Fuzzy Complex Proportional Assessment Research Articles

Bloodmobile location selection for resilient blood supply chain: a novel spherical fuzzy AHP-integrated spherical fuzzy COPRAS methodology

PurposeBlood availability is critical for saving lives in various healthcare services. Ensuring blood availability can only be achieved through efficient management of the blood supply chain (BSC). A key component of the BSC is bloodmobiles, which are responsible for a significant portion of blood donation collections. The most crucial factor affecting the efficacy of bloodmobiles is their location selection. Therefore, detailed decision analyses are essential for the location selection of bloodmobiles. This study proposes a comprehensive approach to bloodmobile location selection for resilient BSCs.Design/methodology/approachThis study provides a novel integration of the spherical fuzzy analytical hierarchy process (SF-AHP) and spherical fuzzy complex proportional assessment (SF-COPRAS) methodologies. In this framework, the criteria are weighted using SF-AHP. The alternatives are then evaluated using SF-COPRAS, employing criteria weights obtained from SF-AHP without defuzzification.FindingsThe results show that supply conditions and resilience are the most important criteria for a bloodmobile location selection. Additionally, the validation analyses confirm the stability of the solution.Practical implicationsThis study presents several managerial implications that can aid mid-level managers in the BSC during the decision-making process for bloodmobile location selection. The critical factors revealed, along with their importance in choosing bloodmobile locations, serve as a comprehensive guide. Additionally, the framework proposed in this study offers decision-makers (DMs) an effective method for ranking potential bloodmobile locations.Originality/valueThis study presents the first application of multi-criteria decision-making (MCDM) for bloodmobile location selection. In this manner, several aspects of bloodmobile location selection are considered for the first time in the existing literature. Furthermore, from the methodological aspect, this study provides a novel SF-AHP-integrated SF-COPRAS methodology.

Eco-performance evaluation of product designs – an integrated fuzzy complex proportional assessment with life cycle assessment

ABSTRACT The growing public awareness has driven enterprises to develop environmentally friendly designs to fulfill customer satisfaction. Some eco-design tools have been introduced to designers to support greener product design and development qualitatively. However, most require qualitative judgement during the evaluation process. Although life cycle assessment (LCA) helps quantify a product system’s environmental loads, it requires much time and resources for data collection. Design selections are not preferred during the early product development stage, especially when the product development schedule is rather tight nowadays. This paper proposes a practical approach integrating a fast-track LCA, fuzzy-set, with COmplex PRoportional ASsessment (COPRAS). This paper proposes an approach by incorporating the fuzzy-set, COPRAS, and LCA for decision-makers to evaluate design alternatives at the early product development stage. The originality of the paper is to provide an efficient approach to evaluating various design alternatives by quantifying their corresponding environmental burdens using LCA and fuzzy-set. Three distinct features can be found in the proposed generic approach. This study brings two contributions to the related field. For the area related to environmental impact assessment, this study illustrates the applicability of LCA in conjunction with COPRAS and fuzzy-set for the design alternative selection. Decision-makers can apply the proposed method that in-depth knowledge of environmental impact assessment is not essential, and the qualitative input required is minimal compared to a comprehensive LCA study.

An integrated model for rice supplier selection strategies and a comparative analysis of fuzzy multicriteria decision-making approaches based on the fuzzy entropy weight method for evaluating rice suppliers.

Rice, being a staple food in many countries, necessitates the identification of reliable suppliers to ensure a steady supply. Consequently, it is vital to establish trustworthy vendors for various types of this essential grain who can meet stringent product quality standards. This study aims to identify, analyze, rank, and select primary rice suppliers. The study emphasizes the importance of selecting and managing suitable providers to meet customer demands, proposes a ranking model for rice suppliers, and introduces developed fuzzy MCDM techniques. It proposes an integrated model for selecting rice suppliers, considering factors related to the processes before, during, and after selecting providers within a defined framework. The outcomes shows that rice supplier selection strategy can efficiently identify reliable rice suppliers, improve buyer value, reduce procurement risk, enhance efficiency, and establish strong supply chain relationships in complex decision-making processes. To assess suppliers, the study introduces two advanced integrated approaches and compares them. The fuzzy entropy weight method (EWM) was used to determine the criteria weights. The ranking of rice suppliers was achieved using a fuzzy multi-objective optimization based on ratio analysis (MOORA), fuzzy complex proportional assessment (COPRAS), and combinations of these two methods in different approaches. The methodology supports decision-makers in a rapidly evolving global environment by assisting importers, traders, suppliers, procurement, and logistics management, particularly for non-rice-cultivating countries in rice importation and supplier selection. The numerical analysis is grounded in a real-world case study of selecting rice suppliers in Jordan. The findings reveal that the various strategies yield both similar and different results. Furthermore, the integrated method is considered the most accurate for evaluating rice imports and suppliers, aligning closely with the reality of the current situation.

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.

A novel fuzzy SECA model based on fuzzy standard deviation and correlation coefficients for resilient-sustainable supplier selection

Selecting the best out of a pool of potential suppliers is one of the most significant challenges in supply chain management. Although numerous works have studied supplier selection (SS) as a function of individual criterion (e.g., sustainability or resiliency), few studies have explored it as an all-in-one. To address this gap, this study proposes a novel fuzzy multi-criteria decision-making (MCDM) framework for a real-world color and glaze case study. First, resilient-sustainable criteria are identified for evaluating the performance of the suppliers. Expert opinions and literature review are then utilized to establish a set of criteria in two different categories: sustainability and resiliency. This two-pronged approach ensured that important criteria are considered in the evaluation process. Next, a new fuzzy simultaneous evaluation of criteria and alternatives (FSECA) model is developed to highlight the most crucial criteria and the best supplier. The proposed model utilizes a predetermined set of criteria reference weights as benchmarks to calculate the final weights that closely align with these reference points. Moreover, the consideration of fuzzy standard deviation and correlation coefficients of fuzzy numbers are incorporated to take into account the inherent variability and uncertainty in the data, resulting in a more accurate and robust analysis of the data. To validate the proposed method, the findings are compared to the state-of-the-art fuzzy approaches for SECA method, and other MCDM techniques such as fuzzy weighted aggregated sum product assessment (FWASPAS), fuzzy technique for order performance by similarity to ideal solution (FTOPSIS), fuzzy Multi-Objective Optimization on the basis of Ratio Analysis (FMOORA), fuzzy simple additive weighting (FSAW), and fuzzy complex proportional assessment (FCOPRAS). Moreover, by applying the geometrical analysis for interactive aid (GAIA), a visualization approach, the results demonstrate that the performance of the proposed framework is satisfactory and highlight the most crucial criteria, such as the supplier responsiveness in resiliency and delivery in economic impact of sustainability categories. Since choosing supplier is a strategic decision, the proposed work can help managers to choose the right suppliers.

Putting into practice a decision-making framework for a thorough performance and location evaluation of solar photovoltaic plants in India from distinctive climate zones

Solar energy utilization has evolved tremendously in many countries to compete with conventional energy sources due to abundant availability, ongoing module price declines, and efficiency gains. Besides these advantages, most solar power plants continue to underperform due to various technical and environmental factors. Some solar projects in India even failed to meet the generation targets. Hence, to achieve sustainable development goals and be future-ready, solar power plants must be sustainably developed for power generation. One should focus on every sustainable aspect before constructing a powerplant, i.e., proper location selection. This study focuses on this problem and considers seven solar photovoltaic plants from different climatic zones in India to compare their performance and location criteria using fuzzy multi-criteria decision-making methods. Fuzzy Stepwise Weight Assessment Ratio Analysis is used to determine the criteria significance, while Fuzzy COmplex PRoportional ASsessment and Fuzzy ELimination Et Choix Traduisant la REalité (ELimination Et Choice Translating REality) are used to rank the seven solar photovoltaic plants. Given solar photovoltaic plants' high initial investment cost, economic criteria precede all other evaluation criteria in assessment, followed by technical, environmental, and connectivity criteria. The social criteria are neglected here because of the minor societal concerns about deploying solar photovoltaic plants. According to the results, the Unchahar solar photovoltaic plant (from the composite climatic zone) outperforms other selected photovoltaic plants and is determined to be the most appropriate plant. This is due to the lowest levelized cost of energy and lowest total cost for the Unchahar solar photovoltaic plant, as these criteria are the most significant per the study. This study's findings will be useful to energy decision-makers and may serve as a reference for constructing and developing future solar plants. Further, this study will make recommendations for future solar photovoltaic plant development. The study also includes sensitivity analysis to determine the data's robustness. This study is very robust, as the normalized alternative is constant for each criterion, and the ranking remains the same.

An Extended Application of Fuzzy Complex Proportional Assessment Decision-Making Approach to Diagnose Vector-Borne Diseases

Making medical decisions can be challenging because they heavily rely on the knowledge, expertise, and judgement of the doctor. A Physician needs to be knowledgeable, but they also need to understand many other things. Medical diagnosis employs a variety of multi-criteria decision-making approaches. The core goal of this study work is to present an appropriate decision-making model, Fuzzy Complex Proportional Assessment (F-COPRAS) to diagnose vector-borne diseases.

An Integrated Approach Based on MADM and MODM for Order Picking System Considering Human Factors

Optimizing order-picking systems (OPSs) while considering human factors and integrating key decisions is a major challenge for warehouse managers. This study presents a two-stage framework based on multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models to integrate decisions on picker selection, order batching, batch assignment, picker routing, and scheduling. In the first stage, the human factors affecting picker selection are considered as the problem’s criteria and the available pickers are treated as alternatives. The fuzzy entropy method and fuzzy COmplex PRoportional ASsessment (COPRAS) are used to weight the factors and rank the pickers, respectively. In the second stage, a three-objective mathematical model is formulated to minimize makespan and the operating costs of picking while maximizing the total scores of the selected pickers. The improved augmented epsilon constraint method (AUGMECON2) and the non-dominated sorting genetic algorithm II (NSGA-II) are applied to solve the proposed model. The performance of the two methods is tested on well-known benchmark instances and a real-world case study. The NSGA-II algorithm can generate optimal results using only about 6.58% of the CPU time required by AUGMECON2 to solve the problem. Our computational experiments show that increasing the number of pickers from 2 to 8 and doubling their capacity reduces the makespan by 2.61% and 2.74%, respectively.

An integrated approach to rank prospective sites for wind-powered Pumped Storage Hydroelectric plants to meet rising demand in the Indian subcontinent

With a paradigm shift towards renewable energy in the power sector, different sustainable energy-extracting mechanisms have sprung up. Among these, wind-powered Pumped Storage Hydroelectric (PSH) plants make use of the virtues of both wind and hydro energy. Being a hybrid system, it has found prominence across the world, making significant contributions to energy reliability and sustainability. However, in the Indian subcontinent, a region that is rapidly transitioning its energy dependence on fossil fuels to cleaner, greener sources, the presence of PSH power is still constrained. This work studies different locations in India under seven criteria from technical, geological, and socioeconomic perspectives that affect the construction of wind-powered PSH plants in India. Seven different water bodies from the country are selected and weighed against these criteria. A Fuzzy-Complex Proportional Assessment (Fuzzy-COPRAS) approach is then used for their comparison. The selected water bodies are ranked according to their suitability for constructing a wind-powered PSH plant. The authors conclude that the Tehri Dam Reservoir in the state of Uttarakhand and the Bhakhra Nangal Dam Reservoir in Himachal Pradesh are the top two optimal choices for profitable wind-powered PSH plant construction in India. The presented methodology aims to provide a guideline for policymakers and investors in their goal of promoting wind-powered PSH generation in India and can be replicated for different regions and other forms of renewable energy as well.

Risk quantification and ranking of oil fields and wells facing asphaltene deposition problem using fuzzy TOPSIS coupled with AHP

Asphaltene precipitation and its subsequent deposition always remain a major concern for Oil industry. Formulation of a comprehensive and reliable risk management system for asphaltene prone wells and fields is a challenging task because of the influence of diverse factors. In this study, a decision support system is developed for the asphaltene risk assessment in wells and fields. Since, the data present in the literature is scarce and not consistent, therefore, a hypothetical data of fields and their wells was considered to conduct the study comprehensively. Three hypothetical fields namely; Field A, Field B and Field C were assumed and in each field ten wells were taken into consideration. A decision support system for assessing the risk of asphaltene prone wells was developed using one of the popular and powerful multi-criteria decision making technique i.e. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) coupled with Analytic Hierarchy process (AHP). The risk of wells was evaluated using three criteria namely; Detection, Severity and Controls. These criteria were further sub-divided into sub-criteria and their data was assumed. The assumed data was transformed into Triangular fuzzy numbers for calculations. According to the final outcomes, Field A was proved be the most risky field followed by Field B and in the last comes Field C. The outcomes were further validated by other method namely; Fuzzy Complex Proportional Assessment (COPRAS) and all TOPSIS outcomes were found in good relationship with Fuzzy COPRAS. The proposed methodology proposed in this study will be landmark in risk ranking of asphaltene prone wells and fields.

Evaluation of Green Marketing Strategies by Considering Sustainability Criteria

The purpose of this study is to identify and prioritize green marketing strategies in the food industry by taking sustainability goals into consideration since it is very difficult to prioritize and select green marketing strategies when considering sustainability criteria in their implementation. Therefore, it is necessary to use an appropriate solution to prioritize these strategies. For this purpose, sustainability criteria and green marketing strategies are recognized. Then, sustainability criteria are screened using the fuzzy Delphi method. Subsequently, using the fuzzy stepwise weight assessment ratio analysis method, the criteria are weighted according to expert recommendations. Next, green marketing strategies in the Iranian food industry are ranked using several fuzzy multiple criteria decision-making methods: fuzzy additive ratio assessment, fuzzy complex proportional assessment, fuzzy technique for order preference by similarity to the ideal solution, and fuzzy Viekriterijumsko Kompromisno Rangiranje methods. Finally, the results are integrated with the help of the Copeland method to choose the best strategies. The results indicated that the criteria of ‘minimizing waste’, ‘minimizing environmental impacts’, and ‘consumer participation’ had the highest weight. In addition, the strategies for the ‘development of waste reduction technology, ‘development of market research to detect green needs’, and ‘use of recycled or reusable materials in products’ received a higher rank.

A novel MCDM approach to selecting a biodegradable dynamic plastic product: a probabilistic hesitant fuzzy set-based COPRAS method

The development of biodegradable polymers for both industrial and commercial uses is crucial nowadays due to the detrimental environmental effects of synthetic plastics. For a variety of uses, researchers have created numerous starch-based composites. The current study examines bioplastics made from maize and rice starch for packaging purposes. Several types of bioplastic samples are created using various ratios of gelatin, glycerol, citric acid, maize starch, and rice starch. People have discovered the value of plastics all around the world. It can be used for packaging, trash bags, liquid containers, throwaway quick service restaurant products, and other things. Regarding the negative aspect of plastics, their dumping after durability poses a serious risk to both people and wildlife. This prompted researchers to seek alternative natural resources that may be used to create flexible polymers that are recyclable, eco-friendly, and sustainable. It has been discovered that tuber and grain starches can be used to produce flexible biopolymers. The decision to choose the best among these choices is an MCDM problem because the carbohydrates from these suppliers have varying qualities. The Probabilistic Hesitant Fuzzy Set (PHFS)-based COmplex PRoportional ASsessment (COPRAS) method for solving uncertainty problems is utilized in this research study. To get the objective weights of the criteria in this case, we used the Critic method of weight determination. An example case of selecting the optimal hydrolyzes for biodegradable dynamic plastic synthesis was chosen to represent the applicability of the suggested approach. The findings demonstrate the feasibility of thermoplastic starches derived from rice and corn for packaging applications.

Efficiency measurement based on novel performance measures in total productive maintenance (TPM) using a fuzzy integrated COPRAS and DEA method

Total Productive Maintenance (TPM) has been widely recognized as a strategic tool and lean manufacturing practice for improving manufacturing performance and sustainability, and therefore it has been successfully implemented in many organizations. The evaluation of TPM efficiency can assist companies in improving their operations across a variety of dimensions. This paper aims to propose a comprehensive and systematic framework for the evaluation of TPM performance. The proposed total productive maintenance performance measurement system (TPM PMS) is divided into four phases (e.g., design, evaluate, implement, and review): i) the design of new performance measures, ii) the evaluation of the new performance measures, iii) the implementation of the new performance measures to evaluate TPM performance, and iv) the reviewing of the TPM PMS. In the design phase, different types of performance measures impacting TPM are defined and analyzed by decision-makers. In the evaluation phase, novel performance measures are evaluated using the Fuzzy COmplex Proportional Assessment (FCOPRAS) method. In the implementation phase, a modified fuzzy data envelopment analysis (FDEA) is used to determine efficient and inefficient TPM performance with novel performance measures. In the review phase, TPM performance is periodically monitored, and the proposed TPM PMS is reviewed for successful implementation of TPM. A real-world case study from an international manufacturing company operating in the automotive industry is presented to demonstrate the applicability of the proposed TPM PMS. The main findings from the real-world case study showed that the proposed TPM PMS allows measuring TPM performance with different indicators especially soft ones, e.g., human-related, and supports decision makers by comparing the TPM performances of production lines and so prioritizing the most important preventive/predictive decisions and actions according to production lines, especially the ineffective ones in TPM program implementation. Therefore, this system can be considered a powerful monitoring tool and reliable evidence to make the implementation process of TPM more efficient in the real-world production environment.

An integrated intuitionistic dense fuzzy Entropy-COPRAS-WASPAS approach for manufacturing robot selection

Manufacturing robots are used for industrial purposes. Robots used for manufacturing purpose have the power to create products from raw materials and are capable of operating endlessly even in lights-out situations for continuous production. Manufacturing robots can be used in applications like arc welding, spot welding, materials handling, machine tending, painting, machine cutting and so on. Selecting a robot for a given application is complex. However, robot selection is used in selecting a suitable robot for the preferred output with specific application ability. In recent years, many authors used multi-criteria decision-making methods to select a robot that meets the need. This study presents a hybrid model by integrating complex proportional assessment (COPRAS) with the weighted aggregates sum product assessment (WASPAS) methods in intuitionistic dense fuzzy set to select a manufacturing robot for a particular application. Intuitionistic dense fuzzy entropy is used in calculating the weights for the criteria and intuitionistic dense fuzzy COPRAS and WASPAS are used in ranking the best alternatives where in the optimum types of robots are obtained. Here, intuitionistic dense fuzzy sets are used as it is capable of dealing with the intangible factors while selecting a robot. On the basis of the manufacturing robot selection, two comparisons are given. First, the results are compared with various [Formula: see text] values between 0 and 1 and second the results are compared with the fuzzy set, intuitionistic fuzzy set and the dense fuzzy set to show the effectiveness of the proposed methodology with the intuitionistic dense fuzzy set.

Fuzzy decision support system for sustainable operational performance optimization for boiler unit in milk process industry

This work proposes a new sustainable operational performance optimization-based decision support system for assessing the failure behaviour of the Boiler Unit (BU) in the milk process industry. A novel fuzzy Jaya Lambda–Tau Optimization (JLTO) approach-based modelling was established for tabulating various reliability indices of BU. Fuzzy Set (FS) theory-based concepts were applied to consider the vagueness/uncertainty involved in the collected data. JLTO approach-based results show decrease in the boiler unit’s availability with the increase in uncertainty level. Further, to improve the boiler unit’s availability using qualitative data obtained from expert’s opinion, Failure Mode and Effect Analysis (FMEA) was performed. To address the shortcomings of conventional FMEA approach-based results and to pinpoint the riskier failure causes contributing to decrease in system’s availability, Fuzzy-Complex Proportional Assessment (FCOPRAS) decision-making approach was integrated with FMEA approach. Fuzzy Combinative Distance-based Assessment (FCODAS) approach was also applied to compare the ranking results for accurate decision-making. The proposed integrated framework predicts the failure behaviour with high accuracy by considering the uncertainty and finding the riskier failure causes of the considered system which is one of the main drawbacks of existing FS theory-based integrated models. The proposed model-based results will help in developing an optimal digitalized maintenance schedule, which will be beneficial for improving the overall performance of the considered unit. The digitalized maintenance schedule will also be useful for BU of various other industries across the globe. Sensitivity analysis was also done for assessing the robustness of the proposed sustainable operational performance optimization-based decision support system.

A new model based on the extended COPRAS method for improving performance during the accreditation process of Indian Higher Educational Institutions

AbstractThe standard of any country in the global world is dependent highly on the intellect content of the people. This intellect content is in turn dependent exclusively upon the quality of the higher education in the country. The aim of this research work is to perform an empirical evaluation to rank various quality parameters suggested by the National Board of Accreditation. This national board was established in India in 1994 by the All India Council for Technical Education with the main motive of assessing the quality of various Higher Educational Institutions. It provides marks to institutions out of 1000 on the basis of 10 parameters, which are further subdivided into 75 subparameters. This research work helps to guide educational institutions to access their weak points during and before applying for this accreditation to recover from them timely and in the most effective manner. An extended model of fuzzy COPRAS (COmplex PRoportional ASsessment) is proposed as an improved multicriteria decision‐making approach to first identify and then set preferences to help institutions improve their data related to accreditation key indicators. The work is divided into four steps. First, all the critical evaluation factors for the accreditation process are identified in consultation with senior, experienced and qualified academicians. They are then converted into fuzzy triangular numeric values and crisp weights. Lingual values and corresponding fuzzy weights of various preference key indicators are then identified and converted into crisp weights. Further critical factors are divided into price and profit values. Finally, key indicators are ranked using extended fuzzy COPRAS. A comparative ranking analysis is generated as end result through fuzzy COPRAS, TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and WASPAS (Weighted Aggregated Sum Product Assessment) approaches, which shows that the proposed model generates better ranking results. The proposed model is applicable to any accreditation process with a varied set of parameters and their subparameters.

Extending the application of fuzzy COPRAS to optimal product design

The availability of customized and multifunctional designs of a particular product in the market due to varieties in users’ requirement and demand necessitates that product design must be properly done before progressing to developmental stage. This implies that the importance of decision making in the selection of optimal conceptual design cannot be overstressed. The main contribution of this article is the implementation of Fuzzy COPRAS (COmplex PRoportional ASsessment) model as a decision tool for identification optimal design concept. The weights of the design features and sub-features were determined in the form linguistic terms generated from a triangular fuzzy number in order to avoid apportioning of crisp values in the decision process. An analytical approach was applied by creating a sub-aggregate matrix for each design feature, considering all the design alternatives in order to develop a sub-decision matrix. The relative significance or priorities of the design alternatives obtained from the maximization and minimization indices are defuzzified by applying the minimum degree of possibility in order to rank the design alternatives. The findings from the application of the Fuzzy COPRAS methodology to four design alternatives of pipe bending machines using eight design features and several sub-features indicates that the methodology can identify an optimal design concept from a set of alternative designs. The application of the methodology also shows that there are slight differences in the priority values of design alternatives next to the optimal design concept. The implication and recommendation from the application is that choosing any of the design alternatives other than the optimal design concept will provide the same risk

Optimization of Wear Parameters for Duplex-TiAlN Coated MDC-K Tool Steel Using Fuzzy MCDM Techniques

The present work evaluates the effects of different tribological process parameters on the measured responses such as hardness, coefficient of friction, surface roughness, wear mass loss and wear depth of duplex-TiAlN coated MDC-K tool steel material. The considered tribological process parameters are load, sliding velocity, and sliding distance. A full factorial design with 27 experimental runs is employed and based on the response values, an optimal combination of the tribological process parameters is subsequently determined. Different multi-objective optimization techniques, like overall evaluation criteria and fuzzy-based multi-criteria decision-making methods (fuzzy evaluation based on distance from the average solution, fuzzy technique for order of preference by similarity to ideal solution, and fuzzy complex proportional assessment) are utilized to identify the optimal intermixes of the considered tribological process parameters. Sensitivity analysis with respect to changing weights of the responses is performed to validate the derived rankings of the trials, whereas the results of analysis of variance revealed the most significant parameters were influencing the responses. In addition to this, two different published problems related to optimization of wear parameters were solved using the proposed method to check its capability.

Assessing Green Approaches and Digital Marketing Strategies for Twin Transition via Fermatean Fuzzy SWARA-COPRAS

Integrating green approaches and digital marketing strategies for Information and Communication Technologies (ICTs), which reduce environmental risks to desired levels by eliminating emissions and pollution, is considered one of the most promising solutions for logistics companies. The study strives to bring a practical and applicable solution to the decision problem involving the selection of indicators for green approaches and digital marketing strategies for ICTs in the logistics sector. An integrated Fermatean Fuzzy Step-wise Weight Assessment Ratio Analysis (FF–SWARA) and Fermatean Fuzzy Complex Proportional Assessment (FF–COPRAS) methodology is applied to evaluate green approaches and digital marketing strategies. Concerning the findings, the foremost criterion is “data management,” whereas the best strategy is “programmatic advertising.” To the best of the authors’ knowledge, there is no other study that both offers a strategy selection for the logistics industry and considers environmental protection, sustainability, digital transformation, energy costs, and social and economic factors. The study is a part of ongoing research on productivity, sustainability, the environment, digitization, recycling and estimating levels of waste reduction, as well as business practices, competitiveness and ensuring employee satisfaction and resource efficiency. Also, it investigates the similarities and dissimilarities in the green approach practices of business in logistics and determines the extent to which these practices could be reflected. It is expected to ensure a roadmap for green approach practices and to support sustainable and ecological awareness efforts for ICTs in the logistics sector. Logistics companies can select an integrated digital strategy based on green informatics that suits them using the decision model employed in this study, which can handle uncertainties effectively. In this regard, the study’s findings, which focus on reaching customers and the most precise target audience in digital applications for businesses, are critical for developing strategy, plan and process.

Estimation of the effectiveness of multi-criteria decision analysis and machine learning approaches for agricultural land capability in Gangarampur Subdivision, Eastern India

Land suitability analysis (LSA) is an evaluation method that measures the degree to which land is suitable for certain land use. The primary aims of this study are to identify potentially viable agricultural land in the Gangarampur subdivision (West Bengal) using Multiple Criteria Decision Making (MCDM) and machine learning procedures and to evaluate the efficacy of the employed methodologies. The Analytic Hierarchy Process (AHP) model was used to assign relative weights to the fifteen various criteria in this suitability analysis, and then the Fuzzy Complex Proportional Assessment (FCOPRAS) model was applied using the AHP's normalised pairwise comparison matrix, whereas the Waikato Environment for Knowledge Analysis (Weka) Software was used to apply machine learning algorithms to the field data. The Random Forest (RF) model, on the other hand, is a better fit for the locational study of soil potential. According to the RF findings, areas of 14.67 per cent (15368.46 ha) are excellent (ZONE V) for growing crops, approximately 22.30 per cent (23367.9 ha) are highly suitable (ZONE IV), and 23.63 per cent (24762.12 ha) are moderately suitable (ZONE III) for cultivation, respectively. The numbers for FCOPRAS are roughly 15.39% (16130.52 ha), 22.54% (23620.65 ha), and 19.79% (20733.26 ha). The Receiver Operating Characteristic (ROC) curve and accuracy measurements of the results indicate the high accuracy of the applied models, with Random Forest and FCOPRAS being the most popular and effective techniques. This study will make an important contribution to evaluations of soil fertility and site suitability. This will help local government officials, academics, and farmers scientifically use the land.