Step-wise Weight Assessment Ratio Analysis Method Research Articles

Enhancing readiness degree for Industrial Internet of Things adoption in manufacturing enterprises: An integrated Pythagorean fuzzy approach

This study proposes a hybrid multi-criteria decision-making (MCDM) methodology designed to enhance the readiness for adopting the Industrial Internet of Things (IIoT) in manufacturing enterprises. The Pythagorean fuzzy approach is employed to address uncertainty and imprecision throughout decision-making processes. The development framework in this study incorporates TOE (Technology-Organization-Environment) and HOT fit (Human-Organization-Technology) to pinpoint barriers to IIoT adoption. Additionally, a triple helix model (THM) emphasizing on the synergy among university-industry-government is utilized to formulate pragmatic strategies. The agro-food processing industry in Thailand is used as a case study. In this study, even barriers are identified and validated through the Delphi method. The SWARA (Step-wise Weight Assessment Ratio Analysis) method determines the importance weights of these barriers, revealing “lack of digital culture”, “lack of knowledge and expertise,” and “job displacement concerns” as the three most critical barriers. The COBRA (COmprehensive Distance Based Ranking) method is employed to prioritize pragmatic strategies under THM, indicating that the role of the university in enhancing human capital capabilities is the most important, followed by the government's roles in enabling national ICT infrastructures and offering investment incentives as the second and third pragmatic strategies, respectively. A sensitivity analysis validates the proposed framework's reliability and robustness. The study's findings emphasize the potential of this integrated framework to guide future research endeavors among scholars and academicians across diverse industries beyond agri-food processing.

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 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.

Assessing the risk and effect of climate change with two-layer fuzzy logic-SWARA: A comparative practice in Turkiye

Many factors such as population growth, development of industry/technology, and increase in production-consumption disrupt the ecological balance and cause climate change, which is a global problem. Determining the criteria that cause climate change is very important in finding effective solutions to the problem. In the study, the criteria were determined, weighted with a new method, Step-wise Weight Assessment Ratio Analysis (SWARA), and ranked according to their priorities with two-layer fuzzy logic model. The Fuzzy SWARA method allows the evaluation process, which becomes complicated due to the difficulties and factors experienced in decision-making, to be carried out more effectively and realistically. The risk and effect of climate change in Turkiye were evaluated regionally. However, the developed model also has a wide application area. Research findings revealed that the highest risk/effect of climate change have the Marmara and Central Anatolia regions. The lowest risk region is the Eastern Anatolia. Air pollution, population growth and deforestation have the highest weights. Important suggestions have presented especially for priority criteria. In this way, the factors that should be prioritized in climate change environmental problem solutions have been revealed and will make it easier for researchers and managers to provide more effective management.

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.

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.

Evaluating airports' Sustainable Development Goals by using multi-criteria decision making methodologies.

The recent growth of the aviation industry, which poses significant environmental challenges, has heightened the pressure on the sustainability of airports. Airport sustainability requires a holistic approach that encompasses economic, social, environmental, and operational aspects. In this regard, the United Nations' 17 Sustainable Development Goals (SDGs) Agenda provides a roadmap for the aviation industry. However, despite recognizing the importance of SDGs, aviation authorities and airports often fail to effectively integrate them into their activities and annual reports. This study aims to evaluate the significance of SDGs for airports and select the airport that prioritizes SDGs the most using Multi-Criteria Decision Making (MCDM) methodologies. This study introduces a novel approach that integrates Step-wise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods, which are MCDM techniques, to enhance airport sustainability. The SWARA method is employed to evaluate and assign weights to the SDGs in the context of airports. SDG 8 holds the highest level of significance among the goals concerning airports, while SDG 14 falls outside the scope of airport sustainability aspects. Then, five international airports that have been designated as green airports by aviation authorities and assessment organizations are selected, and the optimal alternative is determined using the WASPAS method, considering the weights obtained through SWARA. Dallas/Fort Worth International Airport is the top choice due to its successful implementations and reports aligning with the SDGs.

GIS-Enabled Multi-Criteria Assessment for Hospital Site Suitability: A Case Study of Tehran

In developing countries, the interaction between rapid urban expansion and population growth brings forth a host of challenges, particularly concerning essential services like healthcare. While interest in site suitability analysis for identifying optimal hospital locations to ensure equitable and secure healthcare access is on the rise, the absence of a holistic study that encompasses social and environmental aspects in the assessment of hospital site suitability is evident. The objective of this research is to introduce a hybrid methodology that combines Geographic Information Systems (GIS) with Multi-Criteria Decision-Making (MCDM) weighting methods. This methodology aims to create hospital site suitability maps for districts 21 and 22 in Tehran, taking into account socio-environmental factors. In addition to the conventional Analytical Hierarchical Process (AHP) weighting method, this study employs two relatively less-explored methods, the Best-Worst Method (BWM) and Step-wise Weight Assessment Ratio Analysis (SWARA), to enhance the analysis of hospital site suitability. In the SWARA method, there are minimal variations in weights among criteria, indicating that all socio-environmental factors (e.g., distance from existing hospitals, distance from main roads, distance from green spaces) hold significant importance in the decision-making process. Additionally, the findings indicate that the western part of the study area is the most suitable location for the construction of a new hospital. To achieve the average hospital bed availability in Tehran, an additional 2206 beds are required in the studied area, in addition to the existing facilities. Considering the ongoing urban development, population growth, and the potential for natural disasters and epidemics, it becomes essential to enhance the healthcare system by increasing the number of hospitals and available hospital beds. The sensitivity analysis showed that GIS-based SWARA-WLC was the most suitable and stable model for determining hospital site suitability in the study area. This methodology can be adapted for use in other regions and further improved by incorporating additional criteria. In conclusion, the study recommended three specific alternative sites for establishing a new hospital in the study area.

An efficient Method for Evaluation of Unmanned Aerial Vehicles: A Case Study in Livestock

The increasing popularity and ease of use of unmanned aerial vehicles (UAVs) have made them a thriving technology in various fields. One of their applications is in the animal revolution, where they are used to count, monitor, and detect livestock accurately, contributing to the development of the animal revolution. However, with so many types of UAVs available, it can be challenging to decide which one is most appropriate for the task at hand due to their varying characteristics. To address this issue, the suggested model is constructed utilizing neutrosophic sets to effectively manage uncertainty and deal with multi-criteria decision-making (MCDM) situations with several conflicting criteria and options. The proposed model integrates Stepwise Weight Assessment Ratio Analysis (SWARA) and the ARAS methods for evaluating the performance of UAVs in livestock based on diverse criteria and their importance, along with single-valued neutrosophic sets (SVNSs). The SWARA method is used for calculating the weight of criteria, and the ARAS method is used for ranking alternatives. An experimental case study has been established for choosing the best UAV to detect livestock using the data extracted from the thermal and multispectral UAV images.

Multi-Criteria Selection of Electric Delivery Vehicles Using Fuzzy–Rough Methods

Urban logistics implementation causes environmental pollution; therefore, it is necessary to consider the impact on the environment when carrying out such logistics. Electric vehicles are alternative vehicles that reduce the impact on the environment. For this reason, this study investigated which electric vehicle has the best indicators for urban logistics. An innovative approach when selecting such vehicles is the application of a fuzzy–rough method based on expert decision making, whereby the decision-making process is adapted to the decision makers. In this case, two methods of multi-criteria decision making (MCDM) were used: SWARA (stepwise weight assessment ratio analysis) and MARCOS (measurement alternatives and ranking according to compromise solution). By applying the fuzzy–rough approach, uncertainty is included when making a decision, and it is possible to use linguistic values. The results obtained by the fuzzy–rough SWARA method showed that the range and price of electric vehicles have the greatest influence on the selection of an electric delivery vehicle. The results of applying the fuzzy–rough MARCOS method indicated that the Kangoo E-Tech Electric vehicle has the best characteristics according to experts’ estimates. These results were confirmed by validation and the application of sensitivity analysis. In urban logistics, the selection of an electric delivery vehicle helps to reduce the impact on the environment. By applying the fuzzy–rough approach, the decision-making problem is adjusted to the preferences of the decision makers who play a major role in purchasing a vehicle.

Assessment of Mountain Tourism Sustainability Using Integrated Fuzzy MCDM Model

The sustainable development of mountain tourism is crucial for preserving the delicate ecosystems and resources found in these unique landscapes. This research paper investigates the sustainability of mountain lodges, which serve as essential facilities for delivering mountain tourism services. To assess sustainability, expert decision making involving eight selected experts was employed. A hybrid approach combining the IMF SWARA (IMproved Fuzzy Step-wise Weight Assessment Ratio Analysis) method with Fuzzy Dombi Aggregation Operators was utilized to determine the weights of various sustainability criteria. The IMF SWARA method assigned initial weights based on expert input, which were subsequently adjusted using Fuzzy Dombi Aggregation Operators. The findings highlight the significance of two key criteria as per expert evaluations: the quality of the services offered (C21) and the preservation of natural resources (C15). To rank and evaluate the mountain lodges, the fuzzy CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) method was employed, ultimately identifying Zabrana (ML6) as the top-ranked mountain lodge. The validity of these results was confirmed through result validation and sensitivity analysis. This research contributes by providing insights into the current state of mountain tourism and offering guidelines for enhancing the overall mountain tourism experience through the integration of fuzzy methods.

CHALLENGES AND OPPORTUNITIES FOR ACHIEVING OPERATIONAL SUSTAINABILITY OF BOILERS IN THE CONTEXT OF INDUSTRY 4.0

This research aims to explore the challenges and opportunities for achieving operational sustainability of boilers in the context of Industry 4.0, using the Stepwise Weight Assessment Ratio Analysis (SWARA) method. The integration of Industry 4.0 technologies has the potential to revolutionize the way boilers are operated, offering benefits such as improved energy efficiency, reduced emissions, and enhanced safety. However, effective implementation of these technologies requires addressing the associated challenges and opportunities. This study ranks the challenges and opportunities based on expert feedback using the SWARA method, highlighting the most pressing issues and areas for improvement. By the feedback from 15 industrial and academic experts, the results suggest that 'workforce transformation' and 'data management and integration' are the leading challenges, while 'improved energy efficiency' and 'enhanced safety' are the most significant opportunities for achieving operational sustainability of boilers in the context of Industry 4.0. This research can inform and guide the development of policies, strategies, and solutions aimed at improving the sustainability of boilers in a rapidly changing technological landscape, and assist industrial managers in finding strategic management for sustainable boiler operation.

Performance Analysis of Manufacturing Waste Using SWARA and VIKOR Methods: Evaluation of Turkey within the Scope of the Circular Economy

The increasing population and industrial developments driven by growing needs and expectations have led to an increase in consumption. The rise in consumption, in turn, results in more waste generation. The management of waste has become a global issue concerning human and environmental health. As a solution to climate change, waste, and biodiversity loss, the concept of the circular economy has emerged, which involves a global effort. Zero waste, which is one of the key elements of the circular economy, is regulated by waste management legislation in the European Union in accordance with the waste management hierarchy. Therefore, waste management is an important and urgent issue that requires significant planning, especially for countries with trade relations with the European Union. This study aims to evaluate the performance of waste management in Turkey’s manufacturing industry within the scope of the circular economy. The SWARA (Step-wise Weight Assessment Ratio Analysis) and VIKOR (VIseKriterijumsa Optimizacija I Kompromisno Resenje) multi-criteria decision-making methods were used in the research. The examination of manufacturing waste in conjunction with the waste hierarchy and within the scope of the circular economy using multi-criteria decision-making methods sets this study apart from other research on the subject. The analysis results indicate that Turkey, particularly in the preference for the option of selling, has shown an increasing trend in waste reduction, reuse, and recycling indicators, while showing a decreasing trend in disposal. In this context, it can be said that Turkey will not face difficulties in the process of aligning with the European Green Deal, and positive environmental developments have been observed.

A Fuzzy–Rough MCDM Approach for Selecting Green Suppliers in the Furniture Manufacturing Industry: A Case Study of Eco-Friendly Material Production

Green supplier selection is always one of the most important challenges in all of supply chain management, especially for production companies. The purpose is to have reliable suppliers which can fulfill all requests and be flexible in any supply chain stage. The aim of this paper is to create an adequate and strong MCDM (multicriteria decision making) model for the evaluation and selection of suppliers in a real environment. The main contribution of this study is proposing a novel fuzzy–rough MCDM model containing extension stepwise weight assessment ratio analysis (SWARA) and additive ratio assessment (ARAS) methods with fuzzy–rough numbers (FRN). The integrated FRN SWARA–FRN ARAS model was implemented in a case study of eco-friendly material production. The FRN SWARA method was used to calculate the weights of 10 green criteria, while using FRN ARAS, 6 suppliers were evaluated. The results of the applied model show that supplier S3 received the highest ranking, followed by supplier S2, while supplier S5 performed the poorest. In order to verify the strengths of the developed fuzzy–rough approach, we created a comparative analysis, sensitivity analysis, and dynamic matrix, which confirm the robustness of our model.

Environmental Monitoring and Assessment for Landfill Site Selection Using GIS-Based SWARA and Rapid Impact Assessment Matrix

Landfill site selection is a complex decision-making process which requires the evaluation of various environmental, social, and economic criteria. The present research aims to propose a GIS-based Stepwise Weight Assessment Ratio Analysis (SWARA) method for landfill site selection in the east of Lorestan province, Iran. The study used 11 criteria including land use, distance from protected areas, slope, elevation, distance from the water resource, distance from the road, geology, distance from the fault, soil texture, distance from the city, and distance from the village. The results show that the proposed method can be used as a reliable tool for landfill site selection. The method provides decision-makers with a systematic approach to evaluate and rank potential sites based on their suitability. The study found that distance from water resources and soil texture were the most important criteria for landfill site selection in the study area. The proposed method can help decision-makers to identify suitable sites for landfill development considering potential environmental impacts. The study highlights the importance of using the GIS-based SWARA technique for landfill site selection, as it provides a comprehensive and efficient approach to decision-making. The proposed GIS-based SWARA method can be used as a valuable tool for landfill site selection in other regions and can help in achieving sustainable development goals.

Modeling enablers of supply chain decarbonisation to achieve zero carbon emissions: an environment, social and governance (ESG) perspective.

Climate change is the greatest threat to our planet and way of life. There is an immediate need of decarbonisation and a push for a smooth transition to a world with no net carbon emissions. To achieve sustainability, fast-moving consumer goods (FMCG) firms are increasing their efforts to reduce their carbon footprint across their supply chains. The firms and government undertake several initiatives towards achieving the zero carbon mission. Hence, there is a need to identify the prominent enablers that may enhance the decarbonisation in the FMCG sector and contribute towards a net-zero carbon economy. The current study has identified and assessed the enablers (6 main criteria, 19 sub-criteria), including green innovation, green supply chain, sustainable decision-making, organisation decisions and government control from environment, social and governance (ESG) perspective. Eco-friendly manufacturing techniques and eco-friendly goods may give businesses a competitive edge and sustainability. The six primary factors that can help to reduce decarbonisation are evaluated using the stepwise weight assessment ratio analysis (SWARA) technique. The criteria and sub-criteria are assessed by SWARA method. A total of 32 experts from the FMCG industry are undertaken to validate and assess the enablers. This study identified and evaluated the ESG-based decarbonisation enablers in FMCG. The study shows that green innovations are ranked first, followed by organisational decisions and government control. This is likely the first study to examine how the FMCG industry's enablers for decreasing carbon footprints connect. The study is helpful to the supply chain managers and the other decision makers to implement well-designed processes for creating new products and a supply chain from the point of purchase to the place of supply, all backed by advanced technology and the appropriate regulatory adjustments.

Identification and Prioritization of the Factors Influencing Service Quality in the Hotel Industry by SWARA and ARAS Methods During the COVID-19 Pandemic

ABSTRACT Nowadays, the COVID-19 pandemic has had many harmful effects on the hotel industry. This study aims to identify and prioritize the factors affecting SERVQUAL in the hotel industry and evaluates five-star hotels during the COVID-19 Pandemic. The present study prioritizes the influence factors of SERVQUAL using the stepwise weight assessment ratio analysis (SWARA) method and using the Additive Ratio Assessment (ARAS) method to identify the best hotel among the five hotels in Iran in 2022. In the present study, 22 influence sub-criteria in the hotel industry were studied, and the delivery of service promise sub-criterion was ranked first, which shows its importance among other sub-criteria. Among the hotels studied, A1 hotels ranked first among other hotels. The present study examines and prioritizes the influence factors of SERVQUAL in the hotel industry to help hotel managers to identify the most important influence factors of SERVQUAL in the hotel industry to implement these factors in their hotels and can improve their strengths.

Improving supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development

PurposeThe purpose of this study is to improve supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development. During the COVID-19 pandemic, the organizations have struggled a lot to maintain the supplier performance and strategic sourcing decisions in the organizational benefit. However, in this context, the organization’s agile new product development (ANPD) process must be aligned with this requirement by maintaining the inventory and jobshop scheduling. As a result, identifying ANPD indicators, performance metrics and developing a structural framework to guide practitioners at various stages for smooth adoption is essential to improve the overall performance.Design/methodology/approachA comprehensive literature review is conducted to identify jobshop scheduling, inventory management and ANPD indicators along with the performance metrics, and the hierarchical structure is developed with the help of expert opinion. The modified stepwise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assurance (WASPAS) techniques, along with expert judgement, are used in this study to calculate the weights of the indicators and the ranking of the performance metrics.FindingsAs per the weight computation by SWARA method, the strategy indicators have the highest relative weight, followed by the product design indicators, management indicators, technical indicators, supply chain indicators and organization culture indicators. According to the ranking of performance metrics obtained through WASPAS, the “frequency of new product development is at the top”, followed by “advances in product design and development” and “estimated versus actual time to market”.Research limitations/implicationsIt is believed that the framework developed will help industrial practitioners to plan effectively to improve supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.Practical implicationsThe outcomes of the present study will be extremely beneficial for the industry practitioners to improve the supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.Originality/valueA unique combination of modified SWARA–WASPAS technique has been used in this study which would be beneficial for organizations willing to adopt the jobshop scheduling and inventory management and ANPD for improving supply chain performance.

Hybrid MCDM method on pythagorean fuzzy set and its application

Here in this article, a hybrid MCDM method on the Pythagorean fuzzy-environment is presented. This method is based on the Pythagorean Fuzzy Method based on Removal Effects of Criterion (PF-MEREC) and Stepwise Weight Assessment Ratio Analysis (SWARA) approaches. Here, the objective and subjective weights are assessed by PF-MEREC, SWARA model and the preference order ranking of the various alternatives is done through Complex Proportional Assessment (COPRAS) framework on the Pythagorean fuzzy set (PFS). The proposed method is the hybrid model of MEREC, SWARA and COPRAS methods. Further, the proposed model is used to identify the best banking management software (BMS) so that the bank can choose the robust bank management software tool to enhance its efficiency and excellence. Thereafter, a comparative discussion and sensitivity analysis of the proposed model is done with the existing techniques to judge the reasonability and efficiency of the proposed model.

Identification of suitable sites for electric vehicle charging stations; a geographical information systems based multi criteria decision making approach

ABSTRACT The zero emission characteristics of Electric Vehicles (EVs) play an important role in both reducing carbon emissions and creating an alternative to fossil-based fuels. Battery capacity and availability of EV charging stations are the most important criteria for EVs to become widespread. This study focuses on using location-based criteria to determine suitable locations for EV charging stations. Six criteria were determined based on academic literature and expert opinions. Euclidean distance and kernel density analysis for the criteria were determined from Geographical Information Systems (GIS), and the weights for the criteria were determined using Step-Wise Weight Assessment Ratio Analysis (SWARA). C2 was found to be the most significant criterion with a weight of 0.316 and C5 the least important with a weight of 0.088. The six criteria maps were combined as a weighted map and 21 potential locations within the range of 0–0.175 pixel values were identified in the study area. The locations were ranked using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to determine the most suitable sites. According to the pixel values of the criteria maps, CS5 determined as the most suitable site, followed by CS2 and CS4. This study presents a new perspective for the selection of suitable sites for EV charging stations, using a GIS based SWARA and TOPSIS methods.