical usefulness of this approach is influenced by both expressiveness of the assumed model and robustness of the recommendation computed with its use. We experimentally evaluate the above characteristics in view of using an additive value function in the preference disaggregation context. The simulation results are quantified with the following four measures: (1) the share of decision scenarios for which a set of compatible value functions is non-empty, (2) the minimal difference between comprehensive values of reference alternatives compared pairwise by the DM, (3) the number of pairs of alternatives for which the necessary preference relation confirmed by all compatible functions holds, and (4) the number of non-trivial certain inferences which cannot be derived directly from the preference information. We discuss how these measures are influenced by the settings with different numbers of alternatives, criteria, pairwise comparisons, and performance distributions. We also study how the results change when applying various procedures for selection of the characteristic points which define the shape of per-criterion marginal value functions. In this regard, we compare four existing discretization algorithms with a new supervised technique proposed in this paper. Overall, we indicate that expressiveness and robustness are contradictory objectives, and a compromise between them needs to be reached to increase the usefulness of an additive value model in the preference disaggregation methods.

From the farm to the agri-food system: A multiple criteria framework to evaluate extended multi-functional value

Multi-functionality of agriculture and rural areas has an impact on the economy, the environment, nature and also on societal and cultural development. The dimensions of multi-functionality are interpreted in literature in many different ways, but they always deal with environmental, social and economic aspects. The measurement of the multi-functionality of the agri-food system is an important issue, as it allows the value chain to be interpreted under the lens of sustainable development pillars. In this paper, we argue that multi-functionality is a value, which extends its benefits along the entire agri-food chain. We present a methodology to aggregate indicators into an evaluation framework, in order to assess the level of multi-functionality along the entire food value chain. We have called this the “extended value” of multi-functionality, since our approach is able to consider not only the farm level, but also extends to the entire food chain. To analyse multi-functionality, it was necessary to build Non-Commodity Categories (NCC) based on Non-Commodity Output (NCO), which characterised the value added function along the agri-food value chain. A set of indicators was developed to measure the level of multi-functionality in each NCC. The Multiple Criteria Decision Aiding (MCDA) methodology ELECTRE III was used to implement an evaluation process by assigning specific importance to each indicator. This process aggregated the evaluations of multiple indicators into an integrated interpretation, and aimed to support policy makers by providing a ranking of alternative ruling policies for the agri-food value chain. We finally tested our methodological approach on the value chain for the olive oil of five European countries, to analyse which value chain was able to generate more beneficial functions above and beyond the product itself.

Determining new possible weight values in PROMETHEE: a procedure based on data envelopment analysis

Operational research (OR) offers efficient tools to support managers in strategic decision-making processes. Data envelopment analysis (DEA) and multiple criteria decision aid (MCDA) are two important research areas in OR. These two domains are both based on the evaluation of “objects” according to multiple “points of views”. Within the MCDA framework, choosing appropriate weights for the different criteria often arises as a problem itself for decision makers. As a consequence, researchers have developed original methodologies to help them during this elicitation phase. In this work, we aim to investigate how DEA can be used to propose weights in the context of the PROMETHEE II method. More precisely, we suggest an extension of the so-called “decision maker brain” used in the GAIA plane (also known as PROMETHEE VI) based on DEA. The underlying idea is based on the computation of weights in PROMETHEE (GAIA brain) which are compatible with the DEA analysis. We end this paper with a numerical example.

Corporate failure prediction in the European energy sector: A multicriteria approach and the effect of country characteristics

This study examines the development of corporate failure prediction models for European firms in the energy sector, using a large dataset from 18 countries. The construction of the models is based on a multiple criteria decision aid (MCDA) approach taking into account both ordinal criteria and nominal country-sector effects. The analysis is based on different modeling specifications. First, traditional financial variables are examined, which are then extended with additional country-level data related to the economic and business environment, as well as data about the energy efficiency policies of the countries and the characteristics of their energy markets and networks. The results indicate that energy-related attributes have high discriminating power and add valuable information compared to the other attributes.

MACBETHSort: a multiple criteria decision aid procedure for sorting strategic products

Efficient companies need to know which their strategic products are. For this purpose, the ABC classification based on the item’s value was developed and was used for a long time. Later, it was recognised that several other criteria need to be considered and multi-criteria ranking methods were applied. However, the classes have always been defined based only on a relative proportion. Therefore, the number of products in a class is independent of the actual importance of the products. In this paper, a new sorting procedure MACBETHSort is introduced, which is an evolution of the choice, ranking and rating technique MACBETH. The procedure has been validated in a real case study for assigning access and entrance solutions in ABC classes.

A robust ranking method extending ELECTRE III to hierarchy of interacting criteria, imprecise weights and stochastic analysis

A great majority of methods designed for Multiple Criteria Decision Aiding (MCDA) assume that all assessment criteria are considered at the same level, however, decision problems encountered in practice often impose a hierarchical structure of criteria. The hierarchy helps to decompose complex decision problems into smaller and manageable subtasks, and thus, it is very attractive for computational efficiency and explanatory purposes. To handle the hierarchy of criteria in MCDA, a methodology called Multiple Criteria Hierarchy Process (MCHP), has been recently proposed. MCHP permits to consider preference relations with respect to a subset of criteria at any level of the hierarchy. Here, we propose to apply MCHP to the ELECTRE III ranking method adapted to handle three types of interaction effects between criteria: mutual-weakening, mutual-strengthening and antagonistic effect. We also involve in MCHP an imprecise elicitation of criteria weights, generalizing a technique called the SRF method. In order to explore the plurality of rankings obtained by the ELECTRE III method for possible sets of criteria weights, we apply the Stochastic Multiobjective Acceptability Analysis (SMAA) that permits to draw robust conclusions in terms of rankings and preference relations at each level of the hierarchy of criteria. The novelty of the whole methodology consists of a joint consideration of hierarchical assessments of alternatives performances on interacting criteria, imprecise criteria weights, and robust analysis of ranking recommendations resulting from ELECTRE III. An example regarding the multiple criteria ranking of some European universities will show how to apply the proposed methodology on a decision problem.

Transformation of criteria with a-priori chosen optimal values

Aim of multiple criteria decision-aid (MCDA) methods is to find the best alternative among the ones that are available or to rank alternatives in the order of preference. There are the following core pillars of the methods: the set of criteria and matrix with values of criteria that characterise the evaluated alternatives (decision matrix); and vector of weights that reflect relative importance of criteria. Usually, two types of criteria are used by researchers. Maximising criteria (e.g. profits) reflect a better situation whenever the larger value has been attained. While in case a criterion is minimising (e.g. costs), the better situation is reflected when its value is smaller. Such situations, when the best value of a criterion has a certain value, which differs from the maximal or the minimal, are usually not considered. This paper aims to fill this gap. Such criteria will be named as criteria with a-priori chosen optimal values. The aim of the paper is to propose appropriate types of transformation for criteria with a-priori chosen optimal values. Such transformations appear to be general and can be used with all three types of criteria.

A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding

A new framework for preference disaggregation in multiple criteria decision aiding is introduced. The proposed approach aims to infer non-monotonic additive preference models from a set of indirect pairwise comparisons. The preference model is presented as a set of marginal value functions and the discriminatory power of the inferred preference model is maximized against its complexity. To infer a value function that is compatible with the supplied preference information, the proposed methodology leads to a linear programming optimization problem that is easy to solve. The applicability and effectiveness of the new methodology is demonstrated in a thorough experimental analysis covering a broad range of decision problems.