Multiple Criteria Decision Making Methods Research Articles

Water Scarcity Zonation Mapping using MCDM Method in District, NAMCHI, South Sikkim using QGIS

Abstract: A major worldwide issue, water scarcity has especially serious repercussion like India that have an abundance of water. There are only so many fresh water sources available to meet the growing demand for water brought on by urbanisation, industrialisation, and population expansion, Since the limited water supplies cannot satisfy the population’s needs, a state of water scarcity has resulted. The present research is conducted in CCCT Chisopani, Sikkim, India. Water scarcity is a major issue in the area we selected, the multiple criteria decision making (MCDM) approach will be used in this case study to solve the ranking water scarcity problem, and the next upcoming major problem for water scarcity is the growing population in the study area. Along with the households the area consists of 2 study institutes due to which the population may increase in the coming years effecting the water management. The first step was to conduct a survey in and around the study area by preparing questionnaire including total population of household, highest qualification in the house, storage capacity, tenants, local source, rain water harvesting, hours water received, water fetched manually or not? Type of roofs, distance to perennial source. After collection, the data was evaluated and standardised to guarantee comparability across numerous criteria. Further the study was continued with water scarcity index calculation making it easier to prepare the zonation map for the study area. The MCDM method exhibits how the method can be used against the water scarcity problem. The main focus of the study was to create a scarcity map for the study zone in QGIS using RS&GIS method.

The preparation and selection process for a new form stable phase change material (FSPCM) intended for use as thermal storage in applications at intermediate temperatures

There is a greater focus on solar energy since the food processing business requires a lot of thermal energy to remove moisture from agricultural products. This study thoroughly explains how to choose Phase Change Materials (PCMs) within a specific temperature range. The best PCM is selected from the Multiple Criteria Decision Making (MCDM) procedures based on a few characteristics. Based on the results of the MCDM method, the eutectic of paraffin and palmitic acid is chosen. PCM is bound, and FSPCM is formed using clay. The method of preparation of Clay-based FSPCM is described in detail. The amount of clay materials and different clay percentage combinations are chosen from the leakage experiment. Kaolin has the least amount of leakage out of all the clay materials tested. Kaolin-based FSPCM Exhibits improvement in thermal conductivity compared to Pure PCM.

Z-number based fuzzy MCDM models for analyzing non-traditional security threats to finance supply chains: A case study from Vietnam

Non-traditional security (NTS) threats have a vast and profound impact on many aspects of economic, political, social, and many other areas, especially supply chain finance (SCF), particularly in countries like Vietnam, which potentially affects the economic efficiency of businesses' supply chain financial, thereby affecting the general economy of the country and the world. In order to prevent and minimize the negative impacts caused by NTS threats to SCF, this study was conducted to identify NTS threats affecting SCF in Vietnam, at the same time calculate the weight of the impact level and find out the cause and effect relationship between them. Solution strategies are also proposed and ranked, thereby serving as a reference basis for relevant parties to choose appropriate response solutions. Due to the multi-criteria nature of NTS threats, the multiple criteria decision-making (MCDM) method is used in combination with the Z-number concept and Fuzzy set theory to approach the problem of certainty and increase the accuracy of study. The NTS threats are first identified through a literature review and then validated for suitability using the DELPHI technique (DELPHI). Suitable threats will be determined by relationship, weighted by Decision Making Trial And Evaluation Laboratory (DEMATEL) method. Proposed strategies are ranked using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The results indicate that there are 19 NTS factors affecting SCF in Vietnam, and the global economic downturn, pandemic and health crisis, financial crisis and cybersecurity risk are the four root cause factors with the most decisive influence. Businesses and concerns need to prioritize addressing these four threats because they not only have a strong impact but also entail many other threats. The two strategies considered to be the most effective are a sustainable practice and a risk-hedging strategy. Businesses, governments, and stakeholders also should pay attention to the macroeconomic environment, technology, and environment and build sustainable businesses, regularly monitoring economic fluctuations and creating plans to prevent risks.

AI based fuzzy MCDM models: Comparison and evaluation of dissimilar outcomes, an application to enhance pilot recruitment process

AbstractPilot recruitment is critical as they pose a multifaceted challenge for civilian and military organizations due to the complex traits impacting their missions and performance. In this study, a novel set of criteria and sub‐criteria were determined to compare twelve candidate pilots. Numerically immeasurable, imprecise, and non‐linear continuous fuzzy linguistic traits (variables) were studied which make the work unique and challenging due to individual preferences and disagreements between decision‐makers (DMs). The outcomes of three distinct fuzzy multiple criteria decision‐making (MCDM) approaches; fuzzy TOPSIS, fuzzy VIKOR, and fuzzy PROMETHEE were evaluated with trapezoidal fuzzy numbers (TFNs) to sort the positions of candidate pilots. Moreover, a unique defuzzification ranking method was employed to adjust the results of fuzzy MCDM methods for the synthesis and evaluation of outcomes of the pilot selection problem. All these efforts make the paper original and outstanding. Our findings and analysis suggested that fuzzy TOPSIS and PROMETHEE methods' outcomes showed maximum close similarity for ranking positions. However, substantial distinctions were noted when comparing these outcomes with the fuzzy VIKOR approach. Yet, the mission of predicting and revealing the best candidates is related to several traits, their weights, and the methods selected. Therefore, since vague information and ambiguous preferences match fuzzy superiority, a comprehensive and unbiased evaluation was achieved, ensuring the integrity of the decision‐making process. The results can be employed to enhance the safety and efficiency of airline operations and ensure that the most qualified and competent pilots are selected for the job.

Evaluation of management practices in rice–wheat cropping system using multicriteria decision-making methods in conservation agriculture

In this study, we employed two multiple criteria decision-making (MCDM) methods, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and the Analytic Hierarchic Process (AHP), to determine the best management choice for the cultivation of wheat with a regime of conservation agriculture (CA) practices. By combining alternative tillage approaches, such as reduced tillage and zero tillage, with the quantity of crop residues and fertilizer application, we were able to develop the regime of CA practices. The performance of the regimes compared to the conventional ones was then evaluated using conflicting parameters relating to energy use, economics, agronomy, plant protection, and soil science. TOPSIS assigned a grade to each alternative based on how close it was to the ideal solution and how far away it was from the negative ideal solution. However, employing AHP, we determined the weights of each of the main and sub-parameters used for this study using pairwise comparison. With TOPSIS, we found ZERO1 (0% residue + 100% NPK) followed by ZERO4 (50%residue + 100% NPK), and ZERO2 (100% residue + 50% NPK) were the best performing tillage-based alternatives. To best optimize the performance of wheat crops under various CA regimes, TOPSIS assisted the decision-makers in distinguishing the effects of the parameters on the outcome and identifying the potential for maneuvering the weak links. The outcomes of this investigation could be used to improve management techniques for wheat production with CA practices for upscaling among the farmers.

Development of an Integrated Model for Open-Pit-Mine Discontinuous Haulage System Optimization

The selection of the optimal equipment for discontinuous haulage systems is one of the most important decisions that need to be made when an open-pit mine is designed. There are a number of influencing factors, including natural (geological and environmental), technical, economic, and social. Some of them can be expressed numerically, in certain units of measure, while others are descriptive and can be stated by linguistic variables depending on the circumstances of the project. These factors are characterized by a high level of uncertainty, associated with both exploration and mining operations. The experience, knowledge, and expert judgment of engineers and specialists are of key importance for the management of mining processes, consistent with the issues stemming from the dynamic expansion of open-pit mines in space over time. This paper proposes an integrated model that translates all the criteria that affect the selection of the optimal solution into linguistic variables. By employing the multiple-criteria decision-making method and combining it with fuzzy logic, we developed an algorithm that addresses all the above-mentioned uncertainties inherent in various mining processes where the experience of experts forms the basis. The fuzzy analytic hierarchy process is used in order to deal with trending decision problems, such as mining equipment and management system selection. The entire algorithm was applied to a real case study—the Ugljevik East 1 open-pit mine.

Ranking Performance of MARCOS Method for Location Selection Problem in the Presence of Conflicting Criteria

The selection of the best location for a facility is generally a complex process especially when considering multiple criteria in the selection process. The costs associated with purchasing the land and related constructions makes the location selection problem a long-term investment decision. Hence, the problem should be analysed carefully with a reliable approach to avoid the consequences that follows awkward decisions. Many multiple criteria decision-making (MCDM) methods are developed recently which made the selection of a suitable MCDM method has become a confusing decision. In this study, measurement alternatives and ranking according to compromise solution (MARCOS) method is used to solve a location selection problem with conflicting criteria. The ranking produced by MARCOS method is analysed by a comparison with other common MCDM methods and a sensitivity analysis. The aim of the comparison to show that the method agreed the choice of the best alternative while the sensitivity analysis is employed to check the robustness and efficiency of this method. The sensitivity analysis includes the sensitivity of weight test and rank reversal (RR) test. The comparison of ranking with different methods showed that MARCOS method has strong ranking correlation with other methods. Moreover, the sensitivity analysis showed the reliability and robustness of MARCOS method as it produced high consistency of ranking through the tests. Thus, this study validates the applicability of MARCOS method to handle the presence of conflicting criteria in location selection problems.

Extreme analysis of typhoons disaster in mainland China with insurance management

Due to climate change, typhoons, especially extreme typhoons, are becoming more intense and causing ascending financial losses. A majority of previous studies on typhoon economic losses over a period of time considered all types of typhoon rather than the extreme typhoons. This study focuses on the risk management of extreme typhoons by establishing the compensation mechanism and a typhoon-specific insurance product. The annual maximum losses of typhoons is first modelled by generalized extreme value distribution (GEV) under the Extreme Value Theory (EVT). The prediction of unexpected economic losses is then obtained via VaR and CVaR for the compensation mechanism among individual, insurance company and government. To analyse the typhoon vulnerability of 11 Chinese coastal provinces (or municipalities), the Multiple-Criteria Decision Making (MCDM) method, combining Analytic Hierarchy Process (AHP) based Grey Rational Analysis (GRA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), is applied for evaluating the typhoon vulnerability of these regions for 2022. The nationwide best estimates for typhoon reserves on the basis of insurance compensation mechanism is therefore calculated and will be allocated to these 11 provinces according to the vulnerability ranking obtained via MCDM method. The findings indicate that the top three provinces (Guangdong, Fujian and Zhejiang) in typhoon vulnerability rankings are also with the highest losses and frequency in practice, while Hebei has the highest insurance premium.

Storm surge risk assessment and sensitivity analysis based on multiple criteria decision-making methods: a case study of Huizhou City

Coastal areas face escalating storm surge disasters due to rising sea levels and urban growth, posing greater risks to lives and property. Comprehensive storm surge risk assessment and sensitivity analysis in coastal areas are crucial for effective disaster prevention and mitigation. This research focuses on Huizhou, China, conducting a comparative study of storm surge risk assessment and sensitivity analysis based on an integrated approach, which includes the storm surge inundation numerical model (FVCOM-SWAN), Geographic Information System (GIS) and Remote Sensing (RS) techniques, and Multiple Criteria Decision-Making (MCDM) methods. Ten flood-related risk indicators are selected from the hazard, exposure and vulnerability, the weights of which are evaluated through a comprehensive comparison among Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), Entropy Weight (EW), AHP-EW, and FAHP-EW methods. High-precision risk level maps are generated subsequently utilizing GIS and RS techniques. Sensitivities of the indicators are analyzed using One-At-A-Time (OAT) and Fourier Amplitude Sensitivity Test (FAST) methods. The proposed storm surge risk assessment framework, the MCDM comparative study and the sensitivity analysis can offer insights for better understanding and management of storm surge risks, and contribute to the standardization and application of storm surge risk assessment.

Software component selection methods and techniques: a systematic review

<p>Software component selection is critical in software engineering due to its vital role in reducing software development cost and time. This study analyzes software component selection research studies on methodologies, criteria, and multi-criteria decision-making (MCDM) techniques. The key study findings are: first, comprehensive standardized criteria for software component selection are lacking, with ambiguous terminology used in research. Second, current ad hoc selection processes need streamlining to reduce time, cost, and effort. Thus, an integrated approach is required to aid decision-makers. The review suggests developing automated tools or decision support systems combining multiple criteria decision methods to improve selection accuracy and efficiency. Standardized criteria catalogs can also assist software developers in the evaluation. The findings highlight that despite extensive academic research, component selection in practice remains sub-optimal. By informing future research and tool development, this review can benefit practitioners to systematically select the most appropriate software components meeting software requirements.</p>

Multi-criteria model for selection of optical linear terminals based on FUZZY TOPSIS method

Optical networks are an integral part of modern telecommunication systems. Huge traffic and ever-increasing requirements for data transmission capacity and quality encourage the wider use of modern optical technologies in telecommunications. The rational choice of equipment for the design of optical networks is an urgent task at present. The subject of this study is the process of selecting optical line terminals, which is associated with the evaluation of possible options by a set of indicators. The OLT (optical line terminal) in PON technology implements the function of organizing subscriber lines and is also a node equipment - an L4 switch that combines the functions of routing (IP), traffic fragmentation (VLAN), switching (MAC), quality of service (QoS), and some necessary network service functions. The optimal structuring of PON access networks demands a judicious selection of software and hardware, guided by their tactical and technical characteristics, in view of the substantial information load they entail. One of the effective approaches to solving such problems is the use of MCDM (Multiple Criteria Decision Making) methods. Purpose: to construct a TOPSIS model of the optimal choice of optical linear terminals in the conditions of unclear information for different cases of aggregation of evaluations of decision makers. Task: to formalize the process of selecting optical linear terminals; develop a multi-criteria mathematical model for the effective selection of optical line terminals. Methods used in the study: The fuzzy TOPSIS method, four methods of aggregating the opinions of decision makers. The following results were obtained. Alternatives and criteria for their evaluation are defined. On the basis of interviews with decision makers, evaluations of the degree of importance of the criteria and alternatives to the criteria were determined. Linguistic changes were used to describe decision-makers’ evaluations, which were interpreted as triangular fuzzy numbers. During this study, four methods of aggregating the evaluations of decision makers were used. A fuzzy TOPSIS model for selecting an optical line terminal is constructed. The ranking of the selected optical line terminals is obtained and the best alternative is determined. The results of modeling with different methods of aggregating assessments of decision makers are compared. Conclusions. The use of the Fuzzy TOPSIS method for optimal selection of optical linear terminals is proposed. The influence of the methods of aggregation of evaluations of decision-makers is analyzed.

A total distance ranking approach to fuzzy AHP-based MCDM method for selecting sustainable manufacturing facility location

Selecting a sustainable facility location is a crucial strategy for manufacturing companies to achieve long-term success in today’s competitive environment. Various quantitative and qualitative criteria with different importance in a multiple level structure must be considered and aggregated to assist the company in decision-making. How to determine these criteria weights and select the sustainable manufacturing facility locations have become research questions. To resolve this problem, this paper proposes a total distance ranking approach to fuzzy analytic hierarchical process (AHP)-based multiple criteria decision-making (MCDM) method. Currently, the membership functions of fuzzy weighted ratings in the MCDM with a multilevel structure cannot be developed. A ranking method is needed to defuzzify those fuzzy numbers used for fuzzy AHP and qualitative criteria in the MCDM for better executing decision-making procedure. The total distance ranking method related to the centroid on x-axis, centroid on y-axis and the distances of centroids point to the two tangent lines of a fuzzy number are considered in the proposed ranking approach. Formulas of the proposed approach will be presented, and some properties will be investigated to derive formulas for trapezoidal and triangular fuzzy numbers. A comparison with relevant ranking methods will be made to show the advantages of the proposed ranking approach. The proposed ranking approach is then applied to defuzzify the fuzzy numbers used in fuzzy AHP and linguistic values under qualitative criteria to obtain the criteria weights under multi-level structure and crisp values under qualitative criteria, respectively. The final scores of alternatives can be obtained by aggregating crisp criteria values and their corresponding weights by simple additive weighting method to obtain the ranking result. A numerical example will be conducted to show the effectiveness of the proposed model. Finally, a comparison with Best-Worst method (BWM) will be presented to show the persuasiveness of the proposed method.

Determining a framework for institutional support of instructors engaged in distance education programmes using the multiple-criteria decision-making method

Determining a framework for institutional support of instructors engaged in distance education programmes using the multiple-criteria decision-making method

A simulation study on the probabilities of rank reversal, tie making, and tie breaking for multiple criteria decision making methods

Multiple criteria decision making (MCDM) methods can be affected by preference reversal, meaning that the order of two alternatives is reversed after adding or deleting another alternative. Here, we focus on methods that produce rankings with ties (i.e., weak orders). In this context, one usually talks about rank reversal. Existing rank reversal probability simulation experiments are subject to improvement on the following points: (1) the small number of MCDM methods included, (2) the unclear relation between the rank reversal probability and the rank of the deleted alternative, and (3) the lack of consideration of ties. In this paper, considering both strict preferences and ties, we distinguish two new phenomena: tie breaking, i.e., the shift from a tie to a strict preference, and tie making, i.e., the shift from a strict preference to a tie. To investigate the probabilities of rank reversal, tie making, and tie breaking, a simulation study involving thirty versions of twelve MCDM methods and six simulation factors is set up. Results demonstrate that for MCDM methods using pairwise comparison data, deleting an alternative ranked first or last leads to smaller probabilities than deleting an alternative in the middle, while the opposite holds for the methods using evaluation data under criteria. Four findings and three suggestions are given to help decision makers select MCDM methods to use.

Interpretable decision-making model with uncertain weights for sustainable digital economy

In recent years, sustainable digital economy development goals have received increasing attention because of the serious influence environment with the development of the rapid economy. Meanwhile, Multiple Criteria Decision-Making (MCDM) methods are efficient ways to measure sustainable developments, which can controls environmental pollution and protects sustainability improvement. However, assessment results are unconvincing by using the common MCDM methods because various areas that caused pollution conditions are inconsistent. Therefore, the purpose of this paper is to design an interpretable MCDM method with uncertain weights for various areas evaluation. In particular, MCDM can be used to evaluate environmental sustainability development efficiently, and deep neural networks can supply credible weights to MCDM. Interpretability is used to understand key factors in the decision-making process. Referring to the national policy and urgent areas from central areas in China, six areas are conducted to assess the sustainable digital economy problem, which could be interpreted as demonstrating the effectiveness and feasibility of the proposed approach. Besides, the experimental results demonstrate that the sustainable developmental level of Hubei is the best in the latest year.

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.

Renewable Energy Transition Task Solution for the Oil Countries Using Scenario-Driven Fuzzy Multiple-Criteria Decision-Making Models: The Case of Azerbaijan

The renewable energy transition of oil- and gas-producing countries has specific peculiarities due to the ambivalent position of these countries in the global energy market, both as producers and consumers of energy resources. This task becomes even more challenging when the share of oil and gas in the country’s GDP is very high. These circumstances pose serious challenges for long-term energy policy development and require compromising decisions to better align the existing and newly created energy policies of the country. The scale, scope, and pace of changes in the transition process must be well balanced, considering the increasing pressure of economic and environmental factors. The objective of this paper is to develop models that allow the selection of the most appropriate scenario for renewable energy transition in an oil- and gas-producing country. The distinguishing feature of the proposed model is that alternatives in the decision matrix are presented as scenarios, composed of a set of energy resources and the level of their use. Linguistic descriptions of the alternative scenarios are formalized in the form of fuzzy statements. For the problem solution, four different Multiple-Criteria Decision-Making (MCDM) methods were used: the fuzzy simple additive weighting (F-SAW) method, the distance-based fuzzy TOPSIS method (Technique of Order Preference Similarity to the Ideal Solution), the ratio-analysis-based fuzzy MOORA method (Multi-Objective Optimization Model Based on the Ratio Analysis), and the fuzzy multi-criteria optimization and compromise solution method VIKOR (Serbian: VIekriterijumsko Kompromisno Rangiranje). This approach is illustrated using the example of the energy sector of Azerbaijan. The recommended solution for the country involves increasing natural gas (NG) moderately, maintaining hydro, and increasing solar notably and wind moderately.

A decision support system for supplier quality evaluation based on MCDM-aggregation and machine learning

Evaluating suppliers’ quality performance is one of critical tasks of quality management since it is directly related to quality assurance, improvement and development, especially for manufacturing companies. However, the supplier quality evaluation remains challenging due to the involvement of a large amount of qualitative and quantitative data, multi-dimensional attributes, as well as a large number of items and suppliers, etc. In addition, while various multiple criteria decision making methods (MCDMs) exist and can be used for supplier quality evaluation, individual choice and application of the methods often result in inconsistent or conflicting evaluation results. To overcome the challenges, in this study, we develop a decision support system for supplier quality evaluation to deal with the practical complexity and to take advantage of the availability of the big industrial data. The system is presented as a conceptual framework including three modules: the decision matrix and criteria initialization module, the MCDM method selection and implementation module, and the aggregation module. The sequential conduction of each module formulates and solves the multiple supplier and item quality evaluation problem. The implementation of the system can be applied to dynamically monitor and evaluate supplier and item quality performance. Moreover, as the core intelligent part of the decision support system, in the aggregation module, we propose an aggregated MCDM model based on the machine learning concepts. It aims at providing more reliable evaluations across multiple suppliers and items. The aggregation model also contributes to the MCDM literature as an independent method which is not restricted to the purpose of supplier quality evaluation. The methods shed lights on how to construct and solve MCDM problems in the big-data era. Finally, the application of the decision support system is illustrated by a case of a large automotive company. The case shows that our decision support system provide more robust and reliable evaluation results than the traditional individual multiple criteria decision making method.

The Optimal Sustainable Insulation Material by Using MCDM Analysis

Increasing fuel costs is the new norm, and an increase in energy consumption leads to many troubles, such as a growing of harmful environmental emissions and rise in energy consumption bills. Hence, it’s desirable and recommended to estimate the value of energy consumed for buildings in the first design state in order to reduce the impact of these issues, and obtain a sustainable building using thermal insulation for the buildings (walls, roofs, floors...).
 The purpose of this experimental analysis is to determine the best insulating material among 12 selected materials, as the properties of these materials were analysed based on several criteria.
 The study focuses on using multiple analysis methods in order to achieve the best results and to eliminate potential inconsistencies of data with available scientific methods. The evaluation is conducted via implementing three multiple criteria decision-making methods (MCDM)the full Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) multiplication, rating the choices and selection the solution nearest to ideal, VIekriterijumsko KOmpromisno Rangiranje (VIKOR) and the method of ordering options by analogy with the optimal method (TOPSIS). In addition to the significance of the criteria, find the weights of the criterion in the analysis by using the correlation between criteria (CRITIC). Results based on pre-established methods showed consistent results, with the highest and worst performing materials identified in buildings. Extruded polystyrene foam (XPS) is ranked the best among other materials. The number of articles related to MCDM isn’t that high. Therefore, this study will contribute to filling the gap.