Multi-criteria Decision Research Articles

Multi-centre Modified Delphi Exercise to Identify Candidate Items for Classifying Early-Stage Symptomatic Knee Osteoarthritis

To generate a list of candidate items potentially useful for discriminating individuals with Early-stage Symptomatic Knee Osteoarthritis (EsSKOA) from those with other conditions and from established osteoarthritis (OA), and to reduce this list based on expert consensus. We conducted a three-round online international modified Delphi exercise with OA clinicians and researchers ("OA experts"). In Round 1, participants reviewed 84 candidate items and nominated additional item(s) potentially useful for EsSKOA classification; those nominated by ≥3 participants were added. In Round 2, participants rated perceived usefulness of 108 items (1 [not at all useful] to 9 [extremely useful]). In Round 3, participants could revise their ratings after reviewing Round 2 group median and quartiles. Following Round 3, we retained items with a median usefulness score >5 and ≥33.3% of participants categorized the item as useful (7 to 9), overall and in subgroup analysis by clinician field. There were 128 participants in Round 1 and 113 (88%) completed all rounds. We retained 77 items that spanned multiple domains (demographics, symptoms, physical exam, performance-based measures, imaging, laboratory investigations, and gross inspection/arthroscopy). Highly rated items included (median usefulness score): prior knee joint injury (8), diagnosis of OA in a different joint (7), and activity-related knee pain (7). The interquartile range was most often 3. We identified 77 items that OA experts consider potentially useful for EsSKOA classification. The results highlight experts' uncertainty around item usefulness. Next, candidate items will be further assessed and reduced using data-driven and multicriteria decision analysis methods.

GIS and hybrid multi-criteria decision analysis based urban flood risk assessment in Hanchuan City, Hubei Province, China

GIS and hybrid multi-criteria decision analysis based urban flood risk assessment in Hanchuan City, Hubei Province, China

Treatment of cancer patients by generalizing a Fermatean normal vague set with aggregation operators

Multi-attribute decision-making problems can be solved using a Fermatean vague set. Fermatean vague sets are extension of vague sets. We initiated generalized Fermatean vague weighted averaging, generalized Fermatean vague weighted geometric, power generalized Fermatean vague weighted averaging and power generalized Fermatean vague weighted geometric. The algebraic structures such as associative, distributive, idempotent, bounded, commutativity and monotonicity properties are satisfied by generalized Fermatean vague numbers. We discussed some mathematical properties of these sets, as well as the Hamming distance and Euclidean distance. An algorithm that uses aggregation operators to solves multi-attribute decision-making problems. The fields of computer science and medicine are essential for medical diagnosis research. Choosing cancer treatment is a complex process. Patients and health care professionals must communicate effectively to make the right decision about their cancer treatment. There are a number of factors that influence patients treatment decisions. As far as cancer patients are concerned, there are five types of cancer patients. Many different treatments are currently available and they are often combined as part of an overall treatment plan involving various treatment options. Patients with which cancer can be treated in various ways, such as cystoscopy, biopsy, blood tests and CT scan of the urogram. We intended to choose the best treatment based on our comparisons and options. Thus, it is evident that natural number q has a significant influence on the models. Additionally, flowchart based multi-criteria decision-making is offered and applied to a numerical example to show the efficiency of the recommended approach. The results are evaluated for different values of the parameter. Moreover, a comparative analysis has been performed to illustrate the superior outcomes of the suggested approach in comparison with existing methodologies.

Optimizing public investments: A Sustainable Economic, Environmental, and Social Investment Multi-criteria Decision Model (SEESIM)

One of the most important aspects for the economic and social growth of a country is high efficiency of public investments. This imperative has never been more critical, especially in the context of Europe and Italy, where the integration of environmental, economic, and social goals is essential to address the multifaceted challenges of the 21st century. This research introduces the Sustainable Economic, Environmental, and Social Investment Model (SEESIM), a novel multi-criteria decision-making framework designed to ensure the sustainability of projects as their validity over time, especially from a National Recovery and Resilience Plan perspective. Utilizing the Analytic Hierarchy Process (AHP), SEESIM integrates environmental, economic, and social criteria as useful tool to evaluate a project in the post-funding phase, ensuring a balanced approach to efficiency and sustainability. The model is adaptable to various regional contexts, providing a transparent and replicable methodology for assessing the impacts and costs of public investments. An experimental scenario demonstrates SEESIM’s utility in guiding investment decisions, highlighting its potential to significantly enhance the effectiveness of public resources in achieving long-term sustainability goals. SEESIM represents a pivotal advancement in sustainable development strategies, offering a comprehensive tool for integrating multi-dimensional sustainability criteria into public investment decisions.

Setting priorities for police operations by using a multicriteria decision approach with partial information

Abstract The paper presents a real-world decision-making problem in the context of the Brazilian Federal Police (BFP), which consists of establishing priorities for Police Operations. This study deals with allocating resources to trigger police operations that, while demanding more resources and specialized techniques, also promote the effectiveness of police actions more conspicuously and directly. Driven by the Value Focused Thinking methodology, the objectives and values of the Federal Police were structured and represented by criteria, which let police operations be evaluated and prioritized. The FITradeoff method was applied to rank the operations, with an innovative methodological perspective, which integrates two different paradigms for preference modelling: elicitation by decomposition and holistic evaluations. A mathematical modelling approach is presented to deal with the combination of both types of information obtained by the Decision Maker (DM), in order to search for dominance relations between alternatives. This study shows how incorporating holistic judgments in the process can be useful for tightening the decision process, since the inequalities obtained with holistic judgments have a high impact on the space of weights compatible with the DM’s preferences. A clear conceptual framework is presented for the analysis, showing how the application of such a combined approach to prioritizing special police operations adds a step to the decision-making processes and policies of the BFP, thereby broadening the managerial implications of tackling public security issues by seeking to solve them using management mathematics.

The performance model of logistic distribution centers: Quality function deployment based on the Best-Worst Method.

This paper aims to evaluate the performance of logistic distribution centers (LDCs) by Quality Function Deployment (QFD) based on the Best-Worst Method (BWM). The originalities of this paper include: (1) exploring five constructs with 20 customer requirement attributes (CRAs) for LDCs' operations based on the SERQUAL model, (2) using the QFD model to identify five primary divisions and 18 corresponding service technical requirements (STRs) for LDCs' operations, (3) recognizing the top five STRs should be prioritized for the allocation of limited resources from House of Quality (HoQ), including cargo order (7.15%), value-added activities (6.68%), document preparation (6.31%), consolidating and assembling (6.10%), and document management (6.00%), (4) applying the Best-Worst Method (BWM) to estimate CRAs' relative weight. The proposed research model can provide a methodological reference to the relevant literature in association with logistics operations and multiple-criteria decision analysis (MCDA).

ENVIRONMENTAL RISK ASSESSMENT OF TRANSPORTATION INFRASTRUCTURE DEVELOPMENT USING GIS IN LAGOS STATE

Transportation infrastructure development poses significant environmental risks, including habitat disruption, air and water pollution, and climate change. This study develops a GIS-based framework for ecological risk assessment of transportation infrastructure development. This research identifies high-risk areas and prioritizes mitigation measures by integrating spatial analysis, multi-criteria decision analysis, and expert consultations. The GIS-based approach evaluates environmental sensitivity, proximity to ecosystems, and potential hazards. Results provide valuable insights for sustainable transportation planning, policy-making, and ecological management. This study explored its application in solving Environmental Risk Assessment of Transportation Infrastructure Development GIS application in environmental Risk Assessment monitoring has mainly been divided into three aspects: water, soil, and atmosphere. Based on ArcGIS 10.1 software and ArcGIS 9.3.1 version, GIS has been applied in Transportation system monitoring and soil heavy metal concentration monitoring, respectively. A major function of planners is to promote the best use of a community's land and resources for different construction projects; especially critical are infrastructure projects on which economic development relies. Transportation projects typically involve both environmental and economic issues facing a community as it grows and changes. Considerations of sustainability, as well as the widespread use of collaborative planning, design, and construction, require tools that facilitate long-term impact analysis and easy communication among built-environment professionals. Environmental impact assessment (EIA) is instrumental in studying transportation project impacts, but it is a time-consuming process because of the large number of dependent and independent variables involved. A vulnerability grade map and road distribution map were produced, providing an overall vulnerability score for each of the three planning alternatives considered. A road alignment was indicated as the preferred corridor, which was further recommended for a project development and environmental (PD&E) study.

The Use of a Multi-Criteria Decision Analysis Method to Select the Most Favourable Type of Fish Pass in Mountainous Areas

Fish passes are a key element enabling the migration of aquatic organisms in the context of restrictions resulting from the presence of weirs. Multi-criteria decision analysis, AHP, and Rembrandt methods were used to assess the effectiveness of fish passes on mountain rivers. Three common types of fish passes were considered: slotted fish pass, block ramps, and a circulation channel with boulders. The results of the study indicated that block ramps proved to be the most favourable solution, achieving the highest preference values in both methods (Rembrandt: 0.77, AHP: 0.63). The key factors influencing the effectiveness of the fish passes are the availability of space and the water requirements, which reached values of 0.38 and 0.27 in the Rembrandt method and 0.33 and 0.28 in the AHP method, respectively. The differences between the results of both methods were minimal and did not have a significant impact on the final choice. The discussion emphasised the advantage of nature-like fish passes, such as block ramps, which better preserve the ecological continuity of rivers and can be more easily adapted to local hydrological conditions. The study also indicated the need for continuous monitoring of the fish passes and their optimisation to reduce problems related to sedimentation and flow blocking. The obtained results can provide a valuable basis for decision making in the planning and construction of fish passes, especially in demanding mountainous conditions, contributing to improving the effectiveness of fish migration and minimising negative impacts on the natural environment.

Future Site Suitability for Urban Waste Management in English Bazar and Old Malda Municipalities, West Bengal: A Geospatial and Machine Learning Approach

The rapid urbanization occurring globally has significantly intensified the challenges of waste management in densely populated metropolitan areas. A growing amount of waste has become a major concern for municipal authorities and local governments due to the limited availability of suitable land. Geospatial techniques, such as Geographic Information Systems (GISs) and remote sensing, combined with machine learning, play a crucial role in identifying suitable sites for urban waste management. These techniques assist planners in making well-informed decisions that strike a balance between environmental preservation and urban expansion by examining spatial data on land use, population density, and environmental concerns. Geospatial tools provide a data-driven basis for policy and urban planning, ensuring effective land use, reducing ecological hazards, and promoting sustainable urban growth for municipalities such as English Bazar and Old Malda. It can also pose serious threats to the environment, public health, and communities. Focusing on the English Bazar and Old Malda Municipalities in India, this paper examines the use of geospatial technologies to identify suitable sites for waste disposal. The research aims to address the complex processes of waste generation, collection, and disposal in urban environments. Using GIS and a Multi-Criteria Decision Analysis (MCDA) approach, the study employs the Analytic Hierarchy Process (AHP) alongside the Random Forest (RF) model and a machine learning (ML) technique to identify potential waste disposal sites within the English Bazar and Old Malda Municipalities in the Malda district. Eight key criteria were considered in the site selection process: land elevation; distances from surface water, roads, railways, and urban areas; groundwater depth; land use and land cover; and distance from sensitive and restricted areas. AHP analysis showed that 8%, 26%, and 27% of the sites were categorized as very highly suitable, moderately suitable, and unsuitable, respectively. Meanwhile, 38%, 17%, and 13% of the areas were classified as unsuitable, moderately suitable, and very highly suitable according to the RF model. The overall accuracy and Kappa coefficient indicated that the AHP method (overall capacity of 83.83% and Kappa coefficient of 0.7894) was slightly better than the RF model (overall capacity of 80.61% and Kappa coefficient of 0.7474) for site suitability analysis. This research underscores the broad relevance of geospatial technology in creating resilient and environmentally sustainable cities while offering valuable guidance on effectively allocating waste disposal sites. The findings provide crucial insights for urban planners and decision-makers, facilitating the identification of optimal locations for sustainable waste management in urban settings.

Application of Multi-criteria Decision-making Methods for Forensic Analysis of Mechanical Parts in Vehicle Accidents using q-Rung Orthopair Fuzzy Numbers

This study explores the application of a Multi-Criteria Decision-Making (MCDM) approach based on q-rung orthopair fuzzy numbers to identify the origin of mechanical parts found at vehicle accident scenes. The primary objective is to determine the most likely vehicle to which these parts belong by evaluating key criteria such as compatibility, damage level, serial number matching, and color compatibility. Q-rung orthopair fuzzy numbers offer an advanced method to handle the inherent uncertainty and vagueness associated with forensic evaluations, particularly in scenarios where data is incomplete or imprecise. The proposed methodology involves defining the criteria, assigning membership and non-membership degrees using q-rung orthopair fuzzy sets, and applying an aggregation process to effectively rank alternatives. This approach facilitates flexible decision-making by accommodating different levels of confidence and uncertainty, making it particularly suitable for forensic applications where evidence is often ambiguous. The findings demonstrate that integrating q-rung orthopair fuzzy numbers into the MCDM framework significantly enhances the accuracy and reliability of identifying vehicle components involved in accidents. The proposed methodology provides a systematic tool to support forensic investigations, aiding in the determination of liability and contributing to more robust outcomes in mechanical evidence analysis.

Knowledge graph-driven mountain railway alignment optimization integrating karst hazard assessment

Karst hazard is a considerable threat that should be considered in railway alignment design for mountainous regions with dense water systems. Nevertheless, alignment design principles in karst regions have not been systematically studied. Moreover, a quantitative karst hazard assessment model is currently lacking for automated alignment optimization. To solve the above problems, based on the analyses of karst inducing factors and hazard representation, the railway alignment design principles in karst regions are summarized through an event tree. A highly-coupled knowledge graph (called KaRAD-KG) modeling method is proposed. Then, a bi-objective alignment optimization model considering railway construction cost and karst hazard (mainly including hazard components of synclinal karst, anticlinal karst and karst depression) is constructed. To solve the optimization model, a knowledge-driven distance transform algorithm incorporating a karst hazard assessment method and a multicriteria tournament decision method is customized. Finally, the application in a real-world case indicates that the proposed method can generate an alignment which reduces construction cost by 3.39 % and karst hazard by 18.73 % compared to the best manually-designed alternative, which verifies the effectiveness of this method for assisting actual railway alignment design in a karst-dense mountainous region.

Bipolar Complex Fuzzy Rough Sets and Their Applications in Multicriteria Decision Making

Bipolar complex fuzzy set (BCFS) is a more advanced and powerful phenomenon as it consists of two-dimensional data with positive and negative impacts of an element. It can solve the data consisting of the positive and negative impacts of an element which is a bipolar fuzzy set (BFS). It also covers the two-dimensional complex data which is a complex fuzzy set (CFS). Due to these attributes, BFS and CFS are less useful in comparison with BCFS to capture vagueness, complexity, and ambiguity in the data. Furthermore, lower and upper approximations based on equivalency relations constitute another significant phenomenon known as rough set (RS). This structure is also more powerful in dealing with real-life dilemmas. Rather than comparing the RS and BCFS, we combine both phenomena to handle the complexity more powerfully to deal with such types of phenomena that are not handled by other structures. So, by combining both phenomena, we introduce a novel structure known to be bipolar complex fuzzy rough set (BCFRS) in this manuscript. After that, we define some important operations, some significant properties related to this structure, and some aggregation operators (AOs) to solve decision-making (DM) problems related to cyber security. We address a practical application of cyber security (C-S) in computing for the protection of critical data to demonstrate the usefulness of the multi-attribute DM(MADM) approach. Based on the various criteria and attributes given by the experts, we find the best and better alternative to the C-S by applying the MADM approach. We get the A4 as the best and finest alternative by using bipolar complex fuzzy rough (BCFR) weighted arithmetic averaging (BCFRWAA), BCFR ordered weighted arithmetic averaging (BCFROWAA), and BCFR ordered weighted geometric averaging (BCFROWGA) operators. And, by using BCFR weighted geometric averaging (BCFRWGA), we get the A3 as the finest alternative. Lastly, to prove the superiority, validity, and generalization of our unique established theory, we give a detailed comparative study of our established work with several prevalent theories.

Web-based Land Suitability Analysis for Horticulture Crop (Kiwi) in Champawat districts, Uttrakhand using MCDM and Geospatial Analysis

Identifying the suitability of a horticulture fruit with a defined area is an active research area, it has been attempted through different approaches, surveying and geographic information system are the two most popular techniques utilized extensively for this activity across the globe. GIS has its proven advantage over the field surveys based approach and modern GIS tools offers automation of the entire work flow for this activity. This study presents a web-based land suitability analysis for kiwi fruit cultivation in the Champawat District of Uttarakhand, using AHP, TOPSIS, and Ensemble modeling in conjunction with Multi-Criteria Decision Making (MCDM) and Geospatial Analysis. Land suitability was evaluated across various classes based on nine criteria viz. elevation, chilling hours, land use/land cover (LULC), soil texture, soil depth, temperature, rainfall, slope, and aspect. The AHP method identified 26.09 hectares as highly suitable, 14,606.19 hectares as suitable, 31,899.77 hectares as moderately suitable, 8,807.99 hectares as less suitable, and 192.23 hectares as not suitable. TOPSIS results showed 4,294.32 hectares as highly suitable, 14,193.54 hectares as suitable, 11,782.52 hectares as moderately suitable, and 2,399.21 hectares as less suitable. The ensemble method indicated 24.15 hectares as highly suitable, 8,763.51 hectares as suitable, 13,785.13 hectares as moderately suitable, 2,472.13 hectares as less suitable, and 191.94 hectares as not suitable. Integrating these findings revealed that the ensemble approach closely aligns with AHP in identifying highly suitable and less suitable areas, while TOPSIS emphasizes moderate suitability. This synthesis provides valuable insights for land use planning for kiwi cultivation. Additionally, an online spatial decision support system (SDSS) was developed using the Django framework, offering a modular and secure environment for informed decision-making.

The Development of IV-VIKOR Method Under Hesitant Fuzzy Sets in MCDM

Due to its solution correctness and ease of evaluation, the VIKOR approach has already established itself as a popular multi-criterion decision-making (MCDM) method. This strategy focuses on picking and ranking from a list of viable options in order to assist the decision-maker in determining a final course of action. Additionally, it looks for a middle ground to resolve a problem with competing demands. It produces a ranking list of compromises based on a precise measurement of distance from the ideal response. The hesitant fuzzy set (HFS) has furthermore come under increased study because of its potency and ability in capturing ambiguity and uncertainty. This paper introduces the inversion of VIKOR method (IV-VIKOR) to take uncertain fuzzy conditions into account. These novel concepts serve as the foundation for an IV-VIKOR method that we suggest, and a real-world application is given to demonstrate how well our method works when dealing with MCDM problem with hesitant fuzzy preference data.

Evaluating potential landing sites for the Artemis III mission using a multi-criteria decision making approach

The selection of a landing site within the Artemis Exploration Zone (AEZ) involves multiple factors and presents a complex problem. This study evaluates potential landing sites for the Artemis III mission using a combination of Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methodologies, specifically the TOPSIS algorithm. By integrating topographic, illumination, and mineralogy data of the Moon, we assess 1247 locations that meet the Human Landing System (HLS) requirements within 13 candidate regions and Site 004 near the lunar south pole. Criteria considered include surface visibility, HLS-astronaut line of sight, Permanently Shadowed Regions (PSRs), sunlight exposure, direct communication with Earth, geological units, and mafic mineral abundance. Site DM2 (Nobile Rim 2), particularly the point at latitude 84°12’5.61”S (−84.20156°) and longitude 60°41’59.61”E (60.69989°), is the optimal location for landing. Sensitivity analysis confirms the robustness of our approach, validating the suitability of the best location despite the MCDM method employed and variations in criteria weightings to prioritize illumination and PSRs. This research demonstrates the applicability of GIS-MCDM techniques for lunar exploration and the potential benefits they can bring to the Artemis program.

Evaluating the impact of construction delays on project duration using machine learning and multi-criteria decision analysis

Evaluating the impact of construction delays on project duration using machine learning and multi-criteria decision analysis

A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis

BackgroundDNA microarrays provide informative data for transcriptional profiling and identifying gene expression signatures to help prevent progression of latent tuberculosis infection (LTBI) to active disease. However, constructing a prognostic model for distinguishing LTBI from active tuberculosis (ATB) is very challenging due to the noisy nature of data and lack of a generally stable analysis approach.MethodsIn the present study, we proposed an accurate predictive model with the help of data fusion at the decision level. In this regard, results of filter feature selection and wrapper feature selection techniques were combined with multiple-criteria decision-making (MCDM) methods to select 10 genes from six microarray datasets that can be the most discriminative genes for diagnosing tuberculosis cases. As the main contribution of this study, the final ranking function was constructed by combining protein-protein interaction (PPI) network with an MCDM method (called Decision-making Trial and Evaluation Laboratory or DEMATEL) to improve the feature ranking approach.ResultsBy applying data fusion at the decision level on the 10 introduced genes in terms of fusion of classifiers of random forests (RF) and k-nearest neighbors (KNN) regarding Yager’s theory, the proposed algorithm reached a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. Finally, with the help of cumulative clustering, the genes involved in the diagnosis of latent and activated tuberculosis have been introduced.ConclusionsThe combination of MCDM methods and PPI networks can significantly improve the diagnosis different states of tuberculosis.Clinical trial numberNot applicable.

Multi-criteria decision-making model for sustainability functions integrated Industry 4.0 technologies within small and medium enterprises in emerging countries

PurposeIn the Industry 4.0 (I4.0) era, small and medium enterprises (SMEs) face significant pressure to make their manufacturing operations more sustainable. However, SMEs often lack the knowledge and information needed to leverage I4.0 technologies for achieving sustainability. This paper aims to develop a multi-criteria decision-making (MCDM) model to prioritize sustainability functions supported by I4.0 technologies in SMEs.Design/methodology/approachThe fuzzy-DEMATEL method was developed to classify sustainability functions based on their prominence and influence, categorizing them into cause-and-effect groups. Experts in I4.0 technologies, sustainability and SMEs from Brazil were consulted during the data collection phase. Sensitivity analysis was also performed to validate the results obtained through the DEMATEL method.FindingsThe results indicate that the most prominent and influential sustainability functions include organizing production processes, employee skill development and simplifying production planning and control. Therefore, investing in I4.0 technologies to support these functions can enable SMEs to amplify the impact of their sustainability initiatives and improve operational efficiency. The authors also observed that improving quality monitoring is the primary function within the effect group.Practical implicationsThis paper serves as a roadmap for SME managers seeking to enhance the effectiveness of their sustainability practices using I4.0 technologies, thereby maximizing impact while minimizing the complexity of implementation.Originality/valueThis study ranks key sustainability functions as actions and practices that SMEs can implement using I4.0 technologies to achieve sustainability. It also identifies the most prominent and influential functions managers should prioritize when investing in I4.0 technologies.

Performance Evaluation and Management of Indian Manufacturing Organizations Through Fuzzy Optimization Techniques

In a context of intense competition, evaluating financial performance is crucial for manufacturing sectors. As such, a precise and fitting performance review is essential. In the process of evaluation, financial performance indicators need to be carefully chosen because they show how competitive a business is. In this paper, the financial performances of the firms in the Indian manufacturing industry are evaluated using financial ratios, specifically accounting-based financial performance (AFP) measures and value-based financial performance (VFP) measures. These financial performances are assessed through multi-criteria decision-making (MCDM) techniques, specifically fuzzy multi-objective and optimization on the basis of ratio analysis (F-MOORA) and fuzzy step-wise weight assessment ratio (F-SWARA). First, the financial performance indicators' weights are determined by the F-SWARA approach, and then the firms' rankings are determined by the F-MOORA approach. By applying SWARA methodology in a fuzzy environment, the implications of the findings demonstrate that the factor named Return on Assets (ROA) contains the maximum weight and the factor named regret contains the lowest weight. By applying F-MOORA technique, it also demonstrates that company C1 is the best and company C3 is the worst.

Financial Performance Analysis of Energy Companies Using Multi-Criteria Decision-Making Techniques: An Application in the BIST Electricity, Gas and Water Sector

Energy plays a vital role in the macroeconomic development of countries. The increase in energy consumption and the increase in the number of energy companies are among the basic dynamics of country development and growth. Enhancing the financial performance of companies operating in the energy sector strengthens the competitive power of countries at both national and international levels. In this context, this study aims to determine the financial performance rankings of companies operating in the energy sector in Turkey over a 10-year period, identifying the most successful and least successful periods over the years. Using data from energy sector companies listed on Borsa Istanbul (BIST) between 2013 and 2022, after determining the research criteria, the weights of these criteria were calculated using the Entropy method. Subsequently, considering the criteria weights, the performance rankings of energy companies were determined year by year using the MAIRCA method. According to the results of the Entropy analysis, when the importance level is evaluated, the most significant criterion in 2013 was the Liquidity Ratio (LR), in 2014, 2017, 2018, and 2019 it was the Gross Profit Growth (GPG) criterion, in 2015 it was the Current Ratio (CR) criterion, in 2016 and 2022 it was the Net Profit Growth (NPG) criterion, and in 2020 it was the Net Profit Growth (NPG) criterion. Finally, in 2021, the most important criterion was determined to be EBITDA Growth (EBG). Furthermore, based on the MAIRCA analysis, the reasons behind the most successful and least successful years of the companies were explained using financial and activity reports, as well as special situation disclosure notifications obtained from the Public Disclosure Platform (PDP).