Multiple Criteria Decision Aid Research Articles

An example on the use and limitations of MCDA: The case of fuzzy VIKOR

VIKOR is one of the well known Multiple Criteria Decision Aid methods, used frequently and considered to produce robust results. However, it seems that it is not immune to the rank reversal phenomenon. The latter is a well studied phenomenon that occurs sometimes in many such methods and is the cause of often severe criticism. This short paper provides such an example (and a counter-example) in the case of a fuzzy version of VIKOR.

The introduction of the SRF-II method to compare hypothesis of adaptive reuse for an iconic historical building

The paper shows how multiple criteria decision aiding (MCDA) tools can support the analyses of six hypotheses of adaptive reuse of an iconic historical building in Turin, Italy (called stock exchange) to identify the preferred alternative. In the last 2 years, the debate around the requalification of the building has been huge for several reasons: it is perceived as a “monument” by citizens; it shows architectural and typological values nationally recognized; it involves public and private interests. In this context, we tested the applicability of a recently proposed multicriteria decision making methodology to architecture field. This decision support approach is a conjunction of four MCDA methods, namely: multiple criteria hierarchy process (MCHP), permitting to consider structural relationships between criteria; ELECTRE III, considering three types of interaction effects between criteria (strengthening, weakening and antagonistic effects); the imprecise SRF method, supplying an easily understandable approach to collect information from the Decision Maker; and stochastic multicriteria acceptability analysis (SMAA), providing robust recommendation, in terms of rankings and relations of preference, indifference and incomparability between project alternatives, at each level of the hierarchy. We propose a modification of the SRF methodology, called SFR-II, to increase the reliability of the decision aid procedure, which could constitute a significant advance for the same SRF method.

Pairwise comparison tables within the deck of cards method in multiple criteria decision aiding

This paper deals with an improved version of the deck of cards method to render the construction of ratio and interval scales more “accurate” compared to the ones built in the original version. The improvement comes from the fact that we can account for a richer and finer preference information provided by the decision-maker, which permits a more accurate modeling of the strength of preference between different levels of a scale. Instead of considering only the number of blank cards between consecutive positions in the ranking of objects, such as criteria and scale levels, we consider also the number of blank cards between not consecutive positions in the ranking. This information is collected in a pairwise comparison table that is not necessarily built with precise values. We can consider imprecise information provided in the form of intervals and missing values. Since the provided information is not necessarily consistent, we propose also some procedures to help the decision-maker to make consistent her evaluations in a co-constructive way interacting with an analyst and reflecting and revising her judgments. A didactic example will illustrate the application of the method.

Assessment of transport interchanges for the needs of people with reduced mobility

Transport available to people with reduced mobility assures their full involvement in social and economic life. A fundamental way of improving the travelling conditions for people with reduced mobility is to adapt elements of the transportation system, including interchanges, to their needs. This article covers the niche subject of adapting interchanges to the needs of people with reduced mobility. It contains the results of research assessing selected interchanges in Kraków (Poland) for the needs of that user group. The four variants of interchanges were assessed with one of the Multiple Criteria Decision Aid (MCDA) methods – the Compensation-Conjunctive method, which allowed to obtain the final rankings of the interchanges. The adaptation of the Assessment Method of Public Transport Interchanges (AMPTI), expanded with aspects resulting from a review of literature and the authors’ experience ensured a broad range of assessment criteria that comprehensively refer to the interchange planning. The unique nature of the research is the fact that opinions of people with reduced mobility were taken into account and the results proved that the most important aspects of interchanges are spatial compactness and solutions dedicated strictly for persons with reduced mobility. The other major aspects are quality of stop infrastructure and information.

Performance assessment of energy companies employing Hierarchy Stochastic Multi-Attribute Acceptability Analysis

This paper analyses the development of a performance assessment model for the most important listed companies operating in the energy sector, using a dataset obtained merging different sources. The construction of the model is based on a multiple criteria decision aid approach considering various indicators. The multidimensional nature of the topic in this paper requires the definition of a hierarchical structure of criteria, which has been aggregated into a composite index to obtain a final ranking for the energy companies under investigation. To handle with a hierarchical criteria structure and to take into account the space of fluctuations related to the imprecision on criteria weights, we employ the Hierarchy Stochastic Multi-Attribute Analysis. Thus, the proposed model is able to evaluate the performances of energy companies under different uncertainty scenarios. The results indicate that the first and last positions are quite robust in all considered scenarios, while the rankings relative to the intermediate positions vary widely by the chosen set of weights, exemplifying the need to rank companies based on multiple sets of criteria weights.

Evaluating the efficiency of structural funds: An application in the competitiveness of SMEs across different EU beneficiary regions

The funds dedicated to the cohesion policy are aimed at attaining the convergence of wealth levels across the European Union (EU) member states. These constitute the second-highest group of expenditures in the EU budget and thus their evaluation assumes a prominent role. Their assessment can be carried out through different tools and methodologies, all of them with their own merits and flaws. For instance, macroeconomic models allow assessing the potential impact of EU funds on economic growth, but disregard management issues. Other modelling approaches consider direct econometric estimations which might bring biased estimates of the impacts of Structural Funds if some variables are omitted. A different possible tool is Data Envelopment Analysis (DEA), which is a non-parametric method to measure the relative efficiency of Decision-Making Units (DMUs), allowing for the consideration of multiple inputs and outputs in global performance evaluation. In this work we employ the Value-Based DEA approach, which combines DEA with Multiple Criteria Decision Aiding (MCDA) and enables performing a robustness analysis of the results in face of uncertain information, considering the main factors that might influence the efficiency of the implementation of Structural Funds. Specifically, we address the assessment of its application in the competitiveness of small and medium-sized enterprises (SMEs), in different beneficiary regions of the EU. Our analysis helped in the identification of best practices, sources of inefficiency and gaps regarding the benchmark regions/countries, thus leading to shape opportunities for improvement. Overall, we were able to ascertain that national inefficient programmes mainly need to reduce their dependence on EU co-funding to become efficient, whereas almost all inefficient regional programmes need to improve their capacity of execution. Nevertheless, differences were found on the factors that require adjustments when considering regions’ categories.

A hybrid machine learning framework for analyzing human decision-making through learning preferences

Multiple criteria decision aiding (MCDA) is a family of analytic approaches to depicting the rationale of human decisions. To better interpret the contributions of individual attributes to the decision maker, the conventional MCDA approaches assume that the attributes are monotonic and preference independence. However, the capacity in describing the decision maker’s preferences is sacrificed as a result of model simplification. To meet the decision maker’s demand for more accurate and interpretable decision models, we propose a novel hybrid method, namely Neural Network-based Multiple Criteria Decision Aiding (NN-MCDA), which combines MCDA model and machine learning to achieve better prediction performance while capturing the relationships between individual attributes and the prediction. NN-MCDA uses a linear component (in an additive form of a set of polynomial functions) to characterize such relationships through providing explicit non-monotonic marginal value functions, and a nonlinear component (in a standard multilayer perceptron form) to capture the implicit high-order interactions among attributes and their complex nonlinear transformations. We demonstrate the effectiveness of NN-MCDA with extensive simulation studies and three real-world datasets. The study sheds light on how to improve the prediction performance of MCDA models using machine learning techniques, and how to enhance the interpretability of machine learning models using MCDA approaches.

Multiple criteria decision aiding as a prediction tool for migration potential of regions

This study presents the potential of multiple criteria decision aiding (MCDA) as a tool for prediction analysis. We apply MCDA methodology for the determination of internal migration potential of regions within a country. The ELECTRE Tri-C together with the Multiple Criteria Hierarchy Process (MCHP), the imprecise SRF method and the Stochastic Multicriteria Acceptability Analysis (SMAA) will be applied to investigate the case of the internal migrations in Serbia. We shall give recommendations regarding the migration potential of Serbian municipalities in the following years. In particular, the ELECTRE Tri-C will be applied to classify the regions in four classes ordered with respect to regions’ migration potential, MCHP will be used to take into account the hierarchical structure of criteria on which the municipalities are evaluated. Finally, the imprecise SRF method, as well as the SMAA methodology, will be used to take into account the preferences of numerous economic agents.

Using a segmenting description approach in multiple criteria decision aiding

We propose a new method for analyzing a set of parameters in a multiple criteria ranking method. Unlike the existing techniques, we do not use any optimization method, instead incorporating and extending a Segmenting Description (SD) approach. The algorithm rewrites a set of initial inequalities into an equivalent SD system by means of three fundamental operations in a way that makes it possible to either easily analyze all feasible solutions or identify all sources of inconsistency. While considering a value-based preference disaggregation method, we demonstrate the usefulness of the introduced method in a multi-purpose decision analysis exploiting a system of inequalities that models the Decision Maker’s (DM’s) preferences. We focus on the contexts where informative results can be obtained through the analysis of (in)feasibility of such systems. Specifically, we discuss how SD can be applied for verifying the consistency between the revealed and estimated preferences as well as for identifying the reasons of potential incoherence. Moreover, we employ the method for conducting robustness analysis, i.e., discovering a set of all compatible parameter values and verifying the stability of suggested recommendation in view of multiplicity of feasible solutions. In addition, we make clear its suitability for generating arguments about the validity of outcomes and the role of particular criteria. Overall, the SD approach confirms its usefulness in analyzing the relationships between the DM’s preference information, the compatible parameters of an assumed model, and the value-driven ranking procedure. We also discuss the favourable characteristics of the proposed method enhancing its suitability for use in Multiple Criteria Decision Aiding. These include possibility of studying any system of inequalities without an objective function, keeping in memory an entire process of transforming a system of inequalities that makes identification of potential incoherence straightforward, and avoiding the need for processing the inequalities contained in the basic system which is subsequently enriched with some hypothesis to be verified. The latter is substantial for saving time when a multitude of hypotheses need to be checked in the same conditions. The applicability and the advantages of using the proposed method in the decision aiding context are clearly exemplified on a numerical study.

The methodology of solving stochastic multiple criteria ranking problems applied in transportation

The aim of this paper is to present the methodology of solving complex multiple criteria problems with the presence of non-deterministic information. The author proposes a universal approach of ranking variants applied to solve decision problem in transportation. Since the nature of transportation process is stochastic, the evaluation criteria are expressed by non-deterministic values. Moreover, the preferential information is usually contradictory, with some level of uncertainty. As a result, the proposed methodology is composed of two phases, including: (1) the selection of the most suitable deterministic multiple criteria decision aiding (MCDA) method to decision problem and decision maker’s (DM’s) preferences expressed during the modelling process; (2) its application using non-deterministic information, i.e. transformation of non-deterministic information into deterministic values, computation of relations between variants resulting in many deterministic rankings, calculation of non-deterministic relations between variants, and construction of final ranking. The proposed methodology is verified on the exemplary decision problem in transportation. The application of the procedure increases the credibility of the final decision, giving an opportunity to use deterministic MCDA method in the stochastic transportation process.

Evaluation of soccer players under the Moneyball concept

ABSTRACT The recruitment of athletes with limited resources is a global problem in professional sports. In US Major League Baseball, the experience of the Oakland Athletics’ general manager in the last decade turned his “Moneyball” model into a synonym of quantitative analysis in the transfer market of baseball players. His strategy focused on hiring players with outstanding technical skills but relatively low market value. This study adapted this model to the framework of a multiple criteria decision aid (MCDA), by selecting undervalued players who have complementary abilities. The novelty here refers to the joint use of four algorithms explored by the composition of probabilistic preferences (CPP) (i.e., ranking, classification, dynamic evaluation and regularity analysis) and their application to soccer player performance evaluation. The new model analysed the recent transfer of a left-back soccer player to Europe. The results indicated 12 opportunities for better investment, among 32 left and right-back players considered. Two years later, the value of the same player was considerably lower. He played only five matches in the 2018–2019 season, without scoring or providing any assists. On the other hand, the players better classified by the CPP-MB model presented higher performances and market values.

Use of Life Cycle Cost Analysis and Multiple Criteria Decision Aid Tools for Designing Road Vertical Profiles

The current design practice for the vertical profile of roads in rolling and mountainous terrains is to follow the existing grades in order to minimize earthwork costs. This means that the only criterion considered during the design phase is the initial cost. It would be preferable to include other criteria that are directly related to sustainability, particularly fuel consumption and CO2 emissions. Therefore, this paper describes a proposed design procedure that starts by finding feasible alternatives with different grades. Then, a microsimulation traffic tool is used to simulate the movement of predicted vehicles (volume and type) over the different alternatives. The microsimulation tool provides reliable estimates of travel times, fuel consumption, and CO2 emissions for the different alternatives. With these data, it is possible to use life cycle cost analysis (LCCA) or multiple criteria decision aid (MCDA) tools to select the “optimal” alternative. The proposed procedure was used on a case study involving a 6-km highway section with different proposed grades ranging from 2% to 8%. Using LCCA and an MCDA tool, it was revealed that the current design alternative is not the optimal alternative in most considered scenarios (various fuel values for LCCA and different “Cost” weights for MCDA).

PARS, a system combining semantic technologies with multiple criteria decision aiding for supporting antibiotic prescriptions

Motivated by the well documented worldwide spread of adverse drug events, as well as the increased danger of antibiotic resistance (caused mainly by inappropriate prescribing and overuse), we propose a novel recommendation system for antibiotic prescription (PARS). Our approach is based on the combination of semantic technologies with MCDA (Multiple Criteria Decision Aiding) that allowed us to build a two level decision support model. Given a specific domain, the approach assesses the adequacy of an alternative/action (prescription of antibiotic) for a specific subject (patient) with an issue (bacterial infection) in a given context (medical). The goal of the first level of the decision support model is to select the set of alternatives which have the potential to be suitable. Then the second level sorts the alternatives into categories according to their adequacy using an MCDA sorting method (MR-Sort with Veto) and a structured set of description logic queries. We applied this approach in the domain of antibiotic prescriptions, working closely with the EpiCura Hospital Center (BE). Its performance was compared to the EpiCura recommendation guidelines which are currently in use. The results showed that the proposed system is more consistent in its recommendations when compared with the static EpiCura guidelines. Moreover, with PARS the antibiotic prescribing workflow becomes more flexible. PARS allows the user (physician) to update incrementally and dynamically a patient's profile with more information, or to input knowledge modifications that accommodate the decision context (like the introduction of new side effects and antibiotics, the development of germs that are resistant, etc). At the end of our evaluation, we detail a number of limitations of the current version of PARS and discuss future perspectives.

A historical analysis of tax competitiveness between EU member states

We demonstrate that the dynamics of tax competitiveness in the EU has gained a new higher level. The shift in the tax competition can be noted after new countries have joined the EU. Both positive and negative effects of tax competitiveness are discussed in the paper and the question of trade-off between the strong tax competitiveness of a country and magnitude of its tax revenue is raised. Evaluation of chosen countries using multiple criteria decision aid methods (MCDA) shed more light on the opposition of two groups of countries, the old and new members of the EU, provided more details on the both the present state and dynamics of tax competitiveness in the. The evaluation methodology can be successfully used for monitoring the current state of tax competitiveness of each member country. Prominence of the multiple criteria evaluation TOPSIS method of MCDA evaluation is described.
 Keywords: Competitiveness of countries, tax system, tax competition, tax revenues, multiple criteria evaluation, TOPSIS.

As simple as possible but not simpler in Multiple Criteria Decision Aiding: the robust-stochastic level dependent Choquet integral approach

The level dependent Choquet integral has been proposed to handle decision making problems in which the importance and the interaction of criteria may depend on the level of the alternatives’ evaluations. This integral is based on a level dependent capacity, which is a family of single capacities associated to each level of evaluation for the considered criteria. We present two possible formulations of the level dependent capacity where importance and interaction of criteria are constant inside each one of the subintervals in which the interval of evaluations for considered criteria is split or vary with continuity inside the whole interval of evaluations. Since, in general, there is not only one but many level dependent capacities compatible with the preference information provided by the Decision Maker, we propose to take into account all of them by using the Robust Ordinal Regression (ROR) and the Stochastic Multicriteria Acceptability Analysis (SMAA). On one hand, ROR defines a necessary preference relation (if an alternative a is at least as good as an alternative b for all compatible level dependent capacities), and a possible preference relation (if a is at least as good as b for at least one compatible level dependent capacity). On the other hand, considering a random sampling of compatible level dependent capacities, SMAA gives the probability that each alternative reaches a certain ranking position as well as the probability that an alternative is preferred to another. A real-world decision problem on rankings of universities is provided to illustrate the proposed methodology.

Assigning a house for refugees: an application of a multiple criteria nominal classification method

The paper proposes the use of a multiple criteria decision aiding (MCDA) nominal classification method to tackle a dramatic current social problem: the accommodation system for refugees. In recent years, the number of people in need of protection and assistance as a consequence of forced displacement has drastically increased. As many other European cities, Turin (Italy) offers to refugees temporary places for a limited time span, but they are insufficient and often inadequate. Meanwhile, there are several underused buildings that can potentially be used to cover the refugees’ needs of housing and facility. Combining these two aspects, this study aims at contributing to the definition of an urban strategy and improve the decision aiding process related to the accommodation system in Turin. We adopt a recently developed MCDA method to tackle the problem of accommodation of migrants, that is, the Cat-SD (Categorization by Similarity–Dissimilarity) method. We face a nominal classification problem where underused buildings located in the city of Turin are assigned to categories (i.e., three migrant status). A decision model is constructed based on the judgments of the experts. Thus, the application of the method allows to identify an adequate category (or categories) of migrant status for each building.

Consumer preference analysis: A data-driven multiple criteria approach integrating online information

Multiple criteria approaches can assist the product manager to know the consumer preferences in the context of e-commerce. Consumer preference analysis explains what aspects of a product affect and how they affect a consumer’s purchasing decision. This issue plays an important role in e-commerce platforms from its relevance in marketing decisions such as advertisements, recommendations and promotions. In this regard, we propose a data-driven multiple criteria decision aiding (MCDA) approach to integrate online information, such as explicit (e.g., reviews and ratings) and implicit (e.g., clicks and purchases) feedback from consumers. However, MCDA approaches present a critical challenge that even an experienced product manager could find it difficult to pre-define the criteria on which a product is evaluated. To address this issue, our proposed approach first utilizes text-mining techniques to assist the product manager identify the criteria, and then determines and collects the relative importance of the criteria and their values. Given the criteria information, we use a sampling process to provide two indices, the consumer preference index and rank acceptability index. The first index helps in prioritizing the pairwise comparisons of products, while the second one helps in deriving a default ranking list for first-time-registered consumers. We record the products viewed by consumers and generate their preference information in the form of pairwise comparisons for analyses within an aggregation-disaggregation paradigm. We also provide a representative value function to help the product manager gain insight into the preferences. Finally, we describe how a real-world application including the product manager and consumers exploits the proposed approach on an e-commerce platform to take a large step toward aiding more realistic and data-driven multiple criteria decision making.

The Application of the Multiple Criteria Decision Aid to Assess Transport Policy Measures Focusing on Innovation

The sustainable development of transport is fostered by innovations. To implement innovations, the European Commission issues different regulations, programs and initiatives and the European Transport Policy has a significant impact on transport policy in the member states. At the same time, transport policy is dynamic and requires new solutions that will allow the planned goals to be achieved. In this context, it is important to analyze the effectiveness of the current innovation policies, and to create recommendations for future actions that bring innovations to the market. This article concerns the subject of innovation policy in the transport sector. It illustrates the possibility of applying one of the methods of the multiple criteria decision aid, i.e., the simple additive weighting (SAW) method to assess the European Union (EU) and national policy measures in surface transport in terms of their influence on the market take-up of innovations. The use of this method allows for the analyzed policy measures to be contemplated in terms of various criteria and to identify those that best meet the adopted criteria, and thus those that could contribute the most to the stimulation of innovation. The article focuses on the method itself, indicating its flexibility and ease of use, while the analyzed collection of policy measures constitutes only the background of the deliberations.

An indirect elicitation method for the parameters of the ELECTRE TRI-nB model using genetic algorithms

Indirect approaches for eliciting preference model parameters for multiple criteria decision aiding are of growing interest because they imply relatively less cognitive effort from the decision-maker (DM). Direct approaches are particularly hard in the case of the new ELECTRE TRI-nB method, because the task involves eliciting a number of limiting profiles for each category boundary. However, in ELECTRE methods, the simultaneous inference of the whole set of parameters needs the construction and resolution of a non-linear non-convex programming problem, which is typically very hard to solve. Therefore, an evolutionary-based method to infer the parameters of the ELECTRE TRI-nB model is proposed in this paper. The quality of the solutions is tested by measuring the capacity to restore the assignment examples and the capacity to make consistent assignments of new actions. In extensive computer experiments, using the pseudo-conjunctive assignment procedure, some main conclusions arise: (i) the capacity of the method to restore the training examples reaches high values, mainly with three and five limiting profiles per category; and (ii) the capacity to make appropriate assignments of new actions (not belonging to the training information) can be greatly improved by increasing the number of limiting profiles.

A progressive sorting approach for multiple criteria decision aiding in the presence of non-monotonic preferences

A new decision-aiding approach for multiple criteria sorting problems is proposed for considering the non-monotonic relationship between the preference and evaluations of the alternatives on specific criteria. The approach employs a value function as the preference model and requires the decision maker (DM) to provide assignment examples of a subset of reference alternatives as preference information. We assume that the marginal value function of a non-monotonic criterion is non-decreasing up to the criterion’s most preferred level, and then it is non-increasing. For these non-monotonic criteria, the approach starts with linearly increasing and decreasing marginal value functions but then allows such functions to deviate from the linearity and switches them to more complex ones. We develop several algorithms to help the DM resolve the inconsistency in the assignment examples and assign non-reference alternatives. The algorithms not only incorporate the DM’s evolving cognition of the preference, but also take into account the trade-offs between the capacity for satisfying incremental preference information and the complexity of the preference model. The DM is guided to evaluate the results at each iteration and then provides reactions for the subsequent iterations so that the proposed approach supports the DM to work out a satisfactory preference model. We demonstrate the applicability and validity of the proposed approach with an illustrative example and a numerical experiment.