Vise Kriterijumska Optimizacija I Kompromisno Resenje Research Articles

Construction and application of economic resilience evaluation model for megacities.

Economic resilience provides a new perspective for megacities to achieve sustainable development when facing multiple shocks, and its accurate evaluation is an essential prerequisite for optimizing urban governance. There are currently no generally accepted methods for empirical evaluation or measuring economic resilience, and the present study aims to contribute to in both the research field and methodology. The present study sets dimensions and indicators based on economic resilience's theoretical and empirical research and used Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interactive Structural Modeling (ISM) methods to exclude the effect indicators and divide the indicator hierarchy, respectively. Subsequently, the present study conducts model validation using Chinese megacities as a case study. The game theory weighting method, which combines the Analytic Hierarchy Process (AHP) and Entropy methods, is used to calculate indicator weights, and the VIKOR (VIseKriterijumska Optimizacija i KOmpromisno Resenje) method is used to evaluate and compare economic resilience of megacities. The research findings indicate that the evaluation model constructed in the present study included 15 indicators (after excluding three effect indicators) divided into four levels. After merging the levels, they correspond to three dimensions: resistance, recoverability, and adaptability. In addition, using Chinese megacities as a case study, the evaluation results found that Beijing, Shanghai, and Shenzhen have high economic resilience, Tianjin and Guangzhou have moderate economic resilience, Chengdu has low economic resilience, and Chongqing has the lowest economic resilience. This result is consistent with previous studies and verifies the model's effectiveness. The present study also found that megacities with lower levels of economic resilience exhibit a more significant upward trend, as well as the highest and higher proportion of economic resilience in Chinese megacities depending on time passes, indicating that megacities' economic resilience is weakening. The evaluation result obtained in the present study is more specific, precise, and focused on depicting the distribution differences and development trends of economic resilience at the urban level.

IVPF-AHP integrated VIKOR methodology in supplier selection of three-dimensional (3D) printers

Complex geometries, fine details, and various designs that are difficult to create using traditional methods can easily be turned into a tangible object with Three-Dimensional (3D) printers. 3D printers have advantages such as providing design flexibility, obtaining prototypes in the shortest possible time, allowing for personalization, and reducing waste through the use of advanced technology. These advantages emphasize the significance of 3D printers in a sustainable production model. The widespread usage of 3D printers leads to increased efficiency and cost reduction in production. When the literature is examined, it is observed that there are limited studies on the evaluation of supplier performances for company using 3D printers. The aim of this study is to address 3D printers, which are highly significant for sustainable production, and to reveal the criteria that companies utilizing these printers need to consider for determining their suppliers. As a result of the literature review and expert interviews, a model has been developed that gathers the criteria to be considered for supplier selection, which is an important cost factor for companies involved in designing and producing 3D printers under five main and 18 sub-criteria. The importance weights of the criteria have been determined using the Interval Valued Pythagorean Fuzzy Analytic Hierarchy Process (IVPF-AHP) method, and the most suitable supplier among alternative suppliers has been selected using the Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Finally, the supplier scores have been statistically analyzed to show the validation of the results of the proposed method. According to the results, it has been concluded that for company using 3D printers, quality and technical service criteria are more important in the supplier selection. Additionally, cost of the material/equipment, product price and easy maintenance criteria also play a critical role in the supplier selection of 3D printer.

Evaluating the supply chain resilience strategies using discrete event simulation and hybrid multi-criteria decision-making (case study: natural stone industry)

ABSTRACT One of the challenges that organisations face is the risk reduction through creating resilient supply chains. There are different strategies to reach the resiliency. In this paper, we analysed five important strategies: redundancy, backup supplier, reserved capacity, reserved inventory, and increasing work-shifts. These supply chain resilience (SCR) strategies are evaluated using discrete event simulation and hybrid multi-criteria decision-making methods. For this end, Bayesian best-worst method (BWM) and Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were used for weighting the SCR evaluation criteria and ranking the SCR strategies, respectively. Six discrete event simulation models were generated and run for evaluating each strategy in order to achieve quantitative criteria. On the other hand, the ideas of experts were used for weighting qualitative criteria and also ranking the alternatives. Results showed that the increasing work-shift strategy has the first rank and the reserved capacity strategy has the lowest rank.

An integrated framework for assessing renewable-energy supply chains using multicriteria decision-making: a study on Bangladesh

Abstract The depletion in non-renewable energy sources and a fast-growing population in Bangladesh are exacerbating the already existing energy scarcity, highlighting the need for an efficient and robust renewable-energy supply chain. The primary goal of this study is to evaluate the most optimized renewable-energy supply chain based on natural resource availability and government policies of Bangladesh. In the present study, four renewable energy resources, including solar, biomass, wind and hydropower, are studied and nine subcriteria are defined under four primary criteria for each supply chain. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) are multicriteria decision-making approaches used in this study to compare and choose the best renewable-energy supply chain. The relative significance of four supply-chain criteria for primary renewable energy in this study, namely energy procurement, production, operations and maintenance costs, and social and environmental impact, is gathered via a survey. The results of this research, supported by a comprehensive sensitivity analysis, indicate that hydropower is the best renewable-energy supply chain, followed by wind as a compromise solution, biomass and solar. The study also demonstrates that no energy source can satisfy all supply-chain criteria alone; each resource is better for a specific criterion—solar is better for procurement, hydropower is significant for production and wind is remarkable for operations and social impact. Therefore, to maximize output, renewable energy sources must be integrated. From Bangladesh’s perspective, for the first time, by using TOPSIS and VIKOR together, this study offers significant insights to establish an efficient and sustainable renewable-energy supply chain for practitioners, academics and policymakers.

An integrated fuzzy multi-criteria decision-making model for determining the interdependencies among the african swine fever spread factors

African swine fever (ASF) is a disease outbreak that has substantially impacted agriculture, public health, and food security. This study tackles the lack of knowledge regarding the interactions between multiple factors causing the spread of ASF. We also present a potential for cross-fertilization between applying soft computing to nonmeasurable factors of different natures through investigating the relationships and significance of ASF spread factors in the Philippine setting using Multi-criteria Decision Making (MCDM) methodologies and fuzzy logic. We develop a list of ASF spread factors through a literature review. Fuzzy Analytic Hierarchy Process (AHP)-VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is used for the dimensionality reduction of factors. These elements are then classified as either net causes or net effects using the Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL), which also shows their interdependencies. The results show how socioeconomic characteristics, particularly those connected to biosecurity lapses, significantly influence other parameters through a causal graph. This knowledge can be used to develop effective ASF preventive and management methods as it provides a systematic and unbiased decision-making framework for policymakers, breeders, and manufacturers.

Evaluation of new scintillation crystals with MCDM methods for brain PET

Brain Positron Emission Tomography (PET) imaging has been increasingly important in recent decades for improved detection and staging of brain cancer and other brain illnesses. Many elements, including the choice of radiation detecting medium, contribute to the overall image quality used to characterize a PET system's performance. In our previous study, novel transparent optical scintillator crystals for brain PET system were simulated. Therefore, there is a need to evaluate these materials using advanced computational techniques. Hence, in this study, we evaluated and compared them using two Multi-Criteria Decision-Making (MCDM) techniques, namely Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and Visekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The crystals used in this study were strontium hafnate (SHO), gadolinium aluminum gallium garget (GAGG), gadolinium yttrium gallium aluminum garget (GYGAG), gadolinium lutetium gallium aluminum garget (GLuGAG), and lastly lutetium oxyorthosilicate (LSO) for comparison. Density, effective atomic number, energy resolution, light output, and decay time were selected as important criteria. Importance weights of each criteria were then assigned by considering the high resolution and high sensitivity detectors. With both MCDM methods, the results showed that SHO outranked the other scintillator materials followed by LSO and GLuGAG. GYGAG, and GAGG is revealed as the least favorable crystals, in agreement with the previous simulation studies. This study can be extended by including more scintillators as they become available in the future.

A MAGDM approach for evaluating the impact of artificial intelligence on education using 2-tuple linguistic q-rung orthopair fuzzy sets and Schweizer-Sklar weighted power average operator.

The impact of artificial intelligence (AI) in education can be viewed as a multi-attribute group decision-making (MAGDM) problem, in which several stakeholders evaluate the advantages and disadvantages of AI applications in educational settings according to distinct preferences and criteria. A MAGDM framework can assist in providing transparent and logical recommendations for implementing AI in education by methodically analyzing the trade-offs and conflicts among many components, including ethical, social, pedagogical, and technical concerns. A novel development in fuzzy set theory is the 2-tuple linguistic q-rung orthopair fuzzy set (2TLq-ROFS), which is not only a generalized form but also can integrate decision-makers quantitative evaluation ideas and qualitative evaluation information. The 2TLq-ROF Schweizer-Sklar weighted power average operator (2TLq-ROFSSWPA) and the 2TLq-ROF Schweizer-Sklar weighted power geometric (2TLq-ROFSSWPG) operator are two of the aggregation operators we create in this article. We also investigate some of the unique instances and features of the proposed operators. Next, a new Entropy model is built based on 2TLq-ROFS, which may exploit the preferences of the decision-makers to obtain the ideal objective weights for attributes. Next, we extend the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique to the 2TLq-ROF version, which provides decision-makers with a greater space to represent their decisions, while also accounting for the uncertainty inherent in human cognition. Finally, a case study of how artificial intelligence has impacted education is given to show the applicability and value of the established methodology. A comparative study is carried out to examine the benefits and improvements of the developed approach.

Effect of RAP fractionation and dosage on design and mechanical behaviour of cold asphalt mixes

This study examines the effect of reclaimed asphalt pavement (RAP) material dosage and incorporation method on the mechanical characteristics of Cold Asphalt Mix (CAM). The RAP materials were fractionated into fine and coarse in addition to the unfractionated method. Four levels of RAP dosage (25, 50, 75, and 100%) were considered for each category. The mix design parameters, such as total fluid and emulsion content, were evaluated using the Indirect Tensile Strength (ITS) test. Overall, 455 specimens were prepared for mix design. The Optimum Emulsion Content (OEC) is the design emulsion content determined at peak value of ITS test. ITS, Indirect Tensile Stiffness Modulus (ITSM), and moisture susceptibility tests were conducted. Incorporating RAP in CAM increased the average tensile strength predominantly by 34% in coarse mixes, 28% in unfractionated, and < 10% in fine mixes. The average ITSM of the mixture increased by 30%, 60%, and 80% for fine, coarse, and unfractionated RAP. Further, emulsion demand was reduced by 53%, 43%, and 79% for fine, coarse, and unfractionated RAP, which showed that the RAP incorporation method profoundly influenced OEC. However, regardless of fractionation, CAM mixes showed lower cracking resistance and higher moisture susceptibility with increased RAP dosage. Two different conditioning protocols were used to capture the variation in moisture susceptibility for all 13 mix combinations. Coarse and unfractionated RAP mixes significantly differed in ITS, moisture susceptibility, cracking tolerance index, and ITSM. VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach was selected to rank the outperforming combination. The overall ranking of RAP incorporation showed that the coarse RAP mixes outperformed fine and unfractionated RAP mix characteristics. The study recommends restricting RAP usage by up to 75% to ensure the required performance of CAM mixtures.

Recommendation of Machine Learning Techniques for Software Effort Estimation using Multi-Criteria Decision Making

&amp;nbsp;For the development of the software industry, Software Effort Estimation (SEE) is one of the essential tasks. Project managers can overcome budget and time overrun issues by accurately estimating a software project&amp;#39;s development effort in the software life cycle. In prior studies, a variety of machine learning methods for SEE modeling were applied. The outcomes for various performance or accuracy measures are inconclusive. Therefore, a mechanism for assessing machine learning approaches for SEE modeling in the context of several contradictory accuracy measures is desperately needed. This study addresses selecting the most appropriate machine learning technique for SEE modeling as a Multi-Criteria Decision Making (MCDM) problem. The machine learning techniques are selected through a novel approach based on MCDM. In the proposed approach, three MCDM methods- Weighted Aggregated Sum Product Assessment (WASPAS), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were applied to determine the ranking of machine learning techniques on SEE performance based on multiple conflicting accuracy measures. For validating the proposed method, an experimental study was conducted over three SEE datasets using ten machine-learning techniques and six performance measures. Based on MCDM rankings, Random Forest, Support Vector Regression, and Kstar are recommended as the most appropriate machine learning techniques for SEE modeling. The results show how effectively the suggested MCDM-based approach can be used to recommend the appropriate machine learning technique for SEE modeling while considering various competing accuracy or performance measures altogether.

A Hybrid Group Multi-Criteria Approach Based on SAW, TOPSIS, VIKOR, and COPRAS Methods for Complex IoT Selection Problems

The growth of Internet of Things (IoT) systems is driven by their potential to improve efficiency, enhance decision-making, and create new business opportunities across various domains. In this paper, the main selection problems in IoT-type systems, criteria used in multi-criteria evaluation, and multi-criteria methods used for solving IoT selection problems are identified. Then, a Hybrid Group Multi-Criteria Approach for solving selection problems in IoT-type systems is proposed. The approach contains the Best Worst Method (BWM) weighting method, multi-criteria Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Complex Proportional Assessment Method (COPRAS), and a method that combines the solutions obtained using the four considered multi-criteria methods to obtain a single solution. The SAW, TOPSIS, VIKOR, and COPRAS methods were analyzed in relation to their advantages, disadvantages, inputs, outputs, measurement scale, type of normalization, aggregation method, parameters, complexity of implementation, and interactivity. An application of the Hybrid Group Multi-Criteria Approach for IoT platform selection and a comparison between the SAW, TOPSIS, VIKOR, and COPRAS solutions and the solution of the proposed approach is realized. A Spearman correlation analysis is presented.

Using VIKOR and RSM-DA in the optimization of dry turning of 9XC steel

Dry turning is an effective method for reducing the production costs and environmental impact of machining processes. In this study, the effect of cutting speed (Vc), feed rate (fz), and depth of cut (ap) on the surface roughness (Ra) and material removal rate (MRR) of 9XC steel during dry turning was investigated. A Box-Behnken experimental design was employed to analyze the main effects and interaction effects of these cutting parameters. In this research, the combining Response Surface Methodology - Desirability Approach (RSM-DA) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method were both employed for solving multiple objective optimization problems, and their performance was compared. The results from both methods can be assessed based on their ability to identify the optimal set of parameters that simultaneously optimize surface quality and production rate, as well as their computational efficiency and ease of implementation. Both RSM-DA and VIKOR have been found effective for solving multi-objective optimization problems, such as optimizing cutting parameters in dry turning. While RSM-DA is a statistical tool that combines multiple objectives into a single function, VIKOR is a decision-making method that ranks alternatives based on multiple criteria. The choice of which method to use depends on the specific requirements of the problem and the availability of resources for implementation. The research results show that both VIKOR and RSM-DA are suitable for solving the multi-objective optimization problem of the turning process. According to the VIKOR method, the optimum cutting conditions were found to be a cutting speed Vc of 120 m/min, a depth of cut ap of 0.1 mm a feed rate fz of 0.06 mm/rev, and, which resulted in a surface roughness of 0.209 um and a material removal rate of 0.72 cm3/min. Meanwhile, RSM-DA predicts the optimal Ra and MRR values of 0.254 um and 3.36 cm3/min, respectively; corresponding to the Vc of 180m/min, fz of 0.06mm/rev and ap of 0.3mm; That means, the increasing the value of surface roughness by 21.5% will increase the MRR by 366.7%. The findings of this study can provide guidance for selecting the appropriate cutting parameters for dry turning of 9XC steel to achieve desired surface roughness Ra and material removal rate MRR in the specific case.

Risk ranking of rig up operation activities using multi-criteria decision-making methods based on fuzzy environment

The main aim of this research study is to determine the risk ranking of 29 failure modes of rig-up operations using a well-known and highly effective MCDM method i.e. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS). This method takes into account the four criteria which comprise human impact, property damage, environmental effect and company reputation as risk criteria. Three Experts in the field were invited to present the risk ranking of the relative importance of criteria and criteria performance of each risk (as failure mode) by utilizing linguistic variables which are then integrated in triangular fuzzy numbers for further analysis using the Fuzzy TOPSIS method. The evaluation concluded that FM20 (Collision between the offshore platform and boats) is the most dangerous risk with a coefficient of closeness (CCi) equal to 0.992 while the risk level of FM26 (foggy weather) was found least having a coefficient of closeness (CCi) of 0. Subsequently, sensitivity analysis of ranks concluded that FM1 (H2S inaccurate monitoring) is the most sensitive alternative with standard deviation and frequency of rank change was 5.16 and 15 respectively whereas FM20 (Collision between offshore platform and boats) is observed to be the least sensitive alternative. Fuzzy TOPSIS results were found in excellent correlations with the other reliable MCDM methods which include Fuzzy VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) and Fuzzy COPRAS (COmplex PRoportional ASsessment) yielding correlation of around 1. This study proves to be a landmark in the petroleum industry and significantly contributes to verifying safe practices, by taking appropriate actions in minimizing the risk level associated with different activities during rig-up operations.

Hybrid (Optimal) Selection Model for Phase Change Materials Used in the Cold Energy Storage of Air Conditioning Systems

The latent heat storage of phase change materials (PCMs) can be used in refrigeration and air conditioning systems. Storing cool energy during the nighttime (off-peak hours) and releasing the cool energy during the daytime (on-peak hours) to reduce the number of starts of the chiller and pumps is a practical approach for achieving energy saving and carbon reduction. Therefore, selecting PCMs is vital for improving energy efficiency and preventing future energy shortages. However, selecting PCMs is complicated by their unique characteristics and types. The purpose of this study was to establish a PCM selection model by combining the Delphi, analytic hierarchy process (AHP), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods to select the optimal PCM type applied in cold storage. A numerical example was used to verify the model’s usability, confirming that A9 is the optimal PCM for the cold storage of an air conditioning system. This three-stage PCM selection model combining the Delphi, AHP, and VIKOR approaches provides a more suitable selection model and considers the selection method of material criteria. Moreover, it can solve the problem of difficult PCM selection. Simultaneously, it considers mechanisms to incorporate a company’s primary considerations into material selection for real-world applications. These results can facilitate material evaluation and selection during system design and material qualification, helping companies achieve the goals of energy saving, carbon reduction, and sustainable management in the future.

Wastewater Treatment Technologies Selection Using Analytical Hierarchy Process and VIKOR Methods: A Case Study

Due to the ever-increasing water scarcity problem across the globe, the treatment of wastewater is an important public health and socio-economic issue. Treating wastewater through proper technology is vital to protect the ecosystem from unsafe and contaminated matter available in wastewater. Identification of suitable wastewater treatment technologies is a complex Multi-Criteria Decision Making (MCDM) problem since it includes many conflicting assessment criteria. The objective of the paper is to construct an integrated model using the Analytical Hierarchy Process (AHP) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) for evaluating wastewater treatment technologies (WWTTs). AHP is applied to calculate criteria weights, and the VIKOR method is applied to prioritize and select the best WWTTs. The proposed model is applied to selecting the best WWTT among four alternatives and seven criteria. It is found that the proposed model yields better results when compared with other MCDM solutions.

IMPROVING PERFORMANCE OF THE BANANA AGROINDUSTRY SUPPLY CHAIN USING MCDM (MULTI CRITERIA DECISION MAKING)

The problems faced by the banana agro-industry include the procurement process of raw materials from banana suppliers, which has not been able to meet the industry's needs. The selection process of banana suppliers has not been a factor considered by the industry in its strategic planning. The technology used should be able to produce quality processed banana products, but in reality, production is still below the available normal capacity. The distribution of processed banana products to consumers has not been well identified, and market access is still limited, resulting in stagnant industry development. The aim of this research is to identify the problems related to the performance of the banana agro-industry supply chain, measure the performance of the banana agro-industry supply chain, and provide and evaluate solutions that can be used to improve the performance of the banana agro-industry supply chain. The method used in this research is to measure the performance of the supply chain using weighting with the multiple criteria decision-making (MCDM) method, which consists of the Analytical Hierarchy Process (AHP), determining alternative solutions using the VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) method. The results show that the identification of problems in the banana agro-industry supply chain consists of 5 main criteria and 20 sub-criteria, as well as 7 proposed solutions. The measurement of the supply chain performance resulted in a score of 32.032 (poor), indicating that the performance of the banana agro-industry supply chain is still low. The alternative solution provided to improve the performance of the banana agro-industry supply chain is to establish collaboration and partnerships among supply chain stakeholders.

Green Supplier Selection Based on Sequential Group Three-Way Decision Making

Environmental protection and sustainable development have become the consensus of all countries in the world. Enterprises must pay attention to the impact on the environment in their operations. Therefore, the selection of green suppliers has become a crucial issue for companies. Supplier selection is a dynamic and complex multi-attribute group decision-making process. The decision results have the tripartite characteristics of “accepted”, “rejected” and “pending further investigation”, and experts need to constantly negotiate in the decision-making process to achieve consensus. In view of the above characteristics, this study constructs a sequential group three-way decision making (TWDM) method to support green supplier selection. Firstly, we review the existing literature on the evaluation criteria and selection methods of green suppliers. In this process, we construct an evaluation attribution system including the following four aspects: product formation, service level, development capability, and green level. Secondly, combining with the sequential and group characteristics of decision making, we propose a multiple-attribute sequential group TWDM method based on a multi-level granularity structure. The weight of each decision maker is determined by his influence weight and interval-valued intuitionistic fuzzy entropy weight. The attribute weight is determined by entropy weight and subjective weight. By using the VIsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method improved by grey relation analysis (GRA), we calculate conditional probabilities. Since the cumulative prospect theory can reflect the risk attitude of decision makers, we apply it to calculate the decision thresholds. Finally, we apply a case of Q automobile manufacturer to verify the effectiveness, applicability and feasibility of the method. The innovation of this study is to construct social networks at each granularity level and introduce an expert information exchange model to promote group consensus. The sequential group TWDM method provides a new reference and idea for the selection of green suppliers.

Multi-attribute optimization of drilling CFRP/Ti-6Al-4V alloy hybrid stacks using VIKOR

Drilling carbon reinforced polymer composites (carbon fiber-reinforced polymers, CFRP) and the hole quality produced are highly critical and significantly influence the performance and reliability of structures used in aircraft components. When CFRP composites are stacked together with titanium alloy, the damage caused by the machining operation is incalculable due to the burr and chip evacuation of metallic phases. In this article, efforts were made toward the identification of optimal machining conditions in drilling CFRP/titanium hybrid stacks. Experimental investigations on dry drilling of CFRP/titanium stacks were conducted with the aid of tungsten carbide drills. This research article discusses an improved compromise ranking method named VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) to determine the optimal machining parameters in drilling of CFRP/titanium stacks to produce high-quality holes. Attributes measured from experimental trials are: delamination factor of CFRP, burr height of titanium alloy, thrust force, torque, surface roughness, roundness and hole diameter of CFRP and titanium alloy. Experimental trial 13 with a combination of speed of 30 m/min, feed of 0.025 mm/rev was chosen as an ideal machining parameter in drilling CFRP/Ti-6Al-4V stacks without any trade-off. Confirmation tests convey that the results agree well with optimal parameters and the decision making in drilling such multimaterial stacks is highly consistent and judicious. In regard to the optimal parameters determined, investigations on tool life were performed and a tool life of 20 min for 0.10 mm flank wear land was observed.

Evaluating green financing mechanisms for natural resource management: Implications for achieving sustainable development goals

China has made noteworthy strides in achieving the Sustainable Development Goals (SDGs), including natural resource management. The country’s economic expansion has put more strain on its natural resources during Covid-19 pandemic, which has contributed to environmental deterioration, the extinction of species, and climate change. Consequently, green finance can create new opportunities for assisting natural resource management practices that support environmental, social, and economic sustainability. In order to realize the SDGs, this study employs a hybrid Fuzzy Multi-Criteria Decision Making (MCDM) approach based on the Analytical Hierarchy Process (AHP) and Visekriterijumska Optimizacija i Kompromisno Resenje (VIKOR) to assess and prioritize important criteria, sub-criteria, and green financing mechanisms for natural resource management in China. Therefore, this study identified six criteria, eighteen sub-criteria, and six green financing strategies for promoting sustainable development and natural resource management methods in China. According to the Fuzzy AHP findings, financial sustainability (0.192), institutional feasibility (0.176), and environmental impact (0.171) are the most crucial criteria. While, the Fuzzy VIKOR findings reveal that carbon markets, green bonds, and green investment funds are the best financial solutions for reaching sustainable development and effective resource management practices in China. This research enables policymakers, investors, and other stakeholders improve sustainable development and natural resource management in China.

A risk evaluation model of electric power cloud platform from the information perspective based on fuzzy type-2 VIKOR

With the swift progression of digital technology, the construction of electric power cloud platform is of great significance for the transformation of the electricity industry. Based on the demonstration of the State Grid Corporation of China's cloud platform, power companies should proactively encourage the establishment of cloud platforms, and the research from the perspective of information risk is imperative before finalizing any decisions related to cloud platforms. Therefore, an index system containing 15 information risk factors is established at first. Secondly, the weight of each index is determined based on GCWM (game combination weighting method). Thirdly, an MCDM (Multi-Criteria-Decision-Making) model based on the fuzzy type-2 VIKOR (from Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje) algorithm is established. Then, the validity of the model is verified through case study, which reveals that scheme 2 should become a better choice for power enterprises. Finally, sensitivity analysis, comparative analysis, and corresponding management suggestions are also put forward based on the research content. Drawing upon the foundation of the MCDM model, this paper studies the risk evaluation problem of electric power cloud platform from the information perspective, which can provide a certain reference for the development of power enterprise cloud platform in the digital technology environment, as well as bearing relevance for the implementation of associated technologies.

An improved Interval-valued intuitionistic fuzzy MCGDM for the evaluation of agricultural products live-streaming e-commerce platform

A crucial step for agricultural product merchants to achieve profitable and sustainable development in the live-streaming e-commerce age is evaluating the risk of the agricultural products live-streaming e-commerce platform. However, there isn’t much reliable research available right now on the risk evaluation of platforms. Therefore, this study suggests an improved risk evaluation method based on interval-valued intuitionistic fuzzy multi-criteria group decision-making (MCGDM). This method determines the decision-maker weight for the risk criterion according to the levels of professionalism of the decision-makers in the risk criterion and uses the VIse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rate the risk of the alternative agricultural products live-streaming e-commerce platforms. The viability and dependability of the approach described in this work are demonstrated using a case study. The strengths and weaknesses of this approach are illustrated by a comparative analysis. With the help of this paper, agricultural product merchants will be able to identify the live-streaming e-commerce platform that carries the least amount of overall risk and work toward the paper’s stated objectives of sustainable development in addition to developing and enhancing theoretical research findings in the field.