Multi-criteria Decision Analysis Methods Research Articles

Delineation of Groundwater Potential Zones Using Geospatial Techniques. Case Study: Roman City and the Surrounding Area in the Northeastern Region, Romania

Effective groundwater management is crucial under the current climatic conditions, addressing both qualitative and quantitative aspects. An important step in delineating groundwater potential zones involves remote sensing (RS) data and geographic information systems (GISs), facilitating resource assessment, and the implementation of suitable field data management. This study introduces the delineation of potential groundwater zones using seven layers and the Multi-Criteria Decision Analysis (MCDA) method. Satty’s Analytic Hierarchy Process (AHP) was employed to rank the seven selected parameters, contributing to the advancement of groundwater research and resource assessment. All seven thematic layers (Rainfall, Geology, Land Use/Land Cover, Drainage Density, Elevation, Slope, and Soil) were prepared and analyzed to delineate groundwater potential zones. The resulting groundwater potential zone map was categorized into four classes, Very Good, Good, Moderate, and Poor, covering areas of 81.53 km2 (45.1%), 56.36 km2 (31.2%), 19.54 km2 (10.8%), and 23.17 km2 (12.8%) of the total area, respectively. The accuracy of the output was validated by comparing it with information on groundwater prospects in the area, and the overall accuracy of the method was approximately 72%. High-yield boreholes were drilled and concentrated in the Very Good groundwater potential zones, while low-yield ones were developed in the Poor areas.

7956 Assessing Factors Influencing Polycystic Ovarian Syndrome Using Hybrid Multi-Criteria Decision Analysis

Abstract Disclosure: N.B. Trivedi: None. J. Amin: None. M. Patel: None. S. Patel: None. P. Rana: None. A. Rajani: None. V. Dave: None. Polycystic Ovarian Syndrome (PCOS) is a complex hormonal disorder having multifactorial origins. This study aims to apply Multi-Criteria Decision Analysis (MCDA) methods like Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Hierarchy Process (AHP) to prioritize factors influencing PCOS. In our study, 10 criteria impacting PCOS were identified through extensive literature review. AHP and DEMATEL were used via matrices, assessed by 13 experts. AHP determined relative importance/weights while DEMATEL assessed interconnectedness among criteria. AHP revealed that family history held the highest weight (0.22) while Insulin resistance held the lowest weight at 0.04. The results obtained from DEMATEL indicated that factors like obesity, insulin resistance, diet, psychological stress and sedentary lifestyle displayed higher D+R values, suggesting a strong influence on other factors; indicating their significant impact on the syndrome. The division of criteria into cause and effect groups based on D-R values revealed that family history, environmental factors, diet, sedentary lifestyle and psychological stress appear to be causative and sleep disturbances, low-grade inflammation, excess androgen and insulin resistance seem to be the consequences of PCOS. In AHP the criteria are treated as being independent to each other and their hierarchal order is determined. However, in realistic scenarios factors may have some sort of interdependency/relationship with one another. To identify that interdependency which AHP fails to focus on, DEMATEL was used. DEMATEL ensues that Insulin resistance along with obesity, sedentary lifestyle, and psychological stress show strong level of influence and interconnectedness, indicating a significant impact on the syndrome. However, insulin resistance, when treated as an independent criterion in AHP, displays lower weightage. In the end, improved PCOD treatment would come from targeted interventions and strategies that centre on the variables with a higher D+R value. When the criteria were considered independent of each other in AHP, highest weight is conferred to family history (despite of it having moderately low level of influence/interconnectedness on other criteria as ensued from DEMATEL) thus prompting more research in the direction of investigating genetic and familial factors associated with PCOS. This could consequently lead to early diagnosis and effective long term management. The combined methodology (DEMATEL+AHP) ensures comprehensive assessment of PCOS factors. Previous studies might have focused on individual factors separately or failed to adequately explore their interrelationships and relative importance. MCDA can be further employed in healthcare to assess conflicting treatment modalities, risk factors etc. for other multifactorial disorders. Presentation: 6/2/2024

A Fuzzy PROMETHEE Method for Evaluating Strategies towards a Cross-Country Renewable Energy Cooperation: The Cases of Egypt and Morocco

Recognising the urgency of addressing climate change and the imperative to mitigate its effects, the European Union (EU) has embarked on a transformative journey to reshape its energy landscape, with a pronounced emphasis on harnessing renewable energy sources (RESs) and augmenting their contribution to electricity generation. To propel Europe towards sustainable development through its energy transition, EU member states are encouraged to foster collaborative efforts on a European scale, inviting neighbouring countries to participate in joint ventures aimed at leveraging RESs for electricity generation. Consequently, it becomes imperative to evaluate the potential depth of cooperation among these nations, assessing how such partnerships can align with Europe’s overarching objectives while fostering mutually beneficial conditions. This paper seeks to undertake a thorough analysis and evaluation of the potential impacts of such cooperation, both in advancing RES objectives and in promoting broader cooperation goals concerning the countries involved. The appropriate methodological framework has been developed, utilising and implementing the fuzzy PROMETHEE multicriteria decision analysis method, to address the problem’s multidimensional character, intending to implement an appropriate action plan and promote production from RESs. The methodology has been applied to assess alternative strategies in two case study countries, Morocco and Egypt, while important outcomes have emerged towards the successful implementation of cooperation mechanisms promoting RES.

Sustainability Assessment Methods for the Transport Sector Considering the Life Cycle Concept—A Review

This paper presents a summary and review of life cycle sustainability assessment (LCSA) methods for the transport sector. The paper provides a comprehensive overview of articles that employ a variety of methods for assessing sustainable development in the transport sector, taking into account the economic, social, and environmental dimensions. In the sustainability assessment of transport, three methods were evaluated: life cycle assessment (LCA), life cycle cost analysis (LCC), and social life cycle assessment (SLCA). An overview of sustainability assessment methods in transport and a review of the indicators used in the life cycle sustainability assessment was conducted. It was found that the selection of indicators within the LCSA for assessing various aspects of sustainable development is dependent on various geographic and policy contexts. An overview of the application of multi-criteria decision analysis (MCDA) methods to assess LCSA in the transport sector was performed. MCDA methods are used to support decision-making regarding the selection of the most sustainable transport options and allow for the simultaneous consideration of multiple criteria, enabling a more sustainable assessment of different transport options. MCDA methods help to rank alternative transportation fuels and help decision-makers consider indicators encompassing economic, environmental and social aspects.

Supporting strategy for investment evaluation of photovoltaic power generation engineering projects using multi-criteria decision analysis methods

This scientific study examines the evaluation of photovoltaic power generation projects through the application of multi-criteria decision analysis methods. Two groups of large-scale grid-connected PV power generation system projects with a nominal power of 50 MW and 500 MW respectively were analyzed and evaluated. These systems were designed to be installed in the same wider geographical area in northern Greece, but they were differentiated in the part of the PV circuit. Twelve systems were analyzed in which either monocrystalline silicon panels or poly-crystalline silicon panels or bifacial photovoltaic panels were to be installed. In these systems either central photovoltaic inverters or photovoltaic string inverters were considered for installation. The following criteria were used to evaluate the investment in these projects. These criteria were related to the profitability, the financial cost, the technical level, and the electrical energy production of the systems and these were the initial investment cost, the operation and maintenance cost, the levelized cost of electricity, the net present value, the internal rate of return, the capital recovery or payback period, the technical level of the photovoltaic circuit, the technical maturity of the photovoltaic circuit, and the annual electricity production. The evaluation of these criteria was initially conducted with fixed weighting coefficients followed by a sensitivity analysis of these weighting coefficients. The results of the evaluation using the PROMETHEE, AHP and TOPSIS multi-criteria decision analysis methods showed that the PV power generation systems which should be preferred are those with increased nominal power, where monocrystalline silicon technology panels are employed following the central inverter topology.

Interdependencies in industry 4.0 maturity: Fuzzy MCDA analysis for open innovation in developing countries

The emergence of Industry 4.0 (I4.0) is reshaping industries worldwide, driven by rapid technological progress and the need for open innovation. This study focuses on understanding the interdependencies of driving factors of I4.0 maturity in developing countries using Fuzzy Multi-Criteria Decision Analysis (MCDA) methods. By analyzing Indonesia, Pakistan, and Venezuela, the research aims to foster open innovation and address the unique challenges these nations face in adopting I4.0 technologies. I4.0 maturity models are essential for evaluating current maturity levels and identifying areas for improvement. However, the complexity and interdependence of various factors—ranging from data science and technology to policy, governance, and open innovation dynamics, such as social open innovation and the role of SMEs—complicate this process. This study employs Fuzzy TOPSIS and Fuzzy DEMATEL to identify the critical factors influencing I4.0 maturity and analyze their interdependencies and prioritization. The results indicate that 'Data and Information' and 'Willingness to Change' are crucial across all countries, while strategic differences between large enterprises and SMEs highlight the need for tailored approaches. This research highlights the importance of continuous IT investment, digital leadership, collaborative ecosystems, and agile strategies in fostering open innovation and driving I4.0 adoption. This research contributes to the theoretical and practical understanding of I4.0 maturity, offering valuable insights for practitioners and academics to explore the dynamic interactions of I4.0 factors and their impact on operational efficiency.

Adaptive multi-criteria decision making for electric vehicles: a hybrid approach based on RANCOM and ESP-SPOTIS

In today’s globalized technological area, aligning decisions with customer preferences is crucial yet challenging due to the complexities and uncertainties involved. Multi-Criteria Decision Analysis (MCDA) serves as a vital tool for constructing support systems that cater to customer-centric trends. While existing MCDA methods vary in their calculation concepts, some prioritize ideal solutions, while others accommodate personalized preferences within dynamic decision contexts. Moreover, determining the relevance of criteria based on expert knowledge adds another layer of personalization to the evaluation process, further individualizing decision-making. However, current decision models often fail to integrate these concepts, leaving a gap in how recommendations can be enhanced when both are combined. To address these challenges, this paper introduces an innovative approach integrating Ranking Comparison and Expected Solution Point Stable Preference Ordering Towards Ideal Solution methods. This hybrid model incorporates personalization into multi-criteria evaluation, catering to individual preferences. By representing customer preferences through two distinct measures, the proposed approach ensures personalized recommendations aligned with decision-makers’ needs. The efficacy of the hybrid model was validated through its application to the electric vehicle selection problem. The verification process highlighted potential disparities compared to other multi-criteria approaches, establishing a consumer preference-based Decision Support System approach for more precise and personalized selection recommendations.

Intelligent characteristic objects method (INCOME): a data knowledge-based multi-criteria decision analysis

Multi-criteria decision analysis (MCDA) methods are vital in assessing decision variants under multiple conditions. However, involving domain experts in developing decision models can be challenging and costly, necessitating more scalable and independent solutions. This paper introduces the intelligent characteristic objects method (INCOME), which combines the k-Nearest Neighbor (kNN) algorithm and the COMET method to create a theoretical decision-maker for comparing characteristic objects (COs). INCOME overcomes limitations of classical MCDA methods, such as the TOPSIS approach, which struggles with complex functions and non-monotonic modeling. INCOME influences data-based knowledge to provide a robust framework for assessing decision options. The integration of the COMET method and kNN algorithm enables improved modeling of decision functions based on evaluated data, increasing the flexibility and independence of the INCOME approach. A case study assessing gas power plants based on four criteria is presented to validate the performance of the INCOME method. The results demonstrate high correlations with the reference model and slightly higher classical approaches like TOPSIS and TOPSIS-COMET. However, INCOME exhibits greater stability and flexibility by utilizing all available data instead of relying on limited expert knowledge. The proposed INCOME approach offers several advantages, including creating a continuous decision model, resistance to the Rank-Reversal phenomenon, and the potential for replacing domain experts with artificial experts. This study highlights the effectiveness of INCOME in Multi-Criteria Decision Analysis. It suggests future research directions, such as parameter selection and testing in different decision-making problems.

Developments and evolution of housing architecture in the post-Corona era with a health-oriented approach

This research, with a descriptive-analytical approach and in a pragmatic way, examines the evolution of housing architecture after COVID-19. Thirty experts in architecture and urban planning made up its statistical population. A combination of artificial intelligence algorithms, tree data mining techniques, and the FTOPSIS multi-criteria decision analysis method have been applied to the information analysis process. A panel of experts was formed to gather information, and the criteria of housing design that had evolved in the post-corona period were identified by designing a questionnaire on the Likert scale (i.e., a psychometric scale named after its inventor)—the primary influential factors in the pattern of housing architecture after the Corona period were determined. The findings showed a significant correlation between housing architecture in the post-corona period and people's health patterns. Results showed that the factors that have the most significant impact on the evolution of housing in the region under investigation include the new function of housing (0.152), changing behavioral interactions (0.152), design criteria (0.145) and the passive ventilation factor with a score of 0.113. Other results showed that the physical distance as well as quarantine and isolation components obtained a score of 0.359 and 0.328, respectively, and were identified as the most critical developments in housing architecture. Finally, data mining findings showed that the spatial quality variable in the first phase had the most significant influence on the growth and fortification of the evolution of housing components at the study area level and the promotion of the health of people in the community in the post-pandemic period.

Combining Bayesian Networks and MCDA methods to maximise information gain during reconnaissance in emergency situations

In the immediacy of an event that disrupts the operation of an infrastructure, the time between its occurrence and the arrival of qualified personnel for emergency response can be valuable. For example, it can be used for gathering information about the status of the infrastructure by using automated reconnaissance devices. In an operation which precedes the intervention of human first responders, such devices can gather information about the situation, providing knowledge about the locations of stressors (e.g. fire), about the inaccessibility of parts of the infrastructure or about the presence of hazardous materials. In this study, we show how a Bayesian Networks can be used for knowledge representation and how it can be combined with methods from the realm of Multi-Criteria Decision Analysis (MCDA) for situation reconnaissance and route-optimisation in emergency situations, where different criteria (current belief about the location of zones of special interest, such as emergency exits, distance to the next point of interest, etc.) can be considered. As an example, we consider the case of an outbreak of a fire in a building. A pedantic check of all rooms by an automated reconnaissance device would take too long and thus delay intervention. Due to the limited time in which the building can be explored, the route is optimised to gather the greatest possible amount of information in the available time window. Results show how it is possible to maximise the information collected in a limited time window. This is done by discovering the location of fire and any hazardous materials through causal inferences automatically calculated by the Bayesian network. Route optimisation is facilitated by sequential MCDA using a parameter selection that meets the priorities of the specific application example.

Policy strategy: Nickel mining contribution to rural development equity and social violence minimization in Kolaka Regency

The lack of attention from mining companies to the majority of areas still affected by mining activities can result in regional economic disparities and high levels of social violence. It is crucial to have policy strategies for mining contributions to rural development equity and social violence reduction through CSR assistance and other aid funds. This research employs the Multi-Criteria Decision Analysis method using the MULTIPOL analysis tool. Recommended action programs include the construction of schools, provision of scholarships, job openings, business capital, and infrastructure development, supported by strong regulations and law enforcement. Cracking down on illegal mining permits is essential to reduce environmental damage. Holistic and sustainable integration policies, alongside effective law enforcement, are necessary to achieve the goals of equitable development and social violence reduction. These steps should be reinforced with incentives for traditional/community leaders and increased police/military presence in villages within the next 2 years, particularly in zones 2 and 3 of the mining areas. Failure to implement these measures could escalate social violence, jeopardize security, and impede the operations of mining companies in Kolaka. The findings of this research support the priority of security and orderliness in development and underscore the importance of diverse research methods for mining area development policies.

Multi-Criteria Decision Analysis to Evaluate the Geographic Potential of Alternative Photovoltaic Types

This paper contributes to the expansion of green energy production, which is being pursued in order to mitigate climate change and carbon emissions from energy production. It addresses the delineation of areas that are suitable for the application of photovoltaics in the context of agricultural areas, water bodies, and parking spaces. Three specific photovoltaic types are examined in order to find out which criteria influence their geographic potential and whether spatial multi-criteria decision analysis methods are suitable for identifying suitable areas. The proposed approach consists of four steps: (1) collecting factors through expert interviews and questionnaires; (2) mapping the criteria to the spatial datasets; (3) deriving weighted scores for individual criteria through expert interviews; (4) applying the multi-criteria decision analysis method to compute and aggregate the final scores. We test our methodology at selected sites in the state of Styria, Austria. The test sites represent the topographical characteristics of the state and are about 5% of the size of Styria, approximately 820 km2. The key contributions are a weighted set of criteria that are relevant for the geographic potential of alternative photovoltaic types and the developed methodology to determine this potential.

An integrated evaluation framework for Environmental, Social, and Governance-driven social media performance through Multi-criteria Decision-Analysis

Social media is crucial in providing data for networking with individuals, companies, or industries. This study deals with the topic of Environmental, Social and Governance (ESG). These can be seen as a set of standards which impacts a corporation’s structure and performance. Twenty years ago, when ESG started to gain notoriety, ESG initiatives were labeled as a tool to ensure ‘ethical’ and/or ‘responsible’ investing. This study collects data from LinkedIn company profiles on the industry, the staff, the number of followers, and the state where the company has its headquarters. In addition, the media’s take on pending legislation on ESG was assessed. Based on data from 537 companies, indicators were created, either normalized, the median, or ranked, and analyzed with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The current work provides information on this variation of ESG activity within the different states in the USA. Multi-criteria Decision Analysis (MCDA) was applied by weighing four criteria. The overall assessment was presented depending on the combined weighted performance score of the four criteria. The results showed that 36% of the companies active within the ESG landscape represent the “Financial Services” and “Management Consulting” industries. 65% of the investigated sample represented companies with up to 10 employees. Moreover, eight states perform well on both median of staff and followers (in alphabetical order: Michigan, Mississippi, Missouri, New Hampshire, New Mexico, Ohio, Oklahoma, and Wisconsin). Lastly, through the MCDA method, it was also observed that there are not many variations when different weights are applied to the four criteria: i.e., “the normalized number of companies by state,” “the median of the followers of the company per state,” “the median of the staff of the company per state,” and the “sentiment of the legislation.” Finally, the top five highly ranked states considering ESG visibility on LinkedIn through their companies are Ohio, Michigan, Delaware, Georgia, and Pennsylvania. The results indicate that companies can more proactively advertise themselves as compliant with current ESG principles. This methodology can also be applied to other topics and regions to assess online activity in line with existing indicators.

Sustainable solutions: using MCDM to choose the best location for green energy projects

Purpose This study aims to find the best location for constructing green energy facilities in India and reducing CO2 emissions. Incorporating green energy is a priority for many countries under the Paris Agreement. This task is challenging due to factors that affect implementation, and making the wrong decision wastes resources. India’s goals are net-zero emissions by 2070 and 50% renewable electricity by 2030. Other developing nations should emulate India’s renewable energy strategy. India ranks fourth in renewable energy and wind power, and fifth in solar power capacity. This research aims to identify the best locations in India for implementing green energy projects. Design/methodology/approach To identify the optimal green energy implementation sites in India, this research uses the hybrid multicriteria decision analysis (MCDA) in an uncertain environment. This research uses the Delphi method to identify the most suitable green energy implementation sites in India. It adapts the elements for this investigation. In addition, the utilization of the Fermatean fuzzy weighted aggregated sum product assessment technique is implemented to effectively prioritize the factors that impact the selection of these sites. This study used the MEREC method (method based on the removal effects of criteria) to identify the most suitable areas in India for implementing green energy. The highest accuracy is attained through the amalgamation of these hybrid methods. Findings Following the computation data by hybrid MCDA in uncertainty environment, NP Kunta in Andhra Pradesh emerges as the recommended green energy site among the 11 considered. Also among the factors political strategies and objectives hold the highest priority among them. Originality/value This study is pioneering in its efforts to provide a comprehensive perspective on the development and management of green energy operations in India. The study proves advantageous for diverse sites in the successful adoption and management of green energy. The study is additionally valuable in informing policy development aimed at promoting the use of green energy by employees through the utilization of MCDA methods in uncertain environments.

A multi-dimensional sensitivity analysis approach for evaluating the robustness of renewable energy sources in European countries

The necessity of making reliable decisions is exceptionally important in the field of sustainable development, particularly when evaluating the management of renewable energy sources. A thorough analysis becomes imperative in complex decision problems where various factors influence outcomes. Multi-Criteria Decision Analysis (MCDA) methods have emerged as valuable tools for addressing such challenges, enabling decision-makers to navigate through conflicting criteria and make informed choices. Combined with sensitivity analysis approaches, comprehensive assessments can be achieved, ensuring the robustness of decision-making processes. To increase the reliability of the results, different aspects of input data fluctuations should be examined to provide a broader view of the stability of the results.This paper proposes a comprehensive multi-dimensional sensitivity analysis approach to assess the robustness of renewable energy source (RES) development in selected European countries. By evaluating RES management in terms of electricity and energy consumption and generation, the study addresses key components of sustainable development. It offers a holistic perspective on result reliability and stability by analyzing five sensitivity dimensions: a comparative analysis of four MCDA methods, varying criteria weights scenarios, probabilistic modifications, criteria relevance identification, and ranking stability. This approach enhances decision-making in sustainable energy development, providing valuable insights for policymakers on prioritizing sustainable technologies. The provided open-source implementation promotes transparency and accessibility in decision support systems.

Designing Food Hubs for Territories of Proximity: Assessing the Spatial, Ecological, and Cultural Potentials of Places through Multi-Criteria Decision Support Systems

Logistics, distribution models, and landscapes of food production strongly influence the space of our cities and territories. In addition to the network of large-scale retail distribution that is diffused in urban and non-urban areas, with this contribution, we study the presence of new forms of the local and sustainable distribution of food (such as Alternative Food Networks, and community-supported agriculture). Studying and understanding how these distribution models can support and be integrated within a landscape planning and design approach is explored through the Analytic Hierarchy Process (AHP), a multi-criteria decision analysis method. Through the specific focus of a Food Hub localization, the aim is to demonstrate how distribution models can not only support but also integrate into landscape planning and design. The fundamental objectives for structuring and locating a Food Hub can be organized under three strategic objectives: pursuing the benefit of people, the planet, and profit. The choice of one distribution method over others, or what is the best location and condition for distribution centers, is the question we have tested with the collaboration of “L’Ortazzo” Association. The case study is a solidarity purchasing group located in the upper Valsugana valley area (Trentino Region, Italy), a supra-municipality reality involving about a hundred families that, currently, do not have a physical distribution center.

Systematic risk ranking of microbiological hazards in infant foods

Ensuring food safety, particularly for vulnerable groups, like infants and young children, requires identifying and prioritizing potential hazards in food chains. We previously developed a web-based decision support system (DSS) to identify specific microbiological hazards (MHs) in infant and toddler foods through a structured five-step process. This study takes the framework further by introducing systematic risk ranking (RR) steps to rank MH risks with seven criteria: process survival, recontamination, growth opportunity, meal preparation, hazard-food association evidence, food consumption habits of infants and toddlers in the EU, and MH severity. Each criterion is given a semi-quantitative or quantitative score or risk value, contributing to the final MH risk calculation via three aggregation methods: semi-quantitative risk scoring, semi-quantitative risk value, and outranking multi-criteria decision analysis (MCDA). To validate the criteria and ranking approaches, we conducted a case study to rank MH risks in infant formula, compared the results of the three risk ranking methods, and additionally evaluated the ranking results against expert opinions to ensure their accuracy. The results showed strong agreement among the three methods, consistently ranking Salmonella non-Typhi and Cronobacter spp. and Shiga-toxin-producing Escherichia coli as the top MH risks in infant formulae, with minor deviations. When MHs were ranked after an initial hazard identification step, all three methods produced nearly identical MH rankings, reinforcing the reliability of the ranking steps and the selected criteria. Notably, the risk value and MCDA methods provided more informative MH rankings compared to the risk scoring method. The risk value and risk scoring methods were implemented into an online tool, called the MIcrobiological hazards risk RAnking decision support system (Mira-DSS), available at https://foodmicrobiologywur.shinyapps.io/MIcrobial_hazards_RAnking/. In conclusion, our framework enables the ranking of MH risks, facilitating intervention comparisons and resource allocations to mitigate MH risks in infant foods, with potential applicability to broader food categories.

A comparative study of different methods of Multi-Criteria Decision Analysis (MCDA) with spatial reference to select of suitable sites for treated wastewater of irrigated perimeters

A comparative study of different methods of Multi-Criteria Decision Analysis (MCDA) with spatial reference to select of suitable sites for treated wastewater of irrigated perimeters

A Fuzzy Entropy Approach for Portfolio Selection

Portfolio management typically aims to achieve better returns per unit of risk by building efficient portfolios. The Markowitz framework is the classic approach used when decision-makers know the expected returns and covariance matrix of assets. However, the theory does not always apply when the time horizon of investments is short; the realized return and covariance of different assets are usually far from the expected values, and considering additional factors, such as diversification and information ambiguity, can lead to better portfolios. This study proposes models for constructing efficient portfolios using fuzzy parameters like entropy, return, variance, and entropy membership functions in multi-criteria optimization models. Our approach leverages aspects related to multi-criteria optimization and Shannon entropy to deal with diversification, and fuzzy and fuzzy entropy variants provide a better representation of the ambiguity of the information according to the investors’ deadline. We compare 418 optimal portfolios for different objectives (return, variance, and entropy), using data from 2003 to 2023 of indexes from the USA, EU, China, and Japan. We use the Sharpe index as a decision variable, in addition to the multi-criteria decision analysis method TOPSIS. Our models provided high-efficiency portfolios, particularly those considering fuzzy entropy membership functions for return and variance.

The Quest for an ESG Country Rank: A Performance Contribution Analysis/MCDM Approach

Utilizing Multi-Criteria Decision Analysis (MCDA) methods based on environmental, social, and governance (ESG) factors to rank countries according to these criteria aims to evaluate and prioritize countries based on their performance in environmental, social, and governance aspects. The contemporary world is influenced by a multitude of factors, which consequently impact our lives. Various models are devised to assess company performance, with the intention of enhancing quality of life. An exemplary case is the ESG framework, encompassing environmental, social, and governmental dimensions. Implementing this framework is intricate, and many nations are keen on understanding their global ranking and avenues for enhancement. Different statistical and mathematical methods have been employed to represent these rankings. This research endeavors to examine both types of methods to ascertain the one yielding the optimal outcome. The ESG model comprises eleven factors, each contributing to its efficacy. We employ the Performance Contribution Analysis (PCA), Clifford algebra method, and entropy weight technique to rank these factors, aiming to identify the most influential factor in countries’ ESG-based rankings. Based on prioritization results, political stability (PSAV) and the voice of accountability (VA) emerge as pivotal elements. In light of the ESG model and MCDA methods, the following countries exhibit significant societal impact: Sweden, Finland, New Zealand, Luxembourg, Switzerland, Denmark, India, Norway, Canada, Germany, Austria, and Australia. This research contributes in two distinct dimensions, considering the global context and MCDA methods employed. Undoubtedly, a research gap is identified, necessitating the development of a novel model for the comparative evaluation of countries in relation to prior studies.