Multiple Criteria Decision Making Techniques Research Articles

An analysis of social acceptance of energy in Iran: case study of fossil, nuclear, solar and wind energy

Purpose The purpose of this paper is to investigate the social acceptance of renewable and non-renewable energies in Iran using the social acceptance pyramid. Design/methodology/approach Today, social acceptance is considered a very important phenomenon in the development, implementation and achievement of energy policy goals. Low acceptance will make it difficult to achieve energy development goals; therefore, social acceptance must be taken into account when making policy. Firstly, the model criteria, using data obtained from questionnaires, are weighted by the Shannon entropy method and, finally, four sources of fossil, nuclear, wind and solar energy were ranked by means of VIKOR, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Findings The results show that, in Iran, the social acceptance criterion and trust sub-criterion are the most important criteria for energy acceptance. The results of the ranking of options based on multiple-criteria decision-making (MCDM) techniques show that, given Iran's specific energy requirements, social acceptance of fossil energy is higher than wind, solar and nuclear, and wind, solar and nuclear energy come later in the rankings. Originality/value This research contributes to the literature in two ways: Firstly, social acceptance is considered a very important phenomenon in the development, implementation and achievement of energy policy goals; thus social acceptance must be taken into account when making policy. The results of the ranking of options based on MCDM techniques show that, given Iran's specific energy requirements, social acceptance of fossil energy is higher than wind, solar and nuclear, and wind, solar and nuclear energy come later in the rankings. Also, the social acceptance criterion and trust sub-criterion are the most important criteria for energy acceptance in Iran.

A TOPSIS-based thermal performance optimization of DHCTHX using MWCNT nanofluids

Double-helically coiled tube heat exchangers are employed in many different heat transfer applications because of their better heat transfer capabilities and compact construction. The double helically coiled tube heat exchanger (DHCTHX) improves turbulence and maximizes heat transfer rate compared to straight tubes. A full factorial, orthogonal array of trials is used in this study (L27) to test various volume concentrations of nanofluids. The pressure drop, de, LMTD0C, re, and F have all been attempted to be optimized using the order preference similarity to the ideal solution technique. In this case, the mass flow rate and volume concentration are the variables for the input process, while the outputs are the heat transfer rate, pressure drop, de, LMTD0C, and F. The experimental results show that a nanofluids volume concentration is 0.6% and its mass flow is 120 with a range of 1460 Dean Numbers, which is the optimal configuration for maximizing the overall heat transmission and minimizing the pressure drop. The multiple-criteria decision-making technique was used in this study to identify the ideal cooling fluids in a DHCTHX with a significant pressure drop. The multi-walled carbon nanotube nanofluids may be used instead of conventional coolants in conjunction with the DHCTHX. According to this strategy, A3B1 is the ideal value, which aligns with the experiments’ findings.

Enhancing efficiency in photo chemical machining: a multivariate decision-making approach

Non-Traditional Machining (NTM) outperforms traditional processes by offering superior geometric and dimensional accuracy, along with a better surface finish. Photo Chemical Machining (PCM) represents one such NTM process, using chemical etching for material removal. PCM finds substantial application in the creation of microchannels in pharmaceutical, chemical and energy industries. Several input parameters—such as etchant concentration, etching time and etchant temperature—profoundly influence the machining’s quality and efficiency. Therefore, the optimization of these parameters is crucial. This study presents a comparative analysis of five Multiple Criteria Decision Making (MCDM) techniques—Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Multi-Objective Optimization on the basis of Ratio Analysis (MOORA), Additive Ratio Assessment (ARAS), Weighted aggregated sum product assessment method (WASPAS) and Multi-Attributive Border Approximation Area Comparison Method (MABAC)—for the optimization of the PCM process. Key performance metrics considered are Material Removal Rate (MRR), Surface Roughness (SR), Undercut (Uc) and etch factor (EF). The weights of these criteria were calculated using the Criterion-Induced Aggregation Technique (CRITIC) and was compared with other popular methods like MEREC, Entropy and equal weights. MRR and EF are seen as beneficial criteria, while SR and Uc are perceived as cost criteria. Optimum process parameters were identified as 850 g/L etchant concentration, 40 min etching time and 70°C etchant temperature. Two of the three employed MCDM techniques agreed on these optimal parameters, reinforcing the findings. Furthermore, a strong correlation was observed amongst the employed MCDM techniques, further validating the results.

Evaluation of the relevance of LED lamp selection based on manufacturer-declared parameters

The purpose of this study was to evaluate the relevancy of LED lamp parameters to ordinary customers as well as to find out which parameters are more or less significant for the purchasers. It was found that the majority of customers have some difficulties understanding the luminous parameters of LED lamps but are aware of the efficiency and advantages of these artificial light sources. In order to propose the selection guide for LED lamps, the MCDM (Multiple-Criteria Decision-Making) techniques were applied. The proposed algorithm consists of eight steps and covers all LED lamp parameters declared by the manufacturer.

The Development of a Hybrid Model for Dam Site Selection Using a Fuzzy Hypersoft Set and a Plithogenic Multipolar Fuzzy Hypersoft Set

Inrecent years, there has been a notable increase in utilising multiple criteria decision-making (MCDM) methods in practical problem solving. The advancement of enhanced decision models with greater capabilities, coupled with technologies like geographic information systems (GIS) and artificial intelligence (AI), has fueled the application of MCDM techniques across various domains. To address the scarcity of irrigation water resources in Bortala, Northwest China, the selection of a dam site has been approached using a hybrid model integrating a multipolar Fuzzy set and a plithogenic Fuzzy hypersoft set along with a GIS. This study considered criteria such as a geological layer, slope, soil type, and land cover. Four potential and reasonably suitable dam locations were identified using a dam construction suitability map developed for Bortala. Ultimately, we showcased the benefits of the innovative method, emphasizing an open, transparent, and science-based approach to selecting optimal dam sites through local studies and group discussions. The results highlight the effectiveness of the hybrid approach involving a fuzzy hypersoft set and plithogenic multipolar fuzzy hypersoft set in addressing the challenges of dam site selection.

A Comprehensive Review of Multiple Criteria Decision-Making (MCDM) Methods: Advancements, Applications, and Future Directions

This research paper presents a comprehensive review of Multiple Criteria Decision-Making (MCDM) methods, encompassing their advancements, applications, and future directions. The study begins with an introduction emphasizing the significance of MCDM in complex decision-making scenarios. Through a systematic literature review, recent developments in MCDM techniques are examined, including multi-objective methods, fuzzy-based approaches, data-driven models, and hybrid methodologies. The strengths and limitations of each method are critically analyzed. Furthermore, the paper investigates the diverse applications of MCDM in domains such as business, engineering, environment, healthcare, and public policy, highlighting the practical implications through real-world case studies. The study then identifies emerging trends and challenges in MCDM research, discussing the integration of MCDM with emerging technologies, enhancing robustness and adaptability, addressing uncertainty, and identifying unexplored domains for potential application. This comprehensive review serves as a valuable resource for decision-makers and researchers, providing insights into the advancements, applications, and future directions of MCDM methods.

Evaluation of third-party logistics service providers for car manufacturing firms using a novel integrated grey LOPCOW-PSI-MACONT model

Automotive businesses often delegate logistical tasks to third-party logistics (3PLs) service providers to acquire a competitive edge in the dynamic market. Nevertheless, selecting the most suitable third-party logistics (3PL) partner is a multifaceted undertaking that needs careful evaluation of several criteria and alternatives. This research aims to introduce an integrated grey Multiple Criteria Decision Making (MCDM) framework for automotive businesses to deal with the multidimensional 3PL selection decision problem. This framework incorporates an enhanced Preference Selection Index (PSI), Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), and Mixed Aggregation by Comprehensive Normalization Technique (MACONT). The LOPCOW-G and grey PSI (PSI-G) methods extract the criterion weights, whereas the MACONT-G method ranks the alternatives. The suggested framework's practicality is shown by conducting a case study about evaluating and selecting a third-party logistics (3PLs) provider. The findings indicate that the parameters of significant importance are “skilled workforce (0.0977),” “financial strength (0.0901),” and “IT-IS competence (0.0839).” Furthermore, TPL4 has been recognized as the most optimum option with a value of 0.4797. The MACONT-G model is as well compared against other grey MCDM techniques to assess the validity of the proposed model. The Pearson correlation coefficient between MACONT-G and the other models based on grey sets is 0.958, suggesting a significant and positive link. Furthermore, it is worth noting that a sensitivity analysis has been conducted to validate the accuracy and reliability of the created framework. In conclusion, this study has identified managerial and policy implications that might assist policymakers and executives in effectively evaluating 3PL providers.

MCDM technique using single-valued neutrosophic trigonometric weighted aggregation operators

Motivated based on the trigonometric t-norm and t-conorm, the aims of this article are to present the trigonometric t-norm and t-conorm operational laws of SvNNs and then to propose the SvNN trigonometric weighted average and geometric aggregation operators for the modelling of a multiple criteria decision making (MCDM) technique in an inconsistent and indeterminate circumstance. To realize the aims, this paper first proposes the trigonometric t-norm and t-conorm operational laws of SvNNs, which contain the hybrid operations of the tangent and arctangent functions and the cotangent and inverse cotangent functions, and presents the SvNN trigonometric weighted average and geometric operators and their properties. Next, a MCDM technique is proposed in view of the presented two aggregation operators in the circumstance of SvNNs. In the end, an actual case of the choice issue of slope treatment schemes is provided to indicate the practicability and effectivity of the proposed MCDM technique.

Prioritization and taxonomy of factors related to environmental tax reform (ETR) using fuzzy AHP approach

PurposeEnvironmental taxes have been in place for many years to reduce environmental damage and pay more attention to the environment. However, some of the adverse socio-economic impacts that may result from such taxes and the many challenges facing developing countries have necessitated policy reform. Therefore, identifying and prioritizing the factors related to environmental tax reform (ETR) is necessary to help governments and environmental protection agencies (EPAs) focus on this prioritizing to develop and improve this process. Awareness of the benefits of ETR encourages governments to use this policy to reduce adverse environmental impacts and contribute to economic growth.Design/methodology/approachThe primary purpose of this work is to prioritize and taxonomy the factors related to ETR using the Fuzzy Analytic Hierarchy Process (FAHP) approach. In the first stage, 25 factors were extracted from the available literature. These factors were divided into five categories for more accessible review. In the second stage, the FAHP as a Multiple-Criteria Decision-Making (MCDM) Technique was used to prioritize and develop the taxonomy of identified factors and the categories of these factors.FindingsThe results show that reducing carbon emissions (DF4) is the essential prioritization factor that governments and environmental organizations can achieve if the ETR is implemented. Following that, reducing greenhouse gas emissions (DF1), double benefit (EcF7) and increase sustainability reports (EnF4) can be achieved by implementing ETR.Research limitations/implicationsThis study is geographically limited to Iran. In terms of the study population, this study is limited to 25 academic, tax and public policy experts. Moreover, in this study, FAHP is the only approach used. For further research, the results of this study can be compared with that of other multi-criteria techniques like FAHP, fuzzy TOPSIS or BWM.Practical implicationsRatings of factors related to ETR can guide and help governments identify important factors that affect environmental tax reform, which can, in addition to controlling ecological pollution will, increase the economic benefits of governments.Originality/valueThis study is the first to identify factors related to environmental tax reform and to develop an MCDM technique for prioritizing these factors and finding important ones.

Multiple-criteria decision making, feature selection, and deep learning: A golden triangle for heart disease identification

Cardiovascular diseases (CVD) are among the deadliest illnesses that suffer many people worldwide. Nevertheless, an on-time diagnosis of heart disease can play a significant role in healthcare; since it can reduce the chances of death and save much money in case of necessary treatment. This article develops an efficient and accurate system that uses artificial neural networks (ANN), feature selection (FS) methods, and multiple-criteria decision-making (MCDM) techniques to diagnose heart disease. Appropriate features are selected by utilizing five feature selection methods. Then, three artificial neural networks for CVD prediction were implemented, and their performances were checked according to eight solid criteria. In addition, Particle Swarm Optimizer (PSO) has been utilized to select the optimal hyperparameters to reach high accuracy. A novel integrated weighting method using MEREC and BWM is developed to consider experts’ statements and data evaluation in the criteria weighting operation to choose the best feature selection-artificial neural network method for heart disease detection. Then, an extended version of ELECTRE III is introduced to assess fifteen alternatives, which has proven to be more accurate than other ELECTRE methods in this research. Results validation has been done by utilizing eight MCDM techniques, the Pearson correlation coefficient test, and criteria weights sensitivity analysis by implementing ten scenarios to assess our methodology’s robustness. Finally, the outcomes indicate that LASSO-CNN, AdaBoost-CNN, and AdaBoost-MLP are the top 3 approaches with the accuracy of 99.51%, 98.54%, and 98.54%, respectively. To the best of our knowledge, these are the highest accuracy rates obtained in the literature so far.

A Credibility and Strategic Behavior Approach in Hesitant Multiple Criteria Decision-Making With Application to Sustainable Transportation

Multiple criteria decision-making (MCDM) methods do not account for the potentially strategic evaluations of experts. Once the ranking is delivered, decision makers (DMs) select the first alternative without questioning the credibility of the evaluations received from the experts. We formalize the selection problem of a DM who must choose from a set of alternatives according to both their characteristics and the credibility of the reports received. That is, we transform an MCDM setting into a game-theoretical scenario. We build our analysis on a recent extension of hesitant fuzzy numbers incorporated within the formal structure of technique for order of preference by similarity to ideal solution. We define the restrictions that must be imposed regarding the credibility of the evaluations and the capacity of experts to form coalitions and manipulate rankings based on their subjective preferences. This feature constitutes a considerable drawback in real-life scenarios, mainly when dealing with environmental and sustainable strategic problems. In this regard, sustainable transportation problems incorporate both technical variables and subjective assessments whose values can be strategically reported by experts. We extend a real-life study case accounting for the evaluations of several experts to demonstrate the importance of strategic incentives for the rankings obtained when implementing MCDM techniques. We numerically illustrate the interactions between the experts’ reporting strategies and the formal tools available for the DMs to counteract potential manipulations of the final ranking.

Multi-jet fusion for additive manufacturing of radiotherapy immobilization devices: Effects of color, thickness, and orientation on surface dose and tensile strength.

Immobilization devices are used to obtain reproducible patient setup during radiotherapy treatment, improving accuracy, and reducing damage to surrounding healthy tissue. Additive manufacturing is emerging as a viable method for manufacturing and personalizing such devices. The goal of this study was to investigate the dosimetric and mechanical properties of a recent additive technology called multi‐jet fusion (MJF) for radiotherapy applications, including the ability for this process to produce full color parts. Skin dose testing included 50 samples with dimensions 100 mm × 100 mm with five different thicknesses (1 mm, 2 mm, 3 mm, 4 mm, and 5 mm) and grouped into colored (cyan, magenta, yellow, and black (CMYK) additives) and non‐colored (white) samples. Results using a 6 MV beam found that surface dose readings were predominantly independent of the colored additives. However, for an 18 MV beam, the additives affected the surface dose, with black recording significantly lower surface dose readings compare to other colors. The accompanying tensile testing of 175 samples designed to ASTM D638 type I standards found that the black agent resulted in the lowest ultimate tensile strength (UTS) for each thickness of 1–5 mm. It was also found that the print orientation had influence on the skin dose and mechanical properties of the samples. When all data were combined and analyzed using a multiple‐criteria decision‐making technique, magenta was found to offer the best balance between high UTS and low surface dose across different thicknesses and orientations, making it an optimal choice for immobilization devices. This is the first study to consider the use of color MJF for radiotherapy immobilization devices, and suggests that color additives can affect both dosimetry and mechanical performance. This is important as industrial additive technologies like MJF become increasingly adopted in the health and medical sectors.

A systemic review for measuring circular economy with multi-criteria methods.

Determining the circularity of a system through a criteria and indicators approach has been the focus of research in many branches of science. In this way, this work aims to review multiple criteria decision-making (MCDM) methods employed for measuring circular economy (CE) indicators and CE aspects at different levels (micro, meso, and macro). For this purpose, a systematic literature review (SLR) was conducted in ISI Web of Science and Scopus databases. Further, the results summarized 22 MCDM techniques, 27 CE aspects, and 36 CE indicators The SLR provides a concept map highlighting the relation of the level of circularity implementation (subdivided into micro-, meso-, and macro-levels) and MCDM techniques and demonstrates the interaction between CE aspects and indicators. The review provides an outline as to how MCDM techniques can solve problems related to CE aspects and marks the relevance in the use of CE indicators to support circularity aspects and to construct criteria in the methodologies applied.

Evaluating the operation performance of the Serbian transport Freight system by using multiple criteria decision-making technique

Freight transport is a composite dynamic system that forms the unity of the infrastructure, vehicles, and personnel and which has its purpose or aim. Evaluating operation performance for freight transport systems is important for the government, companies, and users. This paper will use one of the most famous Multiple Criteria Decision-Making (MCDM) techniques to assess the operational performance of freight transport systems. At beginning, the authors will create the evaluation indicator system based on official data with four basic indicators and two sub-indicators for freight transport. These operational data/indicators represent the input of the chosen MCDM method. After that, the Analytic Hierarchy Process (AHP) method will use to calculate the indicator and sub-indicators weight and also will use to calculate the universal evaluate values and rankings of each transport mode performance. The Serbian freight transport system for the 2021st year will be selected as a case study for testing the AHP method within the MCDM approach; the associated suggestions to freight transport will also be given.

Complex q-Rung Orthopair Fuzzy Aczel–Alsina Aggregation Operators and Its Application to Multiple Criteria Decision-Making With Unknown Weight Information

In decision making problems, complex q-rung orthopair fuzzy set is regarded as a more practical tool than complex intuitionistic fuzzy set and q-rung orthopair fuzzy set. This paper proposes several aggregation operators based on the Aczel-Alsina t-norm and t-conorm for aggregating complex q-rung orthopair fuzzy data. The suggested operators are then used to establish a multiple criteria decision-making (MCDM) method. The Aczel-Alsina operations t-norm and t-conorm can have the advantage of good flexibility with the operational parameter. In this regard, we expand the notions of the Aczel-Alsina t-norm and t-conorm to the complex q-rung orthopair fuzzy environment and provide certain aggregation operators in this study. Furthermore, we show the compatible features of the suggested operators. To overcome the defects of the existing entropy measures, a novel complex q-rung orthopair fuzzy entropy approach is put forward for acquiring the unknown criteria weights objectively. Following that, we describe an MCDM technique with unknown criterion weights in a complex q-rung orthopair fuzzy environment grounded on the originated operators. Then, to demonstrate the model’s flexibility and validity, we analyze and solve a problem concerning with the selection of the sector that had the most impact on the Pakistan Stock Exchange. Subsequently, we demonstrate how the parameter’s inclusion in our proposed model influences decision-making outcomes. At last, the generated outcomes are compared to the past approaches to demonstrate our suggested technique’s superiority.

Neutrosophic Multi-Criteria Method for Selecting Optimum Market

The Indonesian rolling stock maker's target market and business strategy are analyzed here—the study's planning, data, and technique. To achieve sustainable growth across the board in the power generating industry, we concentrate on understanding the critical variables driving sustainable development in a market context unlike before. To identify the most vital aspects to consider while dealing with ambiguous rules, a methodology using Multiple-Criteria Decision-Making (MCDM) was presented. To address ambiguity and bring the problem-solving process closer to reality, we created a unique method that integrates MCDM techniques. Analytical Hierarchy Process AHP was employed in this investigation. To zero in on our ideal clientele, we turned to the AHP technique. This strategy considers quantitative aspects such as market characteristics and degree of competition when making decisions. The results indicate that rolling stock manufacturers have good reason to invest in expanding their share of these markets. However, despite the many prospects available in this field, the rolling stock producer faces a significant obstacle: the inability to adequately fund the pursuit of a more considerable portion of the market

A Review on Weight Process Method and its Classification

Weight Process Method (WPM) is a popular number Criterion Results Analysis (MCDA) / Multiple Criteria Decision Making (MCDM) method. This is the sum of the weights Similar to the model (WSM). The main difference is, In the main mathematical function there is now multiplication instead of addition. MCDM is multifaceted, inconsistent, standard and / or quantitative Consensus on integrating alternative performance into criteria Is a technique for creating a solution that requires feedback. To calculate the weighted product, we will multiply the value of each attribute in every column row-wise. The value with the highest weighted product is given the higher rank. Like all MCDA / MCDM methods, many Described in terms of decision making criteria The result is a finite set of alternatives. For each end The substitute is also multiplied by several ratios and compared with the others, One for each end criterion. Weighted shading mode () like density mode Colors that represent density in each pixel Draw markers using, but optional With weighting integration. To provide the final weighing result How the coordinates weigh in each pixel You can configure which are connected. Multiple criteria for decision making (MCDM) size and Complex decision making (DM) involving quality factors Is considered a tool. In recent years, many MCDM Techniques and approaches optimal feasibility Recommended to select options. In statistics, a weighted median of a sample is the 50% weighted percentile. … Like the median, it is useful as an estimator of central tendency, robust against outliers

Socioeconomic Influence on Cardiac Mortality in the South Asian Region: New Perspectives from Grey Modeling and G-TOPSIS.

Background Measuring the potential socioeconomic factors of cardiac mortality is fundamental to identifying treatments, setting priorities, and effectively allocating resources to minimize disease burden. The study sought to present a methodology that explores the connections between urbanization, population growth, human development index (HDI), access to energy, unemployment, and cardiovascular disease (CVD) mortality within the South Asian Association for Regional Cooperation (SAARC) nations to mitigate the cardiac disease burden. Methods This investigation uses multiple-criteria decision-making methodologies to analyze data between 2001 and 2017 commencing with a mathematical grey incidence analysis (GIA) methodology to estimate weights and rank nations based on CVD mortality. Then, utilizing the conservative min-max model approach, we sought to determine which country contributes the most to CVD mortality among all South Asian nations. The grey preference by similarity to ideal solution (G-TOPSIS) method is adopted for further optimization by prioritizing the selected factors that have the greatest influence on CVD mortality. Results The estimated statistic highlights that, among SAARC nations, Pakistan has a significant proportion of the disease burden attributable to cardiac events. In addition, HDI showed a significant contribution in the reduction of CVD mortality, whereas unemployment showed a significant contribution in the rise of CVD mortality among all selected variables. Conclusions This investigation may facilitate researchers with a multiple-criteria decision-making roadmap to help them enhance the quality of their studies and their understanding of how to use multiple-criteria decision-making techniques to evaluate and prioritize the influencing factors of disease mortality in healthcare research. Further, the study outcomes provide additional practical knowledge for appropriate policy solutions.

Parametric Optimization of Electric Discharge Machining of Metal Matrix Composites Using Analytic Hierarchy Process.

The present study reports on the method used to obtain the reliable outcomes for different responses in electric discharge machining (EDM) of metal matrix composites (MMCs). The analytic hierarchy process (AHP), a multiple criteria decision-making technique, was used to achieve the target outcomes. The process parameters were varied to evaluate their effect on the material erosion rate (MER), surface roughness (SR), and residual stresses (σ) following Taguchi’s experimental design. The process parameters, such as the electrode material (Cu, Gr, Cu-Gr), current, pulse duration, and dielectric medium, were selected for the analysis. The residual stresses induced due to the spark pulse temperature gradient between the electrode were of primary concern during machining. The optimum process parameters that affected the responses were selected using AHP to figure out the most suitable conditions for the machining of MMCs.

A Hybrid Approach Based on Fuzzy TOPSIS-AHP for Ranking and Classifying MOOC Key Acceptance Factors

This investigation is done during COVID-19 to identify, rank, and classify MOOC (massive open online course) key acceptance factors (KAFs) from an Indian perspective. A systematic literature review identifies 11 KAFs of MOOC. One more novel factor named ‘contingent instructor' is proposed by the authors considering pandemic and new normal post-COVID-19. The paper implements two popular fuzzy MCDM (multiple-criteria decision-making) techniques, namely fuzzy TOPSIS and fuzzy AHP, on 12 KAFs. The fuzzy TOPSIS approach is used to rank factors. Affordability, performance expectancy and digital didactics are found as the top three KAFs. Fuzzy AHP classified KAFs into three groups, namely high, moderate, and low influential. Examination of the literature indicates that this study is among the first attempt to prioritize and classify MOOC KAFs using fuzzy TOPSIS and fuzzy AHP approach. The results offer managerial guidance to stakeholders for effective management of MOOC, resulting in higher acceptance rate. Likewise, this investigation will upgrade the comprehension of MOOC KAFs among academicians.