Multiple Criteria Decision Aid Research Articles

Blockchain service platform evaluation with probabilistic linguistic preference information based on Choquet integral and ExpTODIM

The application of Internet of Medical Things (IoMT) can improve the treatment efficiency of medical institutions, but it still has hidden dangers in security and privacy. The introduction of blockchain technology into IoMT is expected to prevent these hidden dangers. This study proposes a multiple criteria decision aiding model to select a suitable blockchain service platform for IoMT to enhance its safety and reliability. The proposed model combines the probabilistic linguistic term set (PLTS), multiple criteria hierarchy process (MCHP), 2-additive Choquet integral and ExpTODIM method to assist decision-makers (DMs) to obtain a reliable and low-risk decision result. The MCHP is employed to analyze decision-making problems by hierarchical criteria. The 2-additive Choquet integral is adopted to reflect interactions between criteria, and parameter values in the 2-additive Choquet integral are indirectly determined based on the preference information of DMs. PLTSs are used to represent the evaluation information of experts and the preference information of DMs. The ExpTODIM (Exponential TODIM) method is adopted to rank alternatives considering the psychological characteristics of DMs. An illustrative example demonstrates the applicability and merits of the proposed model with comparative analysis.

Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies–part I

AbstractMultiple criteria decision aiding (MCDA) and preference learning (PL) are established research fields, which have different roots, developed in different communities – the former in the decision sciences and operations research, the latter in AI and machine learning – and have their own agendas in terms of problem setting, assumptions, and criteria of success. In spite of this, they share the major goal of constructing practically useful decision models that either support humans in the task of choosing the best, classifying, or ranking alternatives from a given set, or even automate decision-making by acting autonomously on behalf of the human. Therefore, MCDA and PL can complement and mutually benefit from each other, a potential that has been exhausted only to some extent so far. By elaborating on the connection between MCDA and PL in more depth, our goal is to stimulate further research at the junction of these two fields. To this end, we first review both methodologies, MCDA in this part of the paper and PL in the second part, with the intention of highlighting their most common elements. In the second part, we then compare both methodologies in a systematic way and give an overview of existing work on combining PL and MCDA.

Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies—part II

Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies—part II

Inducing a probability distribution in Stochastic Multicriteria Acceptability Analysis

In multiple criteria decision aiding, very often the alternatives are compared by means of a value function compatible with the preferences expressed by the Decision Maker. The problem is that, in general, there is a plurality of compatible value functions, and providing a final recommendation on the problem at hand considering only one of them could be considered arbitrary to some extent. For such a reason, Stochastic Multicriteria Acceptability Analysis gives information in statistical terms by taking into account a sample of models compatible with the provided preferences. These statistics are given assuming the existence of a probability distribution in the space of value functions being defined a priori. In this paper, we propose some methods aiming to build a probability distribution on the space of value functions considering the preference information given by the Decision Maker. To prove the goodness of our proposal we performed an extensive set of simulations. Moreover, a sensitivity analysis on the variables of our procedure has been done.

A multiple criteria approach for ship risk classification: An alternative to the Paris MoU Ship Risk Profile

The Paris Memorandum of Understanding on Port State Control (Paris MoU), adopted in the European Union (EU) under Directive 2009/16/EC, as amended by Directive 2013/38/EU, is responsible for controlling substandard shipping in European waters and, consequently, increasing the standards of safety, pollution prevention, and onboard living and working conditions. Since 2011, the Memorandum adopted a system of points, named the “Ship Risk Profile” (SRP), under which each ship is assigned a risk profile according to its score on a set of criteria. Being a multiple criteria decision aiding (MCDA) tool at its core, comprising criteria, weights, and risk categories, limited research has been performed on the SRP from an MCDA perspective. The purpose of this paper is to propose an MCDA approach for ship risk classification through the Deck of Cards Method (DCM). The DCM is particularly suitable within this context as it allows, intuitively for the decision-maker, to model preference among different criteria and among different levels on criteria scales. First, a framework is built, based on the criteria established in the SRP. Second, the DCM is used to build the MCDA model, including the definition of the criteria value functions and criteria weights. Finally, the proposed MCDA model is applied to a data set of ships and the results are discussed. Robust results have been obtained with the proposed approach, making it a potential alternative to the current SRP.

Questionable stepwise explanations for a robust additive preference model

In this paper, we study Multiple Criteria Decision Aiding (MCDA) problems modeled using an additive value function. We consider an epistemic framework in which the preferences of the decision-maker are imprecisely specified, yielding a robust additive preference model. In this context, we are interested in explaining recommendations derived from such robust model using a transitive sequence of preference swaps. Previous work laid the foundations for explaining the necessary preference relation through a sequence of necessary preference swaps. We extend this to take into account non-necessary preference, yielding to so-called “questionable explanations”: a chain of alternatives which is non-increasing w.r.t. preference of the decision-maker. This approach provides additional descriptive power for explaining robust recommendations. We propose an efficient resolution engine based on Mixed-Integer Linear Programs, and we conduct numerical experiments to assess the benefit of our explanation strategy.

The State Of International Technology Transfer In The Northern Europe Region: Comparative Analysis By Modern Multiple Criteria Methods

The undiminished importance of international technology transfer (ITT) consistently attracts attention from scientists, businesses, and governments. The OECD acknowledges the benefits of the ITT and emphasises that European countries should be open to new opportunities that ITT brings in each economic sector, from primary to tertiary. The mining sector does not represent an exception. ITT contributes to job creation and growth worldwide by creating new markets and expanding existing ones. ITT transforms human fifes and surrounding very quickly. The TT improves production efficiency, stimulates growth; reformats patterns of sectors; brings new opportunities to every capital owner, and improves economic well-being among people worldwide. At the same time, it changes the value system and, in many cases, seriously harms the environment. Despite the researchers' focus on ITT, more complex research still needs to assess the factors of such phenomena. The article contributes to creating a more comprehensive methodology for evaluating the ITT environment not only in the mining sector but also in the other field of the primary sector. For analysis, the authors use popular Multiple Criteria Decision Aid (MCDA) methods and develop objective methods for criteria weights calculation. The study evaluates and compares internal conditions for OECD Northern Europe countries to implement, develop and share technologies. For this purpose, a set of 16 quantitative criteria was created; their hierarchical structure with four categories was set. A detailed comparative analysis of the state of the countries in terms of favourable conditions for the ITT was performed.

Technological, healthcare and consumer funds efficiency: influence of COVID-19

This paper aims to analyze the efficiency of the funds in technological, healthcare, and consumer cyclical sectors based on the U.S. News & World Report rankings. We employed a Principal Component Analysis to select the indicators to explain efficiency. Then, we have used an alternative approach that combines Data Envelopment Analysis (DEA) with Multiple Criteria Decision Aiding, the Value-Based DEA, to assess the efficiency of funds for 1 year (2020), 3 years (2018–2020), and 5 years (2016–2020). The results highlight that in 2020 the number of efficient funds is much smaller than in previous periods and this can be justified by the effect of the COVID-19 pandemic crisis. The sectors with the most efficient funds are technology and healthcare. The factors that determine the efficiency of funds in the health sector and the technology sector are quite different, although they have not undergone major changes in the three periods considered. For managers, health funds are seen as low risk and hardly consider the return factors in all analyzed periods, which is often considered as benchmarks for inefficient funds. In the technology sector, Beta and Alpha are generally the indicators with the greatest weight in fund efficiency, showing that these funds beat the market in terms of returns and are less risky than the benchmark. This study seeks to complete the scarce existing literature on the subject, namely in the sectors under analysis, seeking to identify the indicators that fund managers ponder most to consider a fund as efficient. As far as we know, the joint efficiency analysis of these sectors and the impact they suffered from the COVID-19 pandemic are new in the literature.

Dr. Banu Lokman: 2022 Recipient of Prestigious Bernard Roy Award

It is great pleasure for us to share with the MCDM/A community that Dr. Banu Lokman was the 2022 recipient of prestigious Bernard Roy (BR) award. Dr. Banu Lokman is a Reader in Operational Research at the School of Organisations, Systems and People, University of Portsmouth, UK. We have known Dr. Lokman as an excellent member of the MCDM community for over 10 years and have always thought that she deserves an award. We are very pleased that she was recognized by the EURO Working Group on Multiple Criteria Decision Aiding (EWG MCDA). The award was given during the EWG MCDA business meeting in Agios Nicolaos, Crete last September by Yannis Siskos, the 2022 Jury Chair. Congratulations Dr. Lokman, we are proud of you!

Multiple Hierarchically Structured Criteria in ARAS Method Under Fuzzy Environment

The aim of multiple criteria decision aiding (MCDA) is to assist decision makers (DMs) to make rational decisions with respect to their preferences. In fact, the ranking approaches are the most used ones nowadays in the MCDA field because they are easy to understand by DMs and they are based on realistic assumptions. The hierarchical additive ratio assessment (ARAS-H) method is a ranking method. It represents an extension of the ARAS method in case of hierarchically structured criteria. However, most often, the DM is unable to provide precise performance values. Henceforth, in order to facilitate the task for him, he is asked to provide linguistic variables. Thus, the authors adopted the fuzzy logic. As a matter of fact, the fuzzy set theory takes into account the subjectivity of experts' ‘judgments.' In the light of the above, the fuzzy ARAS-H (F-ARAS-H) algorithm was developed as an extension of the ARAS-H method in a context of a fuzzy environment. To discuss the feasibility of the proposed algorithm, a case study on the selection of a green supplier was presented.

A robust TOPSIS method for decision making problems with hierarchical and non-monotonic criteria

This paper introduces an extension of a well-known Multiple Criteria Decision Aiding method, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Most of the TOPSIS applications assume that preferences are monotonic for each evaluation criterion and that qualitative scales are converted into quantitative ones before the method is applied. However, both assumptions have been subject of discussion and criticism in the literature. To this solution, this paper introduces a normalization technique based on simulations that permit taking into account non-monotonic preferences as well as qualitative criteria. An additional novelty lies in the integration of the Multiple Criteria Hierarchy Process, which extends the applicability of the method to problems in which criteria are hierarchically structured. To deal with robustness concerns, the Stochastic Multicriteria Acceptability Analysis will be used in the new proposal, giving information in statistical terms on the goodness of the considered alternatives. The new method has been applied to evaluate a set of banks listed in the LSE’s FTSE350 Index.

An evolved VIKOR method for multiple-criteria compromise ranking modeling under T-spherical fuzzy uncertainty

The development of T-spherical fuzzy (T-SF) sets in qualitative multiple-criteria choice analysis has been well investigated, but little is explored about how the VIKOR (i.e., VIšekriterijumsko KOmpromisno Rangiranje in Serbian) mechanism is generalized into intricate T-SF environments. This paper has the objective of propounding a creative T-SF VIKOR methodology for compromise ranking modeling in multiple criteria analysis. The exploitation of the T-SF configuration brings about superior information space representation in response to intricate realistic environments. However, because of the increased complexity of decision situations involving T-spherical fuzziness, treatment with the T-SF information arises a pragmatic difficulty in developing methodological approaches to T-SF VIKOR. In light of this concern, this paper unfolds an analytical framework for the T-SF VIKOR method predicated on new notions of an evolved T-SF score function and the Minkowski-type T-SF distance measure along with its special cases of the Manhattan-, Euclidean-, and Chebyshev-type distances. The aspired and despised T-SF characteristics can be identified supported by the evolved T-SF score function of T-SF performance ratings. This paper gives a new delineation of the group utility, individual regret, and joint generalized measures with the aid of the Minkowski-type T-SF distance measure relative to the aspired/despised T-SF characteristics. A synthetic mechanism is built to validate the standards of acceptable advantage and stability and yield the ultimate compromise choices for multiple criteria decision aiding. The practicality of the T-SF VIKOR methodology in down-to-earth decision situations has been well demonstrated through the selection issues of warehouse locations and advertisement strategies. Moreover, the developed approach has shown better application outcomes than the past methods did. The comparative study with parametric analysis has revealed the steadiness and effectiveness of the compromise choice results in T-SF circumstances.

Recovery and Resilience of the Inner Areas: Identifying Collective Policy Actions through PROMETHEE II

Inner areas are rural areas with low population density, distant from the main hotspots offering essential welfare services and endowed with significant environmental assets and cultural heritage. In Italy, their development is at the core of the national strategy for inner areas. Specific documents and program agreements were issued for each inner area and summarized the intervention themes and projects to implement. However, when the inner areas are considered within a regional territorial ‘matrix’, further collective policy actions have to be identified, through comparison and in-depth analysis of their features and influence on development. Therefore, this research aims to identify and analyse the development needs and potentialities of the inner areas, for strengthening the national strategy and improving their recovery and resilience through collective policy actions. The Multiple Criteria Decision Aiding (MCDA) method PROMETHEE II was applied as research methodology to four inner areas in Puglia region (southern Italy), using 43 indicators organized into seven thematic dimensions and setting specific parameters. The results enabled to delineate the overall development score of the four inner areas, and the profile of each inner area based on every indicator and dimension. By analysing this profile, key thematic dimensions where to direct collective policy actions were identified, related mainly to contrast with the depopulation by improving specific essential services (e.g., digitalization, health, education) and to foster the development of agriculture, tourism, and cultural heritage. This research can be considered a first step for future broader studies, to guide the process of policy making for the recovery and resilience of European and Italian inner areas with a multi-perspective approach.

An extension of the FLOWSORT method based on intuitionistic fuzzy set theory to solve Multicriteria Decision Making problems

Multiple attribute decision analysis (MADA) is an important part of the modern decision science. In fact, it has been increasingly used in the literature. Therefore, the study of sorting problems is an active research topic in the multiple criteria decision aid (MCDA) area. Although, it is difficult to quantitatively and precisely express the evaluation criteria to solve real-life sorting problems. To solve this fuzziness and vagueness, the intuitionistic fuzzy set (IFS) theory achieved great success in various recent researches. Therefore, this paper presents a novel extension of the FlowSort method, which is a PROMETHEE-based sorting method, based on the intuitionistic fuzzy set theory. To clarify this new extension, an illustrative example and an empirical comparison with other MCDM methods are presented.

Waste Classification of Spent Refractory Materials to Achieve Sustainable Development Goals Exploiting Multiple Criteria Decision Aiding Approach

The recycling of used refractory materials in the heavy industry constitutes one of the significant environmental problems in the industry related to environmental and financial issues. This study proposes a multicriteria methodological frame to characterize the refractory material waste and identify the recycling capabilities. Considering the chemical and physical analysis of the refractory material wastes, the proposed methodological frame progresses into a two-phase procedure. The first phase includes an on/off approach that allows discretizing the refractory material wastes to compatible or not compatible as far as their recycling prospects. Then, an additive value model is utilized, including (a) the marginal value functions used for every criterion related to critical environmental factors, and (b) the weight vector reflecting the relative importance of the criteria used. A group of experts concerning the environment and the refractory materials was employed to estimate the additive value model. The assessment of the marginal value function is achieved using the module of the Multicriteria Interactive Intelligence Decision Aiding System (MIIDAS), which is based on a modification of the mid-value split point technique incorporating focused dialogues, artificial intelligence, and visual techniques. The weight vector was assessed using the weight assessment through prioritization method (WAP), which concludes with the estimation of the weights based on the criteria ranking and the pairwise expression of the strength of preferences for the consecutive criteria according to their ranking. The outcome of this approach is to introduce an environmental appropriateness index for refractory materials based on their chemical composition and the judgement of an expert group. The main findings of this research may be useful for engineers, decision-makers, and scientists in the field of circular economy and waste management.

Vulnerability to COVID-19 in Pernambuco, Brazil: A geospatial evaluation supported by multiple-criteria decision aid methodology.

The paper presents an innovative application to identify areas vulnerable to coronavirus disease 2019 (COVID-19) considering a combination of spatial analysis and a multi-criteria learning approach. We applied this methodology in the state of Pernambuco, Brazil identifying vulnerable areas by considering a set of determinants and risk factors for COVID-19, including demographic, economic and spatial characteristics and the number of human COVID-19 infections. Examining possible patterns over a set number of days taking the number of cases recorded, we arrived at a set of compatible decision rules to explain the relation between risk factors and COVID-19 cases. The results reveal why certain municipalities are critically vulnerable to COVID-19 highlighting locations for which knowledge can be gained about environmental factors.

Multi-criteria human resources planning optimisation using genetic algorithms enhanced with MCDA

The main objective of this paper is to present an example of the IT system implementation with advanced mathematical optimisation for job scheduling. The proposed genetic procedure leads to the Pareto front, and the application of the multiple criteria decision aiding (MCDA) approach allows extraction of the final solution. Definition of the key performance indicator (KPI) reflecting relevant features of the solutions, and the efficiency of the genetic procedure provide the Pareto front comprising the representative set of feasible solutions. The application of chosen MCDA, namely elimination et choix traduisant la réalité (ELECTRE) method, allows for the elicitation of the decision maker (DM) preferences and subsequently leads to the final solution. This solution fulfils all of the DM expectations and constitutes the best trade-off between considered KPIs. The proposed method is an efficient combination of genetic optimisation and the MCDA method.

The Intuitionistic Fuzzy FlowSort Method for Multicriteria Group Decision Making

The real life problems are multidimensional in nature and may involve some ambiguity when it comes to decision making. It is, therefore, difficult to design the evaluation criteria precisely and determine the exact value of the attributes in the multicriteria analysis. The intuitionistic fuzzy set (IFS) achieved great success in treating this kind of ambiguity in a great deal of research. The study of sorting problems is an active research issue in the multiple criteria decision aid (MCDA) area. This paper investigated one of the sorting methods, FLOWSORT, and extended it to the multicriteria group decision making based on the output aggregation of the individual sorting results. The rating of each alternative was described through linguistic terms that can be expressed in triangular intuitionistic fuzzy numbers. An illustrative example as well as an empirical comparison with other multi-criteria decision making methods were carried out to validate our extension.

Combined optimisation and MCDA based solution of the tram depot location problem

This paper deals with an issue of technical facilities location in a public transport system. The decision problem is formulated as a selection of the most advantageous alternative, i.e. the location of a new tram depot among the already existing facilities of this type. The selection is preceded by the evaluation of the alternatives. The assessment is not a trivial task, because there are many groups of interest with usually contradictory points of view. Therefore, the evaluation of the new tram depot locations should represent different aspects, e.g., economical, technical, environmental, and organizational. To handle such a complex decision problem the authors propose a methodology, which is a composition of the optimisation and multiple criteria evaluation techniques. The developed methodology is experimentally applied to the selection of one out of five tram depot locations in the public transport system of the city of Poznan, Poland. All the computational experiments are performed by means of optimization and multiple criteria decision aiding (MCDA) methods and tools, i.e. a linear optimization engine Solver Premium Platform and AHP method with its application AHORNsimple. The calculations are the basis for recommending the location of a new depot in the central part of the transport system network, which is a reasonable solution taking into account, e.g. the proximity of the main railway line, the possibility of triple distribution of the transport means from depot. The proposed methodology of the decision problem solution gives also an opportunity to create the hierarchy of considered tram depot locations as well as to compare the position in the ranking of the best solution with the existing one. Since the proposed methodology assumes the selection of the most suitable MCDA method to the problem under consideration and the decision maker’s preferen¬ces, it guarantees that the result of analysis becomes reliable and the decision aiding process is credible.

The Intuitionistic Fuzzy FlowSort Method for Multicriteria Group Decision Making

The real life problems are multidimensional in nature and may involve some ambiguity when it comes to decision making. It is, therefore, difficult to design the evaluation criteria precisely and determine the exact value of the attributes in the multicriteria analysis. The intuitionistic fuzzy set (IFS) achieved great success in treating this kind of ambiguity in a great deal of research. The study of sorting problems is an active research issue in the multiple criteria decision aid (MCDA) area. This paper investigated one of the sorting methods, FLOWSORT, and extended it to the multicriteria group decision making based on the output aggregation of the individual sorting results. The rating of each alternative was described through linguistic terms that can be expressed in triangular intuitionistic fuzzy numbers. An illustrative example as well as an empirical comparison with other multi-criteria decision making methods were carried out to validate our extension.