Step-wise Weight Assessment Ratio Analysis Research Articles

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.

People-centric variables in the fourth industrial revolution: an application of SWARA methodology

PurposeThe accelerating business transformation through Industry 4.0 (I4.0) is expected to create significant value in the manufacturing industry by delivering considerably high productivity, superior quality, better efficiency and effectiveness. However, its evolutionary processes have far-reaching challenging for humanity. This has triggered a need to analyze the impact of I4.0 on various people-centric variables (PCVs).Design/methodology/approachThis paper attempts to analyze the interrelationship dynamics between the PCVs in the current digital-industry ecosystem using a focus-group approach and causal loop diagrams. Application of the SWARA (stepwise weight assessment ratio analysis) methodology has provided its prioritized ranking in terms of importance.FindingsThe study has highlighted that I4.0 has a significant influence on five of the 13 PCVs – human quality of life, digital dexterity, high-skilled talent, low-skilled employment and creativity which contribute to 80% of the total impact.Originality/valueThe prioritized weights of the human factors from the SWARA approach have facilitated the assessment of the Human Resource Development Index (HRDI). The study is also contributing in enriching the literature on the human impact of the growing I4.0 and triggered the researchers to study further its adverse impact on critical human factors.Key pointsThe paper pertains to debates on a very critical issue of impact of integration of the current intelligent digital technologies in manufacturing and services to transform businesses to be more flexible and agile.This paper features I4.0 as a technology that allows integration of new products in the existing production lines, one-off manufacturing runs and high mix manufacturing.The paper also highlights major adjustments in operational activities, processes, supply chain, and organizational redesign due to I4.0 adoption.The current research study has significantly enriched the literature on the I4.0 impact on people-centric variables (PCVs) using the SWARA method. The use of the Causal Loop Diagram has very aptly brought out the type of causality (polarity) between the different PCVs in the growth of I4.0.

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.

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.

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.

Barriers to Entrepreneurial Refugees’ Integration into Host Countries: A Case of Afghan Refugees

Pakistan hosts over 1.4 million Afghan refugees and is facing extreme challenges in accomplishing the UN’s refugee pacts and 2030 Sustainable Development Goals (SDGs). The inflow and longer stay of refugees in the host country may affect the local population’s socio-economic conditions. However, not all refugees are a “burden” to the host economy. Some refugees can contribute positively to the local economy given the opportunity. This study investigates the leading hurdles to establishing businesses for refugees to provide a different perspective to policymakers and scholars in achieving refugee integration. Through a thematic analysis of interviews conducted with Afghan entrepreneurial refugees, this study identifies ten hurdles and five opportunities they face while conducting business in Pakistan. Fuzzy Step-wise Weight Assessment Ratio Analysis (FSWARA) prioritizes the central theme, i.e., a lack of policies, among other hurdles, by allocating weights. Hypotheses on hurdles and opportunities are built and tested through multiple regression analysis (MRA). All the hypotheses on hurdles and three on opportunities are accepted. This study highlights the importance of a comprehensive framework for entrepreneurial refugees for their smooth integration into Pakistani society. This study helps policymakers and scholars identify the main barriers for refugee entrepreneurs in Pakistan.

Strategic Warehouse Location Selection in Business Logistics: A Novel Approach Using IMF SWARA–MARCOS—A Case Study of a Serbian Logistics Service Provider

Business logistics encompasses the intricate planning, seamless implementation, and precise control of the efficient and effective movement and storage of goods, services, and associated information from their origin to their final consumption point. The strategic placement of facilities is intricately intertwined with business logistics, exerting a direct influence on the efficiency and cost-effectiveness of supply chain operations. In the realm of business logistics, decisions regarding the location of facilities, including warehouses, distribution centers, and manufacturing plants, assume a pivotal role in shaping the overarching logistics strategy. Warehouses, serving as pivotal nodes in the supply chain network, establish crucial links at both local and global markets. They serve as the nexus connecting suppliers and customers across the entire supply chain, thus constituting indispensable elements that significantly impact the overall performance of the supply chain. The optimal location of warehouses is paramount for efficient supply chains, ensuring minimized costs and bigger profits. The decision on warehouse location exerts a profound influence on investment costs, operational expenses, and the distribution strategy of a company, thereby playing a substantial role in elevating customer service levels. Hence, the primary objective of this paper is to propose a novel methodology grounded in the application of the Improved Fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA)-Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) methods for determining warehouse locations tailored to a logistics service provider (LSP) operating in the Serbian market. Through the definition of seven evaluation criteria based on a comprehensive literature review and expert insights, this study aims to assess five potential locations. The findings suggest that the proposed model offers great decision support for effectively addressing challenges akin to the one presented in this study.

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 integrated quantum picture fuzzy rough sets with golden cuts for evaluating carbon footprint-based investment decision policies of sustainable industries

The purpose of this study is to make evaluation related to the significant determinants of the effectiveness of the carbon footprint-based investments while constructing a novel decision-making model. At the first stage, selected five determinants are evaluated with multi stepwise weight assessment ratio analysis (M-SWARA) methodology based on quantum picture fuzzy rough sets. In the second part, sustainable industry alternatives are ranked by quantum picture fuzzy rough sets extended multi-objective optimization on the basis of ratio analysis (MOORA) technique. Similarly, elimination and choice translating reality (ELECTRE) approach is also taken into consideration to make a comparative evaluation. The main contribution of this study is that a novel methodology is proposed by integrated picture fuzzy row sets and quantum theory. While using the combination of rough sets and picture fuzzy logic, uncertain data in the complex process can be evaluated in a more effective manner. Moreover, due to the criticisms to stepwise weight assessment ratio analysis (SWARA) methodology by not considering causal relationship of the determinants, this methodology is extended with the help of some improvements so that a new approach (M-SWARA) is proposed to overcome this deficiency by creating impact direction map of the items. The ranking results of these two techniques are the same that indicates the coherency of the findings. It is concluded that carbon-free project financing with green bonds is the most important indicator for this situation. On the other side, the ranking results demonstrate that renewable energy investment is the most appropriate sustainable industry alternative. Considering the results obtained in this study, the development of green bonds should be given priority. Establishing an international certification system is important in terms of clearly defining green bonds. Government supports are also of critical importance in the development of green bonds. Tax reductions provided by governments can increase the profitability of green bonds. This may contribute to investors showing more interest in green bonds.

Ranking of Technologies for Intralogistic Bulk Material Handling Processes Using Fuzzy Step-Wise Weight Assessment Ratio Analysis and Axial-Distance-Based Aggregated Measurement Methods

The logistics network is considered the provider of logistics activities in supply chains. The fluctuating requirements of customers and the logistics network’s complex structure are only a few of the factors that cause challenges to its management. Industrial facilities are particularly vulnerable to challenges because material handling operations dominate in addition to manufacturing activities. Disruptions at industrial plants are disseminated through the logistics network, affecting all supply chain participants. As a result, reducing material handling time and costs to decrease material losses, pollution, and productivity is vital to their business. Due to their distinctive properties and significant share in finished goods, bulk materials are particularly vulnerable to issues during manufacturing. Accordingly, this study aims to rank and select technologies for handling bulk materials in an industrial plant where the production of construction materials is performed. This paper proposes four alternative solutions for the observed case study, and nine criteria were selected for the evaluation. A new hybrid multi-criteria decision-making model is proposed. The model combines Fuzzy Step-Wise Weight Assessment Ratio Analysis (SWARA), used to determine the weight of criteria, and the Axial-Distance-Based Aggregated Measurement (ADAM) method, used to rank alternative solutions. The model results indicate that the pneumatic conveyor is the best ranked alternative that significantly increases productivity, reduces losses, and improves working conditions. The key contributions of this study are its analysis of the efficiency of the technologies proposed for bulk material handling and the development and implementation of a model framework for the ranking of these technologies.

A heterogeneous picture fuzzy SWARA-MARCOS evaluation framework based on a novel cross-entropy measure

Multi-criteria decision-making (MCDM) entails a heterogeneous decision-making problem, which poses challenges for decision-makers (DMs) in generating an optimal solution. To address this, we have proposed a heterogeneous evaluation framework. First, a novel picture fuzzy cross-entropy measure was defined with the simultaneous consideration of uncertainty and hesitancy of picture fuzzy information, overcoming the shortcomings of the existing cross-entropy measure in relation to its validity and properties. Next, an optimisation-model for determining the objective weights of criteria was constructed based on the proposed closeness measurement and the step-wise weight assessment ratio analysis (SWARA) method. This model was constituted from both objective and subjective perspectives under the circumstance of completely unknown criterion information. Additionally, the conventional Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method was extended to the picture fuzzy environment, and a normalisation method was developed to transform heterogeneous judgments, including linguistic terms, interval numbers, and picture fuzzy numbers, into a unified representation form. A heterogeneous picture fuzzy SWARA-MARCOS evaluation framework was then established and used to solve a military equipment supplier selection problem. The results demonstrated the validity and feasibility of the proposed evaluation framework, while sensitivity, comparative, and complexity analyses demonstrated the robustness and superiority of it.

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.

Exploring the criteria for a green and smart hotel: insights from hotel managers' perspectives

PurposeThis study aimed to explore the adaptations and perspectives of hotel managers regarding green and smart hotel technologies within the context of sustainability.Design/methodology/approachA comprehensive literature review guided the formulation of this study, followed by face-to-face, semi-structured interviews with hotel managers. A total of 17 prepared questions were finalized after examination by two expert academicians. The responses were analyzed using qualitative research methodology and the results were weighted using the step-wise weight assessment ratio analysis (SWARA) method.FindingsInterviews with sustainability and operational managers yielded insights into environmentally friendly practices and strategies such as reducing energy and water consumption, waste and chemical reduction, supporting local entrepreneurs and adopting smart technologies. These factors are crucial in eco-friendly hotels. According to the SWARA analysis, 'reducing energy consumption' is the most effective criterion.Research limitations/implicationsThis study offers insights into green and smart hotel management by focusing on the perspectives of hotel managers with a small sample. In future studies, research with larger samples on customer perspectives and the effect of hotel selection is recommended.Practical implicationsThis study offers insights to hotel managers on energy conservation and customer satisfaction enhancement through green and technological applications. These technological applications can improve hotel service quality and provide personalized experiences, fostering customer loyalty.Originality/valueThis pioneering study focuses on the intersection of green and smart practices in hospitality. By intertwining the often separately discussed concepts of “green” and “smart,” this study presents a novel approach to the sustainability practices in the hospitality industry, holding a key position, especially in Turkey. Implementing these concepts can yield environmental and economic benefits, offering invaluable insights to hotel managers and policymakers into integrating smart technologies with sustainability.