Step-wise Weight Assessment Ratio Analysis Research Articles

Evaluation of sustainable hydrogen production technologies on an industrial scale using comparative analysis of decision-making methods.

Due to its various advantages in different industrial fields, hydrogen can provide energy based on sustainability goals and recreates a critical function in the economy of countries. In this regard, evaluating hydrogen production technologies on an industrial scale is necessary for industrial development and economic growth. Therefore, this study proposes a comprehensive, integrated framework of hybrid fuzzy decision-making for assessing hydrogen production technologies in Iran. In addition to considering sustainability factors, political, technical, and reliability indicators are also assessed in this research to make a comprehensive assessment. The Fuzzy Step-wise Weight Assessment Ratio Analysis (F-SWARA) technique determines the importance of indicators, and the Fuzzy Weighted Aggregates Sum-Product Assessment (F-WASPAS) approach ranks technologies. The weighing findings indicated that the sub-indices of investment cost, technical infrastructure development, and implementation costs were introduced as the most significant sub-indices with weights of 0.226, 0.151, and 0.126, respectively. The evaluation findings with the F-WASPAS method and comparative analysis with various decision-making methods revealed that electrolysis based on solar energy and electrolysis based on wind energy technologies had the highest preference. In this regard, the infrastructure and costs of hydrogen production can be improved by presenting various incentives, such as improving financial conditions while attracting investment and increasing cooperation with top companies. So, sustainable development, economic growth, and industrial development are provided.

Assessment of Third-Party Reverse Logistics Provider Through the SWARA-WASPAS Integrated Framework

The selection of the most suitable third-party provider for reverse logistics (RL) activities is a key factor in initiating effective RL processes. However, the process of identifying the optimal third-party provider involves navigating through various conflicting objectives and criteria. This task is intricate and time-consuming, necessitating the application of multi-criteria decision-making (MCDM) approaches. This study addresses the evaluation and selection of the best 3PRLP by introducing a comprehensive MCDM framework. Therefore, this study aims to assist a manufacturer specializing in milk and dairy products who has opted to transfer its reverse logistics operations to a logistics service provider. The objective is to aid them in selecting the most suitable 3PRLP. The proposed framework incorporates the SWARA (Step-wise Weight Assessment Ratio Analysis) technique for determining weights and the WASPAS (Weighted Aggregated Sum Product Assessment) method for thoroughly evaluating alternatives. In this study, a panel of four experts made a joint decision after identifying six criteria and five alternatives. The research concluded that the reverse logistics cost (C1) emerges as the most pivotal factor, with the service quality of reverse logistics (C3) being recognized as the least significant criterion. As a result, A1 stands out as the top choice among the 3PRLP.

EVALUATION OF THE CHANGE IN PERSPECTIVES OF SME EXECUTIVES TOWARDS THE INDUSTRY 4.0 PROCESS AND OPPORTUNITIES IN DIGITAL TRANSFORMATION WITH THE SWARA METHOD

The aim of this study is to determine the perspectives of small and medium-sized enterprises (SMEs) in Turkey towards the digital transformation that comes with Industry 4.0 and to determine the potential development and orientation of the information they had. In this study, the questions asked to thirty-two SME executives five years ago with a semi-structured interview were again directed to the same individuals. According to the results, it’s been seen that the executives have more knowledge of the Industry 4.0 process and have more information about the relevant technologies compared to the answers in 2017. In addition, executives have started to take more part in activities in this field. During the five-year period, the most concerned issue of executives was identified as cyber security and budget planning. In the second application of the study, it’s also aimed to determine the importance levels of some opportunities that are predicted to be caused by the Industry 4.0 transformation process on SMEs. In order to realize this aim, Step-Wise Weight Assessment Ratio Analysis (SWARA) application was made with five experts, and the first three opportunities with the highest level of importance were determined as low-cost production, competitiveness and improvement in production defects.

Sustainable Afforestation Strategies: Hybrid Multi-Criteria Decision-Making Model in Post-Mining Rehabilitation

This article describes an effective approach for selecting suitable plant species for afforestation in post-mining rehabilitation. The research was conducted in the Western Black Sea region of Turkey. The aim of the research is to perform accurate criteria weighting and species prioritization for afforestation in post-mining degraded areas. This helps to ensure consistent conditions for the future use of the site as a forest, sustainability of nature, and selection of appropriate species adapted to the difficult post-mining conditions. In this study, which is a multi-criteria decision-making problem (MCDM), the weights of the criteria were determined by stepwise weight assessment ratio analysis (SWARA), and the priority ranking of the species was determined by the analytic hierarchy process (AHP). Analyses were carried out with 10 afforestation criteria and five tree species. According to the analysis, the top three ranked criteria are Economic Efficiency > Carbon Stock and Credit > Reducing Afforestation Cost. The five species’ priority ranking is Robinia pseudoacacia L. (0.456) > Alnus glutinosa subsp. glutinosa (0.248) > Populus nigra subsp. nigra (0.146) > Salix alba L. (0.103) > Quercus robur subs. robur (0.048). The hybrid approach is expected to increase the effectiveness of post-mining rehabilitation works.

A Hybrid Triangular Fuzzy SWARA-MAROCS Approach for Selecting Optimal and Smart Logistic Enterprise Based on IoT, Blockchain, and UAVs

With the development of technology and the Internet of Things (IoT), the use of electronic commerce has become necessary. There are many logistical enterprises now use the IoT to support their delivery process via drones, due to the numerous advantages they offer over traditional methods. However they face some restrictions and security concerns, so blockchain technology was used to overcome these restrictions and follow up on the delivery process to meet the requirements of e-commerce. In light of this, choosing the most suitable logistics enterprises to meet the needs of customers in a given situation has become a challenging task for decision-makers. To facilitate this task, we have introduced a systematic approach that uses triangular fuzzy step-wise weight assessment ratio analysis (SWARA) and triangular fuzzy measurement of alternatives and ranking according to compromise solution (MAROCS) methods to evaluate and rank alternatives. Fuzzy SWARA is used to determine the weights of evaluative criteria, while Fuzzy MARCOS is used to rank alternatives and select the best of them. This method eliminates ambiguity and imprecision in the decision-making process.

Evaluation of agile attributes for low-cost carriers to achieve sustainable development using an integrated MCDM approach

PurposeSince conducting agile strategies provides sustainable passenger satisfaction and revenue by replacing applied policies with more profitable ones rapidly, the focus of this study is to evaluate agile attributes for managing low-cost carriers (LCCs) operations by means of resources and competences based on dynamic capabilities built on resource-based view (RBV) theory and to achieve sustainable competitive advantage in a volatile and dynamic air transport environment. LCCs in Turkey are also evaluated in this study since the competition among LCCs is high to gain market share and they can adapt quickly to all kinds of circumstances.Design/methodology/approachTwo well-known Multi-Criteria Decision-Making Methods (MCDM) named as the Stepwise Weight Assessment Ratio Analysis (SWARA) and multi-attributive border approximation area comparison (MABAC) methods by employing Picture fuzzy sets (PiFS) are employed to determine weight of agile attributes and superiority of LCCs based on agile attributes in the market, respectively. To check the consistency and robustness of the results for the proposed approach, comparative and sensitivity analysis are performed at the end of the study.FindingsWhile the ranking orders of agile attributes are Strategic Responsiveness (AG1), Financial Management (AG4), Quality (AG2), Digital integration (AG3) and Reliability (AG5), respectively, LCC2 is selected as the best agile airline company in Turkey with respect to agile attributes. SWARA and MABAC method based on PiFS is appropriate and effective method to evaluate agile attributes that has important reference value for the airline companies in aviation industry.Practical implicationsThe findings of this study will support managers in the airline industry to conduct airline operations more flexibly and effectively to take sustainable competitive advantage in unexpected and dynamic environment.Originality/valueTo the author' best knowledge, this study is the first developed to identify the attributes necessary to increase agility in LCCs. Thus, as a systematic tool, a framework is developed for the implementation of agile attributes to achieve sustainable competitive advantage in the airline industry and presented a roadmap for airline managers to deal with crises and challenging situations by satisfying customer and increasing competitiveness.

Empowering sustainable manufacturing: Unleashing digital innovation in spool fabrication industries

In industrial landscapes, spool fabrication industries play a crucial role in the successful completion of numerous industrial projects by providing prefabricated modules. However, the implementation of digitalized sustainable practices in spool fabrication industries is progressing slowly and is still in its embryonic stage due to several challenges. To implement digitalized sustainable manufacturing (SM), digital technologies such as Internet of Things, Cloud computing, Big data analytics, Cyber-physical systems, Augmented reality, Virtual reality, and Machine learning are required in the context of sustainability. The scope of the present study entails prioritization of the enablers that promote the implementation of digitalized sustainable practices in spool fabrication industries using the Improved Fuzzy Stepwise Weight Assessment Ratio Analysis (IMF-SWARA) method integrated with Triangular Fuzzy Bonferroni Mean (TFBM). The enablers are identified through a systematic literature review and are validated by a team of seven experts through a questionnaire survey. Then the finally identified enablers are analyzed by the IMF-SWARA and TFBM integrated approach. The results indicate that the most significant enablers are management support, leadership, governmental policies and regulations to implement digitalized SM. The study provides a comprehensive analysis of digital SM enablers in the spool fabrication industry and offers guidelines for the transformation of conventional systems into digitalized SM practices.

Small and medium-sized enterprises in emerging markets and foreign direct investment: an integrated multi-criteria decision-making approach

ABSTRACT This study examines the impact of critical success factors (CSFs) on small and medium-sized enterprises (SMEs). We propose a strategic roadmap for early internationalization strategies to promote foreign direct investment (FDI). Based on the resource-based view (RBV), this research work aims to understand the CSFs that can drive early internationalization within the setting of SMEs, especially in the textile industry of emerging markets. Based on an extensive state-of-the-art literature review we identify relevant CSFs. The set of CSFs is then evaluated using interviews with nine experts and further analysed through multi-criteria decision-making (MCDM) methods, the step-wise weight assessment ratio analysis (SWARA), the additive ratio assessment method (ARAS), and grey relational analysis (GRA) by using Lingo V.18 and MATLAB. Our empirical findings reveal a set of 15 significant CSFs among 28, emphasizing entrepreneurial process, finance, and corporate venturing. In addition, our results underline that it is vital to employ the identified CSFs for SMEs in advancing early internationalization strategies during the COVID-19 pandemic. This study provided significant policy implications for Pakistani SMEs’ by unfolding the suggested CSFs to foster economic development.

Sustainability performance assessment of freight transportation modes using an integrated decision-making framework based on m-generalized q-neutrosophic sets

The freight transport industry is one of the primary sectors responsible for excessive energy consumption and greenhouse gas emissions. Restructuring international and domestic freight transport chains based on sustainability and green transportation is critical for practitioners and policymakers to reduce pressure on the logistics and transportation industries. This study aims to develop a mathematical model for selecting the most appropriate transportation type, and accordingly, the optimal route in transportation operations to improve the sustainability performance of the freight transportation industry. Therefore, the main goal is to choose the most suitable route and transportation type which contributes to create a more eco-friendly and sustainable transportation system. For this purpose, Neutrosophic Number-based Delphi (NN-Delphi), m-Generalized q-Neutrosophic Sets (mGqNSs)-based Stepwise Weight Assessment Ratio Analysis (MGqNS-SWARA) and mGqNSs-based Additive Ratio Assessment (mGqNS-ARAS) are developed and implemented to set the influential criteria, compute the weights of these criteria, and identify the sustainability performance of the freight mode variants, respectively. According to the final results, "Cargo security" and "Accident rates" are the most important criteria with a relative importance score of 0.0237, contributing to the sustainability of load transport modes. Moreover, "Maritime Transport Mode" is identified as the most sustainable transportation type with a relative importance score of 0.7895. Finally, it is revealed that there is a positive relationship between maritime transport and sustainability.

Assessing Supplier Disruption Risks Using a Modified Pythagorean Fuzzy SWARA–TOPSIS Approach

As the complexity and uncertainty of global supply chains escalate, disruptions have become an increasingly common challenge in supply chain management. Suppliers, who serve as essential connectors for the seamless movement of goods and materials critical to production and distribution, are often at the center of these disruptions, highlighting their significant impact on the overall stability of the supply chain. This study proposes an innovative approach to assessing supplier disruption risks by combining the Pythagorean Fuzzy Step-wise Weight Assessment Ratio Analysis (PF-SWARA) with the Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (PF-TOPSIS). By reviewing the literature and consulting with supply chain experts, eight key risk factors were identified. The PF-SWARA method then quantifies the significance of these risks, while a modified PF-TOPSIS technique calculates each supplier’s risk score, facilitating the prioritization of suppliers for targeted improvement. The findings of the study indicate that “natural disasters and geopolitical risks,” “financial instability,” and “delivery delays” emerge as the top three critical disruption risk factors. Suppliers facing higher disruption risks should, therefore, formulate improvement strategies that target these three areas.

A novel multicriteria assessment framework for evaluating the performance of the EU in dealing with challenges of the low-carbon energy transition: an integrated Fermatean fuzzy approach

Climate change, global warming, greenhouse gas emissions, and many other reasons have motivated countries worldwide to change energy systems to move toward low-carbon energy systems; however, the low-carbon energy transition has faced many challenges that motivate the present study to identify the challenges and evaluate the performance of the EU according to challenges. To this end, seventeen challenges were identified through a systematic literature review and classified into five groups: economic, institutional, technical, social, and environmental. Subsequently, fifty-three indicators were selected to measure the performance of the EU in dealing with challenges. Furthermore, a Fermatean “Stepwise Weight Assessment Ratio Analysis” method was applied to determine the subjective weight of identified challenges, while the method based on the removal effects of criteria was applied to determine the objective weight of selected indicators. Afterward, the “Technique for Order of Preference by Similarity to Ideal Solution” method was applied to evaluate the performance of the EU in dealing with the challenges of the low-carbon energy transition for 2015 and 2020. The results indicated that energy justice, mitigation costs, land use, and lack of infrastructure are the most significant social, economic, environmental, institutional, and technical challenges. Also, the Netherlands had the best performance in 2015, followed by Germany; in contrast, Germany improved its energy system and took first place in 2020.

Applying hesitant q-rung orthopair fuzzy sets to evaluate uncertainty in subsidence causes factors

Land subsidence is a widespread problem impacting communities worldwide. Understanding the causes and factors of land subsidence is crucial for identifying and prioritizing effective mitigation measures. One of the main reasons for prioritizing land subsidence causes is the potential impact on infrastructure and the environment. The main objective of this paper is to emphasize the importance of prioritizing the causes of land subsidence. By understanding and prioritizing the factors contributing to land subsidence based on their impact and urgency, the aim is to develop targeted strategies for mitigation, inform policy decisions, and prevent further exacerbation of this problems. The study comprises three phases, where experts in the field provide their opinions and propose a robust hybrid framework. This framework integrates the Failure Mode and Effect Analysis (FMEA) and Step-wise Weight Assessment Ratio Analysis (SWARA) with Hesitant q-rung orthopair fuzzy set (Hq-ROFS). The performance of the proposed technique was then compared with two other decision-making techniques for evaluating and ranking land subsidence causes. According to the results, extraction of groundwater, excessive irrigation using groundwater, and oxidation and drainage of organic soils were identified as primary drivers of subsidence.

Collaboration of Vague Theory and Mathematical Techniques for Optimizing Healthcare by Recommending Optimal Blockchain Supplier

The Internet of Medical Things (IoMTs) has the potential to revolutionize healthcare delivery by connecting medical devices and applications to healthcare IT systems via the internet. This connectivity enables the collection, transmission, and analysis of patient data, facilitating remote healthcare delivery and enhancing patient outcomes. However, the security and privacy of the data transmitted and stored within IoMT systems remain critical concerns. Blockchain technology offers a promising solution to address these challenges in IoMTs applications. Blockchain provides a decentralized and immutable ledger where transactions, in this case, patient data, are securely recorded and cannot be altered retroactively. This ensures the integrity and confidentiality of sensitive medical information while eliminating the need for a central authority to manage data exchange. This study proposes a comprehensive fuzzy decision-making framework for selecting the best blockchain provider in the IoMT field. The framework comprises two main steps: Determining the relative importance weights of the criteria utilizing the Stepwise Weight Assessment Ratio Analysis (SWARA) method. Determining the most suitable supplier for blockchain services providers employing the Ranking Alternatives with the Fuzzy ROOT Assessment Method (RAM) technique. A comparison between Fuzzy Multi-Criteria Decision-Making (MCDM) methods is employed to ensure the robustness and validity of the proposed framework.

Decision-Making for Project Delivery System in Construction Projects Based on SWARA-TOPSIS Methods

The owner of construction projects often faces difficulty choosing the appropriate project delivery system. The construction project’s owner must choose a suitable project delivery system throughout the project's early decision-making stages. This choice greatly impacts the success of the project. This paper aims to construct a method for project delivery decisions. This method can provide a reference for the owner to select the proper project delivery system. This research described the factors affecting the decision about the project delivery method through a literature review. These factors were categorized into eight groups: scope, time, quality, cost, risk and relationships, owner organization, project characteristics, and external environmental factors. Then, a combined methodology of Stepwise Weight Assessment Ratio Analysis (SWARA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are proposed for project delivery system selection. The SWARA is used to determine the factors’ weights. The TOPSIS technique determines the rank of the alternatives proposed for the delivery system in the various categories. The results showed the most significant factors related to the cost with a weight of 0.335, and the design-build is the closest to the ideal solution and ranked first among the other suggested project delivery systems with a relative closeness of 0.829. This paper stipulates the basis for such a process of decision-making.

A Novel Data-Envelopment Analysis Interval-Valued Fuzzy-Rough-Number Multi-Criteria Decision-Making (DEA-IFRN MCDM) Model for Determining the Efficiency of Road Sections Based on Headway Analysis

The capacity of transport infrastructure is one of the very important tasks in transport engineering, which depends mostly on the geometric characteristics of road and headway analysis. In this paper, we have considered 14 road sections and determined their efficiency based on headway analysis. We have developed a novel interval fuzzy-rough-number decision-making model consisting of DEA (data envelopment analysis), IFRN SWARA (interval-valued fuzzy-rough-number stepwise weight-assessment-ratio analysis), and IFRN WASPAS (interval-valued fuzzy-rough-number weighted-aggregate sum–product assessment) methods. The main contribution of this study is a new extension of WASPAS method with interval fuzzy rough numbers. Firstly, the DEA model was applied to determine the efficiency of 14 road sections according to seven input–output parameters. Seven out of the fourteen alternatives showed full efficiency and were implemented further in the model. After that, the IFRN SWARA method was used for the calculation of the final weights, while IFRN WASPAS was applied for ranking seven of the road sections. The results show that two sections are very similar and have almost equal efficiency, while the other results are very stable. According to the results obtained, the best-ranked is a measuring segment of the Ivanjska–Šargovac section, with a road gradient = −5.5%, which has low deviating values of headways according to the measurement classes from PC-PC to AT-PC, which shows balanced and continuous traffic flow. Finally, verification tests such as changing the criteria weights, comparative analysis, changing the λ parameter, and reverse rank analysis have been performed.

Evaluation of disassembly line layouts using an integrated fermatean fuzzy decision-making methodology: An application for refrigerator disassembly line

The layout focusing on the physical arrangement of workstations is an important consideration when designing a disassembly line. The layout of a line has a significant impact on its efficiency. The new problem addressed within this study is evaluating the main disassembly line layout types with respect to a thorough hierarchy of evaluation criteria classifying quantitative and qualitative. The addressed problem is modeled as multi-criteria decision-making (MCDM). To this end, a novel methodology that combines Stepwise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods under the Fermatean fuzzy environment is proposed for sustainable waste management. Fermatean fuzzy SWARA (FF-SWARA) is used to find out the weight of criteria consisting of disassembly line balancing characteristics, reconfigurability, working conditions, and end-of-life product characteristics. Fermatean fuzzy WASPAS (FF-WASPAS) is used to rank the alternative disassembly line layouts consisting of straight, U-shaped, two-sided, and parallel lines considering the weight of criteria. The multi-objective mixed integer linear programming (MILP) model for the disassembly line balancing problem (DLBP) of each line layout type is integrated into the proposed methodology. This integration reflects the effect of balancing performance measurements such as the number of stations, removing hazardous parts early, removing high demand parts early, and line lead time. Reconfigurability principles, working conditions, and product characteristics that rely on expert opinions are considered under the qualitative criteria. The developed methodology was applied to a refrigerator disassembly line and verified by sensitivity analysis and comparative studies. The results show that the two-sided disassembly line layout is the best option.

Determining Risk Management Priorities and Suggesting Strategies in the Management of Family Health Centres

Aim: The purpose of this study is to identify the risks faced by decision-makers in the management of Family Health Center (FHC) and to propose strategies according to their relative importance. Materials and Methods: Eight different risks were identified as a result of the literature review. In order to evaluate the identified risks, four different physicians with at least ten years of managerial experience in FHCs and two health managers with at least ten years of experience in their field were included in the study. SWARA (Stepwise Weight Assessment Ratio Analysis) method was used to evaluate and analyze the opinions. SWARA method is a weight calculation method based on expert opinions developed for the finalization of decision problems. Results: According to the results of the analysis, experts ranked financial risks (%27), clinical and patient safety risks (%21), and operational risks (%15) as the most important risks, respectively. Hazard risks (%4) were assessed as the least risky in relative terms. For FHCs to carry out their activities effectively, financial risks should be minimized first. Conclusions: Additional measures should be taken by the public for the use and rental of FHCs. Diversification of other sources of income can be ensured by expanding the health services provided beyond the resource transfer linked to the public budget. Regional investment and incentives can be developed by municipalities. Strengthening data protection policies to assess the compliance of medical practices with legal standards and to ensure the security of personal patient data can also reduce legal risks. In FHCs, general safety issues, especially patient safety, are also priority issues that should be investigated and legislation should be developed. In addition, management courses/training can be added to the family medicine specialty curriculum to improve the managerial skills of physicians.

Evaluation of micromobility risk management alternatives using interval-valued q-rung orthopair fuzzy interaction operators-based WISP method

Micro-mobility has become a mode of transportation since the 2010 s. Because this mode is relatively new, there are many unknowns, and the associated risks must be evaluated so they can be managed safely and effectively. This paper aims to introduce a decision support system (DSS) for ranking risk management alternatives in the micro-mobility sector under an interval-valued q-rung orthopair fuzzy (IVq-ROF) context. In this context, firstly IVq-ROF-interaction aggregation operators (AOs) are introduced for aggregating the information from IVq-ROF perspective. These AOs can provide a flexible mode to tackle realistic decision support problems within the IVq-ROF set (IVq-ROFS) context. Second, a definition of entropy and new entropies for IVq-ROFS is introduced. Third, a hybrid weight-determining formula is presented using an entropy-based weighting approach for the objective weight of criteria and a stepwise weight assessment ratio analysis (SWARA) method for the subjective weight of criteria. Fourth, the weighted integrated sum-product (WISP) approach is expanded from the IVq-ROF perspective. Further, the developed model is utilized in a case study of risk management alternative evaluation in the micro-mobility sector, which shows its applicability and feasibility under the IVq-ROF environment. Sensitivity and comparative investigations are studied to confirm the strength and stability of the obtained result. The best alternative has emerged as the state-private sector cooperation in successfully allocating the risk in the micro-mobility sector. The final results conclude that the developed methodology can provide more accurate decisions while considering multiple inducing indicators and input uncertainties.

Determination of best renewable energy sources in India using SWARA-ARAS in confidence level based interval-valued Fermatean fuzzy environment

The requirement for renewable energy sources arises from the depletion of fossil fuels and the increasing energy demand. A case study in India has been conducted in this context to identify the most promising renewable energy sources. A novel multi-attribute group decision-making (MAGDM) method integrating stepwise weight assessment ratio analysis (SWARA) and additive ratio assessment (ARAS) has been proposed for this purpose. We have used interval-valued Fermatean fuzzy numbers for data representation due to their ability to accommodate a broad range of fuzzy information. By introducing confidence level-based aggregation operators in an interval-valued Fermatean fuzzy environment, we avoid erroneous assessments by decision experts. We have employed objective and subjective weight determination approaches to determine decision experts’ weights. The SWARA method has been used for attribute weight evaluation due to its simplicity and efficiency, and the ARAS method is employed for alternative ranking and optimal choice selection using the utility degree. The findings show the potential to expand solar and wind power, whereas geothermal energy is not yet suitable for widespread deployment because of its early stage of development. We find the technical and environmental aspects to be especially significant during the decision-making process. To demonstrate the proposed method’s applicability, we have compared the results with existing MAGDM methods. To establish its stability, we conducted a sensitivity analysis. The presented MAGDM method can help make informed decisions regarding implementing solar energy as a primary source of energy supply in India, thereby aiding decision-making for policymakers, regulators, energy planners, and stakeholders.

Multi-Objective Optimization of Cutting Parameters for Polyethylene Thermoplastic Material by Integrating Data Envelopment Analysis and SWARA-Based CoCoSo Approach

This paper presents a comprehensive study on the multi-objective optimization of cutting parameters for Polyethylene (PE) thermoplastic material utilizing a CO2 laser. Recognizing the pivotal role of precise and efficient cutting in various sectors, from packaging to biomedical engineering, we integrate two potent analytical methodologies - Data Envelopment Analysis (DEA) and Step-Wise Weight Assessment Ratio Analysis (SWARA)-based Comprehensive Criteria Score Optimization (CoCoSo) approach. The cutting parameters chosen for the study were material thickness, power, and cutting speed. The experiments were conducted according to the Taguchi L18 orthogonal array. Measurements of surface roughness and kerf width were performed to examine the cutting quality. Additionally, another response variable, material removal rate, was calculated. By integrating Data Envelopment Analysis and SWARA-Based CoCoSo approach, the experimental condition that yielded the lowest surface roughness, kerf width, and the highest material removal rate was determined. The optimum experimental condition was found to be 4 mm material thickness, 80 W laser power, and 15 mm/s cutting speed. This work, therefore, paves the way for the innovative application of these combined methodologies in enhancing the production processes of PE and other thermoplastic materials, with clear implications for cost-effectiveness and sustainability in the manufacturing sector.