Weights Of Criteria Research Articles

Portfolio Optimization with Entropy-CRITIC-IDDWS- PROMETHEE Model in BIST Retail Trade Sector

This research aims to construct an ideal stock portfolio by using financial data from firms in the BIST Retail Trade Sector throughout the 2022-2023 timeframe. . Entropy, CRITIC, IDDWS and PROMETHEE methodologies were used in the study. The criterion weights were determined using the approaches of Entropy, CRITIC, and IDDWS. The alternatives were ranked using the PROMETHEE method. According to the Entropy method, the most effective evaluation criterion on performance is the return on equity ratio in 2022 and the asset turnover ratio in 2023, according to the CRITIC method, the financing ratio in 2022 and the current ratio in 2023, according to the IDDWS method, the financing ratio in 2022 and the asset turnover ratio in 2023.It was concluded that there were 6 companies with values between 0 and +1 in the entire 2022-2023 period, there were 6 companies on the efficient frontier. These firms are CASA, SOKM, VAKKO, BIMAS, SUWEN and TKNSA. The study has a very unique perspective in terms of both proposing a new and original integrated model (Entropy-CRITIC-IDDWS-PROMETHEE) to investors about optimal portfolio selection and testing the proposed model for the 1st time in this study and the breadth of the data set used.

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).

Hybrid Multi-Criteria Decision Making for Additive or Conventional Process Selection in the Preliminary Design Phase

Additive manufacturing (AM) has become a key topic in the manufacturing industry, challenging conventional techniques. However, AM has its limitations, and understanding its convenience despite established processes remains sometimes difficult, especially in preliminary design phases. This investigation provides a hybrid multi-criteria decision-making method (MCDM) for comparing AM and conventional processes. The MCDM method consists of the Best Worst Method (BWM) for the definition of criteria weights and the Proximity Index Value (PIV) method for the generation of the final ranking. The BWM reduces the number of pairwise comparisons required for the definition of criteria weights, whereas the PIV method minimizes the probability of rank reversal, thereby enhancing the robustness of the results. The methodology was validated through a case study, an aerospace bracket. The candidate processes for the bracket production were CNC machining, high-pressure die casting, and PBF-LB/M. The production of the bracket by AM was found to be the optimal choice for small to medium production batches. Additionally, the study emphasized the significance of material selection, process design guidelines, and production batch in the context of informed process selection, thereby enabling technical professionals without a strong AM background in pursuing conscious decisions.

Evaluation of sustainable hydrogen production technologies by fuzzy AHP analysis with bootstrapping

The concept of a hydrogen economy has received increasing attention worldwide. Hydrogen is a promising energy carrier that contributes to the development of a sustainable and reliable energy sector. Selection of the most sustainable hydrogen production technology (HPT) plays a significant role in promoting a hydrogen economy. However, it is regarded as a multi-criteria decision-making (MCDM) problem with several incompatible evaluation criteria. In this study, we develop an MCDM approach based on Fuzzy Analytic Hierarchy Process (FAHP) for prioritizing HPT alternatives, focusing on Egypt as a case study. Our contributions are twofold. First, to combat the issue of a small number of subject matter experts, a bootstrap method is employed to validate the main criteria weights in FAHP. Second, we conduct a comparative FAHP analysis from a broad perspective of the national experts from a developing country and the international experts from developed countries. Our results show that PV electrolysis is the most preferable technology option from the perspectives of both the national and international experts. Since the FAHP approach proposed is a flexible framework that can be applied to countries other than Egypt, it will support global policymakers and industry stakeholders in their decision making toward a sustainable and efficient hydrogen economy strategy.

Multi-criteria analysis and mapping of the vulnerable flood risk areas of Jigawa State, Nigeria

This study evaluates flood risk factors and maps flood-prone areas in Jigawa State, Nigeria, using geospatial techniques. By integrating various flood-causing factors—such as rainfall distribution, elevation, slope, drainage network and density, land cover and use, flow accumulation, and soil type— through a multi-parametric approach, it employs the Analytical Hierarchy Process (AHP) to perform pairwise comparisons and assign priority weights to each factor. Spatial multi-criteria analysis (MCA) then ranks and identifies potential flood locations. Using ArcGIS's weighted overlay tool, these layers are combined to produce a final flood risk map, classifying areas into high, very high, moderate, low, and very low-risk categories. Elevation and slope were found to have the greatest impact on flooding, with normalized criterion weights of 30 and 22, respectively. The Consistency Ratio (CR) of 0.06 validated the strength of the judgment, and these AHP-derived factor weights were used to create the flood risk map showing areas with varying risk levels, highlighting eight local governments in the study area. —Auyo, Hadejia, Kirika-samma, Kafin-Hausa, Ringim, Miga, Jahun, and Dutse—compose areas of very high risk, while Guri, some portions of northern Kiri-kasamma, southern Jahun, northern Auyo, Birnin-Kudu, and northwestern Ringim local governments comprise high-risk areas. Areas of moderate risk comprise a small portion of northern Kirika-samma, larger portions of Gwaram, small portions of Jahun, Kafin-Hausa, Dutse and Birnin-kudu local governments respectively and areas of low to very low risks consist of many parts of Gwaram and some tiny portions of Birnin-kudu.

AN INTEGRATED APPROACH TO IDENTIFY SUITABLE AREAS FOR THE LOCATION OF PUBLIC FACILITIES USING MULTI-CRITERIA ANALYSIS BASED ON GIS AND AHP IN THE CITY OF EL KHROUB, ALGERIA

Random and spontaneous urban growth is the result of massive natural population growth, as it is in all developing countries. As for our study air, it also results from the unloading of the mother city of Constantine to the satellite city of El-Khroub, which gave urbanization the power to take over its vast environment, An uneven distribution of public facilities in the city’s major districts, as well as traffic jams. This work attempted to determine the suitability of urban land for the location of public facilities in the city. It should be noted that land suitability assessment is a major factor in urban planning and management. Our land adequacy assessment is subject to the AHP model, calculating the weighting of various criteria including physical, socio-economic, environmental and urban. The final adequacy analysis revealed the proportion of land most suitable, estimated at 3.52% of the total area, covering mainly fallow land. The results of this study provide a detailed mapping of urban development potential, integrating both existing data and future projections.

CRITID: enhancing CRITIC with advanced independence testing for robust multi-criteria decision-making

In multi-criteria decision-making and model evaluation, determining the weight of criteria is crucial. With the rapid development of information technology and the advent of the big data era, the need for complex problem analysis and decision-making has intensified. Traditional CRiteria Importance Through Intercriteria Correlation (CRITIC) methods rely on Pearson correlation, which may not adequately address nonlinearity in some scenarios. This study aims to refine the CRITIC method to better accommodate nonlinear relationships and enhance its robustness. We have developed a novel method named CRiteria Importance Through Intercriteria Dependence (CRITID), which leverages cutting-edge independence testing methods such as distance correlation among others. This approach enhances the assessment of intercriteria relationships. Upon application across diverse data distributions, the CRITID method has demonstrated enhanced rationality and robustness relative to the traditional CRITIC method. These improvements significantly benefit multi-criteria decision-making and model evaluation, providing a more accurate and dependable framework for analyzing complex datasets.

Determination of the weights of technical criteria influencing the performance of petrol-powered and battery powered chainsaws by means of the Entropy Method

Determination of the weights of technical criteria influencing the performance of petrol-powered and battery powered chainsaws by means of the Entropy Method

Predicting China’s Energy Consumption and CO2 Emissions by Employing a Novel Grey Model

The accurate prediction of China’s energy consumption and CO2 emissions is important for the formulation of energy and environmental policies and achieving carbon neutrality. This paper proposes a new weighted error evaluation criterion that emphasizes the importance of new data, thereby enabling more accurate capture of the variation characteristics of new data and reflecting the principle of new information priority. By optimizing the development coefficient, grey action, and parameters in the time response formula of the traditional GM(1,1), a novel optimized model, OGMW(1,1), is constructed. The accuracy of the new model is verified by three cases from the literature. The future trends of primary energy, oil, and coal consumption and CO2 emissions in China are predicted over the next five years. The conclusions are as follows: First, the new weighted error evaluation criteria are effective and reasonable and can indicate whether a grey model can reliably use the most recent information for modeling. Second, based on the new error evaluation criteria, the development coefficient, ash action, and parameter C in the time response function can be optimized. The results show that the optimization method is reasonable. Third, compared with the traditional models GM, GMO, and ARIMA, the OGMW(1,1) provides better simulation and prediction accuracy, and new information can be prioritized more effectively. Fourth, the forecasting results indicate that China will increase its consumption of primary energy, oil, and coal, as well as its CO2 emissions. Notably, the growth rates of primary energy and oil consumption are high at approximately 22.7% and 25%, coal consumption will increase slightly, and CO2 emissions will increase by approximately 11%.

Wind Power Plant Location Selection with Fuzzy Logic and Multi-Criteria Decision-Making Methods

This study aims to examine the effectiveness of the fuzzy multi-criteria decision-making methods, especially Fuzzy Weight by Envelope and Slope (F-WENSLO) and Fuzzy Ranking Alternatives with Weights of Criterion (F-RAWEC) for evaluating the potential locations for wind power plants. The study's objective is to provide a solid framework for deciding which locations are most suitable for wind energy projects, taking into account various criteria and expert opinion. The study includes the evaluation of five potential places including Gürün, Kangal, Divriği, Ulaş and Zara in Sivas province of Turkey. The evaluation criteria include wind speed and direction, altitude, land use, environmental impacts, infrastructure proximity, social acceptance, economic costs, security and risk factors, climatic conditions and legal and permit requirements. Scores from experts from various fields and weights of criteria were determined. The analysis revealed that Ulaş and Kangal got the highest point for the wind power plant installation. As Ulaş gets the highest point due to favorable wind conditions, favorable altitude and advantageous land use; Kangal stood out as a strong candidate because of its acceptable wind speed, positive social acceptance and low economic costs. The study highlights the importance of integrating multiple criteria and expert assessments in the decision-making process. The findings suggest that fuzzy multi-criteria decision-making methods can be effectively supportive of wind power plant site selection. The study provides valuable information for project managers and policy makers, emphasizing the importance of criteria such as security in the choice of location, legal requirements and social acceptance. Future research may expand these findings to examine the integration of additional criteria, alternative locations, and other multi-criteria decision-making techniques.

Novel Multi-Criteria Decision Analysis Based on Performance Indicators for Urban Energy System Planning

Urban energy systems planning presents significant challenges, requiring the integration of multiple objectives such as economic feasibility, technical reliability, and environmental sustainability. Although previous studies have focused on optimizing renewable energy systems, many lack comprehensive decision frameworks that address the complex trade-offs between these objectives in urban settings. Addressing these challenges, this study introduces a novel Multi-Criteria Decision Analysis (MCDA) framework tailored for the evaluation and prioritization of energy scenarios in urban contexts, with a specific application to the city of Bozen-Bolzano. The proposed framework integrates various performance indicators to provide a comprehensive assessment tool, enabling urban planners to make informed decisions that balance different strategic priorities. At the core of this framework is the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is employed to systematically rank energy scenarios based on their proximity to an ideal solution. This method allows for a clear, quantifiable comparison of diverse energy strategies, facilitating the identification of scenarios that best align with the city’s overall objectives. The flexibility of the MCDA framework, particularly through the adjustable criteria weights in TOPSIS, allows it to accommodate the shifting priorities of urban planners, whether they emphasize economic, environmental, or technical outcomes. The study’s findings underscore the importance of a holistic approach to energy planning, where trade-offs are inevitable but can be managed effectively through a structured decision-making process. Finally, the study addresses key gaps in the literature by providing a flexible and adaptable tool that can be replicated in different urban contexts to support the transition toward 100% renewable energy systems.

Site Selection Decision-Making for Offshore Wind-to-Hydrogen Production Bases Based on the Two-Dimensional Linguistic Cloud Model

Offshore wind-to-hydrogen production is an effective means of solving the problems of large-scale grid-connected consumption and high power transmission costs of offshore wind power. Site selection is a core component in planning offshore wind-to-hydrogen facilities, involving careful consideration of multiple factors, and is a classic multi-criteria decision-making problem. Therefore, this study proposes a multi-criteria decision-making method based on the two-dimensional linguistic cloud model to optimize site selection for offshore wind-to-hydrogen bases. Firstly, the alternative schemes are evaluated using two-dimensional linguistic information, and a new model for transforming two-dimensional linguistic information into a normal cloud is constructed. Then, the cloud area overlap degree is defined to calculate the interaction factor between decision-makers, and a multi-objective programming model based on maximum deviation-minimum correlation is established. Following this, the Pareto solution of criteria weights is solved using the non-dominated sorting genetic algorithm II, and the alternatives are sorted and selected through the cloud-weighted average operator. Finally, an index system was constructed in terms of resource conditions, planning conditions, external conditions, and other dimensions, and a case study was conducted using the location of offshore wind-to-hydrogen production bases in Shanghai. The method proposed in this study demonstrates strong robustness and can provide a basis for these multi-criteria decision-making problems with solid qualitative characteristics.

Assessment of Land Suitability in Perennial Crops using FAHP and GIS in Central Vietnam

Background: Perennial crops including rubber and Thanh Tra grapefruit are highly effective land use that has occupied a large area (nearly 11,000 ha) in Thua Thien Hue province, Central Vietnam. Analyzing land suitability in agricultural production is one of the important tools for determining the effective use of land resources and sustainable development. This study aims to evaluate the land suitability of perennial crops in Phong Dien district, Thua Thien Hue province, Central Vietnam. Methods: The study was conducted in different communes in Phong Dien district, Thua Thien Hue province, Central Vietnam from 2022 to 2023 by collecting secondary and primary data together with soil samples from 45 profiles for analysis and finally application of the fuzzy multi-criteria analysis process (FAHP) tool and geographic information system method (GIS) to evaluate the land suitability. Result: The results indicated that economic criteria played the most important role with weights from 0.068 to 0.343, followed by the social criteria (weights from 0.014 to 0.147) and the environmental criteria (weights from 0.011 to 0.051). With land suitability evaluation, the very high suitability area of rubber and Thanh Tra grapefruit (S1) was 3,144.14 ha and 3,742.29 ha respectively. The FAHP and GIS integrated method is a useful method to assess land suitability for land use of perennial crops based on appropriate assessment of FAO land use requirements.

Green dielectric exploration: spectrographic analysis and technical feasibility of vegetable oils in EDM via PIV, TOPSIS, and MOORA evaluation methods

Achieving a sustainable dielectric medium for Electrical Discharge Machining (EDM) has now drawn the attention of researchers and industries alike. Increased awareness among the Government and NGOs paved the way for the manufacturing industries to move towards a green environment and sustainability in the production process. To search for sustainable dielectric, 5 different types of oils viz., Jatropha, coconut, groundnut, sunflower, and EDM 4 oil (conventional oil) are experimented in this paper and revealed its suitability of potential replacement for the conventional dielectric. The properties such as viscosity, density, flash point, electrical conductivity, and BDV (Break down Voltage) are tested as per the standards. Since the number of output response is more than, Multi Attribute Decision Making (MADM) algorithms namely PIV, TOPSIS, and MOORA are used for the ranking and optimization. Entropy weight method is adopted to endow criteria weight to each of the MADM techniques. Jatropha oil has been ascertained to be rank 1 followed by groundnut, coconut, sunflower, and EDM 4 oil. Subsequently, the Gas Chromatography & Mass Spectrometry (GC–MS) analysis has revealed the quantitative constituents present in Jatropha as well as remaining oils. Furthermore GC–MS analysis reports that the retention time of Jatropha (43.042 min) is relatively longer than other oils except coconut oil, to release Ricinoleic acids. Groundnut and EDM 4 oils took relatively less retention time to release the alkanes. Machining performance was also conducted with the obtained dielectric and compared with conventional type oil (EDM 4 oil).

Decision Analysis Algorithm Using Frank Aggregation in the SWARA Framework with p,qRung Orthopair Fuzzy Information

The present study introduces an innovative approach to multi-criteria decision making (MCDM) aimed at handling decision analysis involving p,qrung orthopair fuzzy (p,qROF) data, where the criteria weights are completely unknown. To achieve this objective, we formulate generalized operational rules referred to as Frank operational rules, tailored for p,qROF numbers (p,qROFNs) utilizing the Frank t-norm and t-conorm. With these newly devised operations as a foundation, we create a variety of p,qROF aggregation operators (AOs) to effectively aggregate p,qROF information. Furthermore, we examine specific instances of these operators and rigorously establish their desirable properties, including idempotency, monotonicity, boundedness, and symmetry. Subsequently, we adapt the SWARA technique to the realm of p,qROF fuzzy data and this adapted technique becomes instrumental in determining criteria weights within the proposed MCDM framework centered around proposed AOs. We furnish a descriptive example to exemplify the practicality of the developed approach. Lastly, the effectiveness and soundness of our approach are underscored through both parameter analysis and a comparative evaluation.

Development of an Indicator System for Evaluating Risks in Trekking Tourism

In today’s fast-paced and and high-stress society, trekking tourism is a leisure activity for stress relief and closer nature engagement. Given the adventurous nature inherent in trekking tourism, risk assessment is deemed both crucial and imperative. This study aims to develop a risk assessment framework for trekking tourism based on the Delphi method and Analytic Hierarchy Process (AHP). Initially, we conducted a literature review and expert interviews grounded in the Systems Theory of Accident Causation and the Human Factors Theory to establish the risk assessment paradigm specific to trekking tourism. Subsequently, we ascertained specific weights for each evaluation criterion and delineated explicit data sources for each operational indicator. The findings indicate that the formulated risk assessment system for trekking tourism is both scientifically grounded and practical, furnishing stakeholders in the tourism industry, organizational teams, and management authorities with an efficacious tool for evaluating the safety conditions of trekking tourism activities.

A large-scale supplier evaluation approach for circular economy in the presence of circular criteria interactions and weight consistency

Circular economy (CE) is an economic model in which we achieve sustainable development through resource recovery, reuse, and remanufacturing. Large-scale supplier evaluation is crucial for ensuring the sustainability of the CE supply chain and efficient utilization of resources. Especially, criterion interaction and consistency of criterion weights are two key factors of large suppliers’ evaluation in CE. However, current research often overlooks these key factors, this study proposes a large-scale supplier evaluation approach in CE to address these issues. The novel aspects of the proposed approach come with considering the interaction between circular and non-circular criteria, as well as eliminating the impact of large-scale complex information on the consistency of criterion weights. Firstly, large-scale hesitant evaluation information is fused by evidence reasoning, and interaction rules between circular and non-circular criteria are established. Then, the interaction weights of the criteria are obtained based on the interaction of circular criteria, and the comprehensive criteria weights are determined by an improved best-worst method. Next, the decision makers’ weights are generated by the utility value of the circular criterion. Finally, the results of numerical example and analysis show that 86% of the supplier ranking results are stable, and the consistency of the proposed approach is 0.9757, indicating that the proposed approach exhibits high validity and reliability. It emphasizes the significance of the supplier’s circular ability in CE and reduces the interference of large-scale complex information on the consistency of evaluation results.

Biweight midcorrelation-based CRITIC method: a case study for electrification of BIPV in Iran

Criteria weights are essential in various multi-criteria decision-making (MCDM) approaches. The amount of weight indicates the criterion's importance, and the performance of MCDM techniques depends on the correct estimation of criteria weights and criteria amount in the decision matrix. The distance correlation-based CRiteria Importance Through Inter-criteria Correlation (CRITIC) technique is a modified version of the CRITIC method. Since Pearson's correlation is sensitive to noisy and outlier data, this paper proposes using the biweight midcorrelation as a robust alternative to Pearson's correlation. The weights obtained from the proposed biweight midcorrelation method make using the full range of MCDM techniques possible. This study examined the performance of the proposed method in comparison with eight criteria weighting techniques on the electric supply for a Building-Integrated PhotoVoltaic (BIPV) problem. Results from three different tests (i.e. distance correlation, Spearman rank-order correlation, and symmetric mean absolute percentage error (sMAPE)) indicated that the proposed method could produce more valid criteria weights and ranks than other methods. The value of the sMAPE criterion for the proposed method is 0.1096. Sensitivity analysis also revealed that the proposed method provides more robust criteria weights and ranks with a larger decision matrix.

Smart retrofitting for office buildings: Comparison of decision-making criteria between developing and developed regions

Given the constantly evolving nature of modern infrastructure, retrofitting existing buildings with state-of-the-art smart technologies presents a pivotal opportunity, enabling promising advancements in sustainability, efficiency and adaptability. Focusing on Hong Kong and Sri Lanka, which are developed and developing regions respectively, this study investigates the decision-making criteria for selecting smart systems as retrofits for office buildings. Using the analytic network process (ANP), 24 expert interviews were conducted in Hong Kong and Sri Lanka to determine the importance weights of the decision-making criteria. The findings show varied importance weights for each criterion between the two regions, with Hong Kong emphasizing upfront financial considerations, specifically 'investment cost' while Sri Lanka emphasizing long-term operational sustainability, specifically 'recurring operation, maintenance and repair cost.' Despite these differences, there are similarities in the importance of core criteria such as 'reliability of retrofit option' and 'ability to increase returns.' The lack of focus on user-centric issues in Hong Kong and implementation of retrofit projects in Sri Lanka underscore the need for decision-makers to prioritize these aspects in future projects. The principal contribution of this study is in offering insights from a thorough analysis of the importance weights of the essential criteria in making decisions regarding smart retrofitting (SR). It is also beneficial to various stakeholders, including building owners and facility managers. The cross-region study approach, enabling the investigation on both developed and developing regions, further serves as a foundation for future research aiming to explore and analyze the influence of regional difference in decision-making for SR.

Fermatean trapezoidal fuzziness average aggregation scheme for selection of infant clothing by group decision-making

The textile industry has gradually increased its use of various chemicals and the quantity of residues produced. Newborn baby apparel should be made of safe materials since it often comes into touch with them. Infant cloth selection is determined via group decision-making in Fermatean trapezoidal fuzzy number (FTFN). This study provides a new score and accuracy function and weighted, ordered, and hybrid averaging aggregation operators on FTFN. The properties of the proposed averaging aggregation operators on FTFN are presented. An algorithm for the multi-attribute group decision-making (MAGDM) approach will be proposed by applying the proposed averaging aggregation operators. The case studies concentrate on choosing newborn clothing with reduced chemical content within the textile sector. The presentation of the sensitivity analysis of the criterion weights is conducted to guarantee the durability and stability of the framework that has been implemented. Furthermore, a comprehensive analysis compares the novel framework with previous models, highlighting its superior performance.