Best-worst Method Research Articles

Analyzing Coastal Vulnerability Using Analytic Hierarchy Process and Best–Worst Method: A Case Study of the Marmara Gulf Region

AbstractCoastal regions, integral to human economic and social frameworks, face increasing threats from disasters and climate change. This situation has made it necessary and a priority to study these areas at an international level. To be able to take precautions, protect, and manage coastal areas, it is essential to identify their coastal vulnerability. In this study, a coastal vulnerability analysis was conducted using best–worst method (BWM) in the Marmara Gulf Region, Türkiye. Moreover, comparison was made with the analytic hierarchy process (AHP) method in the subject of assessment of coastal vulnerability. According to the vulnerability map obtained with AHP, 17% of the region was determined to have a moderate vulnerability risk, 54% had a high risk, and 29% had a very high risk. On the other hand, the analysis results in BWM calculated the vulnerability percentages as follows: 18% moderate vulnerability, 57% high vulnerability, and 25% very high vulnerability. Visual and statistical comparisons revealed that the BWM method provides more consistent results and involves fewer pairwise comparisons than the AHP method. Thus, it offers ease of use and convenience to decision maker while maintaining relatively same level of weights for criteria. This study aims to lay the foundation for a dynamic system designed to assess coastal vulnerability, emphasizing usability for policymakers in decision-making and flexibility across various scenarios. Particularly, the adoption of the BWM provides notable benefits due to its direct approach and ability to yield more uniform and dependable evaluations, thereby efficiently addressing intricate decision-making challenges.

Green, resilient, and inclusive supplier selection using enhanced BWM-TOPSIS with scenario-varying Z-numbers and reversed PageRank

Firms need a reliable decision-making framework to select suppliers who are Green, Resilient, and embrace Inclusive Development (GRID). However, GRID supplier selection is challenged by events that are uncertain and often dependent. Such events affect supplier rankings in a fuzzy decision environment. This paper builds a GRID-based criteria system and applies an enhanced Best Worst Method (BWM) and Technique for Order Preferencing using the Similarity to the Ideal Solution (TOPSIS), with Scenario-Varying Z-numbers (SVZs) and reversed PageRank. The decision criteria are extracted from the literature and confirmed by expert judgement. The SVZs and SVZ Preference Relations (SVZPRs) are developed by combining the Concept of Stratification Theory (CST) with Z-numbers, to reflect the impact of Dependent Uncertain Events (DUEs). Reverse PageRank is then used to find the occurrence probability of the scenarios due to DUEs. BWM is applied in an SVZPR environment to obtain the weights of the decision criteria. TOPSIS, incorporating SVZs, is used to rank the suppliers. An example is used to highlight the enhanced BWM-TOPSIS, to find the priority ranking of the GRID weights. Comparative and sensitivity analyses are conducted to validate the proposed method.

Analysing hindrances to digital disruption with a focus on cleaner cloud manufacturing

PurposeThis study aims to identify and analyse critical barriers to cloud manufacturing (CM) adoption for achieving sustainability amongst manufacturing micro, small and medium-sized enterprises (MSMEs) in an emerging economy.Design/methodology/approachInitially, through a literature survey and expert inputs, this study identified and finalized 21 critical barriers to CM adoption, which were further analysed by employing a hybrid multiple criteria decision-making (MCDM) approach. In this approach, the best–worst method (BWM) was used to rank the identified barriers. However, to analyse the interrelation amongst these barriers and classify them into the cause-effect group, the decision-making trial and evaluation laboratory (DEMATEL) method was employed. Furthermore, the study recommended various strategies that can help overcome high-priority barriers and enhance CM adoption within firms.FindingsThe results of the study show that organizational, legal and economic barriers are the high-priority barriers that need to be addressed to enhance CM adoption.Practical implicationsThe findings of the study will help managers and decision-makers in manufacturing MSMEs develop effective strategies and policies to enhance CM adoption.Originality/valueNotably, this investigation is one of the preliminary research activities to explore the sustainability aspect of CM adoption amongst MSMEs in an emerging economy.

An optimization-based design methodology to manage the sustainable biomass-to-biodiesel supply chain under disruptions: A case study

The detrimental effects of excessive fossil fuel consumption have recently become a major global concern. Moreover, fuel supply chains are vulnerable to disruptive and operational risks. This study proposes a robust-stochastic approach to design a resilient sustainable biodiesel supply chain from Jatropha and waste cooking oil, while facilities and transportation links are vulnerable to multiple site-dependent disruptions. The main contributions are implementing preventive resiliency strategies to manage risks, integrating sustainability and resiliency, and selecting biodiesel conversion technology based on byproducts’ demands. Herein, candidate locations are identified to cultivate Jatropha using the best-worst method and geographical information system. Moreover, the supply chain network is designed to optimize economic performance, network non-resiliency, and social impacts. The Monte Carlo simulation approach, k-means clustering, augmented epsilon constraint, and Mulvey robust optimization are adopted to generate and reduce probable scenarios, manage multiple objectives, and handle uncertainty, respectively. The results showed that the non-resilient supply chain experiences a 20.11 % additional cost in disruption scenarios; however, even with a higher initial cost, adopting resiliency strategies reduces disruption cost effects by 9.46 %. Managerial insights have demonstrated that resilience and sustainability are positively correlated in biofuel supply chain management, and rising biodiesel demand enhances social welfare and economic performance.

Comparative Prioritization of Sub-Watersheds in Flood Generation Using Co-Management Best-Worst Method and Game Theory Algorithm

Comparative Prioritization of Sub-Watersheds in Flood Generation Using Co-Management Best-Worst Method and Game Theory Algorithm

Identifying and prioritizing the barriers to green innovation in SMEs and the strategies to counteract the barriers: An interval-valued intuitionistic fuzzy approach

Increasing environmental burdens and the importance of innovation in the presently competitive environment have encouraged small- and mid-size enterprises (SMEs) to adopt and implement green innovation practices. This process, however, is faced with many limitations and obstacles. The purpose of this study is to propose a framework to identify barriers to green innovation in SMEs and to provide solutions/strategies to overcome such barriers. Using the systematic literature review (SLR) method and the opinions of 20 experts, the study primarily identified 29 sub-categories of barriers falling under seven main categories including “management”, “organization”, “human resources”, “financial resources”, “external cooperation”, “customers, markets or competitors”, and “the external environment.” Similarly, 22 strategies were discovered that could help to counteract the barriers identified. To determine the weights of the barriers, the Intuitionistic Fuzzy Multiplicative Best-Worst Method (IFMBWM) was used. The strategies, too, were ranked through the Interval-Valued Intuitionistic Fuzzy MULTIMOORA (IVIF-MULTIMOORA) method. Then, a sensitivity analysis and a comparative analysis were applied to confirm the robustness of the proposed method and ensure the consistency of the obtained ranking of alternatives. As the results revealed, the optimal and effective solutions that could be used by policy-makers/managers to overcome the barriers to green innovation were “simplifying authorization and licensing procedures”, “effective environmental laws and regulations”, “shared responsibility throughout the supply chain”, “a supportive organizational culture”, and “merit-based selection of managers.”

A BWM-TOPSIS Linear Programming Model for Evaluating the Performance of Health-Promoting Hospitals with McKinsey 7s Framework in Organizational Management

This research addresses the critical aspect of evaluating operational performance in health promotion hospitals, which play a vital role in providing medical services to local communities. The research proposes an integrated method for performance assessment, utilizing the Best-Worst Method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) linear programming model. Taking the case of health promotion hospitals in Maha Sarakham province, Thailand, and considering the McKinsey 7s framework's seven criteria, BWM is employed to determine the criteria weights. Subsequently, the TOPSIS linear programming model selects the ideal health promotion hospital based on these weights. The BWM analysis reveals criteria weights in the following order: system, staff, skill, style, structure, strategy, and shared value. The TOPSIS linear programming model identifies SH12 as the top-performing health promotion hospital with a closeness coefficient value of 0.8821. Additionally, a Spearman's rank correlation test validates this proposed method against the original TOPSIS approach, yielding a correlation value of 1.0. These findings provide valuable guidance for organizations, particularly in shaping strategic policies and resource allocation within medical service units, medical equipment, and personnel management in organizational settings. This study offers that the proposed method is simpler and will aid in the ongoing analysis of strengths and weaknesses in the improvement of organizations and development, helping organizations adapt to changes.

Identification of appropriate sites for solar-based green hydrogen production using a combination of density-based clustering, Best-Worst Method, and Spatial GIS

Hydrogen is a relatively new source of sustainable energy resources; presently, its usage ranges from direct energy generation in mixed-cycle thermal power plants to its usage in the internal combustion engine. If the generation of hydrogen is coupled with renewable energy resources, the use of hydrocarbons can be substantially reduced in both energy generation and transportation. In this paper, we conducted a national-level assessment to identify the hydrogen generation capacity of Pakistan. The methodology used in this study is a combination of spatial multi-criteria analysis and density-based clustering in a geographical information systems environment. Based on the analysis, the national clusters capable of producing green hydrogen were identified. The results shows that the hydrogen generation capacity for Pakistan is approximately 7 million tons per year using solar photovoltaic energy. The Surab-Gwadar alignment, N-25 national highway, and Quetta-to-D-I-Khan alignment offer potential hydrogen generation sites with water resources. Most of the identified sites for the production of hydrogen in the country were found to be near the national electricity transmission networks. The Best-Worst Method was further employed to provide policymakers with a road map for prioritizing the development of these capacities based on their proximity to the national networks, their terrain suitability, and their generation capacities.

Disaster management with cloud-based geographic information systems: site selection of landfill areas after Kahramanmaraş, Türkiye earthquake sequence

On February 6, 2023, the earthquakes centered on Kahramanmaraş-Pazarcık and Kahramanmaraş-Elbistan affected several provinces in the region and generated a vast amount of demolition waste. This study aims to develop a site selection model for the most suitable landfill areas after the earthquake using open-source Geographic Information Systems (GIS) software and the Best-Worst Method (BWM). First, the study identifies decision criteria with a Delphi expert panel. Next, it determines the weights using the BWM based on pairwise comparison. Spatial data of the determinants were obtained from open data portals, and proximity, overlay, and slope analyses were carried out for the disaster region. Combining the output data layer of each criterion, a suitability map of the landfills was produced. As a result, 36,2% (29,826 kms square) of the study area was classified as suitable and very suitable. In this study, a cloud GIS-based decision support platform was developed to act quickly and initiate recovery processes within the scope of disaster management of the destructive earthquake. The suitability map and potential sites were shared with the responsible teams in the disaster area so that the potential sites could be displayed on the map and the suitable areas could be navigated on appropriate routes. The findings of the study revealed that the use of open data and cloud GIS framework in the disaster management process enables quick, reliable, and cost-effective actions in the immediate aftermath of disasters.

Fostering second-life applications for electric vehicle batteries: A thorough exploration of barriers and solutions within the framework of sustainable energy and resource management

Electric vehicles (EVs) play a crucial role in achieving Sustainable Development Goals (SDGs), specifically focusing on Goal 7, which ensures access to Affordable and Clean Energy, and Goal 9, promoting advancements in Industry, Innovation, and Infrastructure. However, the growing stockpile of used EV Batteries (EVBs) presents challenges. These EVBs, however, hold potential for second-use applications, aligning with energy conversion, resource optimization, and sustainable energy systems. This study employs a rigorous multi-criteria approach to bridge the research-practice gap by systematically evaluates and prioritizes barriers hindering the widespread adoption of second-use EVBs. The study's overarching objectives are to overcome these barriers, aligning with broader sustainability goals, and to offer insights shaping policies addressing environmental, social, and economic impacts, in coherence with SDGs 8, 11, 12, and 13. The research unfolds through a three-phase approach involving literature reviews, field visits, and expert interviews for barrier identification, theoretical framework development through literature and Delphi studies, and prioritization using the Fuzzy Best-Worst Method (FBWM). Results indicate that addressing Cost and Technological Challenges is pivotal for successful second-use EVB adoption, with proposed solutions involving regulations, recycling, and awareness. The study underscores the circular economy perspective, connecting identified barriers to specific SDGs. The FBWM proves effective in prioritizing barriers, providing actionable insights for policymakers and stakeholders. This research contributes significantly to the existing literature by systematically evaluating second-use EVBs, addressing a crucial research gap, and offering valuable pathways for broad adoption.

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

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

Exploring the ship operation safety indicators of international ports in Taiwan

Port safety plays an important role in port operations. Ship size has become larger and the port environment has rapidly changed in recent years. Ship accidents occur in the port area due to the complex environment in the port area. To improve past decision analysis methods on port operation safety fields and reduce fatalities and financial loss for potential accidents, the novelty of this study is to construct a safety index of ports with the application of the Best Worst Method (BWM). Four dimensions and 14 indicators were summarized based on an intensive literature review. The BWM was implemented to prioritize the weights of dimensions and indicators. Based on 21 expert questionnaires, the results indicate that the ranking of dimensions is ‘human’, ‘ship facilities’, ‘port facilities’, and ‘documentation check’. Regarding the ranking of indicators, the top three are ‘fire-fighting and life-saving equipment’, ‘captain’, and ‘pilot’. Safety improvement strategies (e.g. revising inappropriate operational rules and strengthening human safety education and training) based on these research findings are provided. The merits of this paper are presenting a simpler questionnaire-filling method and overcoming the traditional complicated questionnaire survey process and research limitations (e.g. indicator independence problems in the Analytic Hierarchy Process, and the complexation of filling out a questionnaire in the Analytic Network Process). In addition, the findings can help decision-making for port management authorities, port practitioners, and shipping operators (shipowners) regarding policy implementations of port safety.

What makes a better hotel selection? A hybrid approach considering tourists’ preference and review helpfulness

ABSTRACT Hotel selection is crucial in planning trips. Several studies constructed the decision methods, which assumed that people are completely rational and neglected decision-maker’s (DM’s) behavioural aspects, based on text reviews or numerical ratings with the subjective or objective weights of criteria. Therefore, this study proposes a picture fuzzy gained and lost dominance score (PF-GLDS) method based on prospect theory, which integrates user-generated text reviews and multi-criteria ratings to facilitate hotel selection while considering tourists’ preference and review helpfulness. First, review helpfulness is analyzed to enhance data quality. Second, a decision matrix is constructed by simultaneously considering sentiment in helpful text reviews as well as multi-criteria ratings. Third, the criteria are derived by the maximising deviation and best worst methods (BWM), respectively, for tourists’ preferred objective and subjective weights. Finally, to address hotel selection problems, an extended GLDS method with picture fuzzy information, considering bounded rationality behaviour, is developed. Empirical evaluation on five hotels from the TripAdvisor website demonstrates the reasonableness and advantages of our proposed hybrid approach in achieving more objective and reliable decision making. Concurrently, this study can serve as a reference for tourists seeking well-suited hotels and tourism website optimisation.

An [formula omitted]-cut intervals based Fuzzy Best–Worst Method for Multi-Criteria Decision-Making

To address ambiguity arising from linguistic decision judgments, Guo and Zhao introduced a model of the well-known Multi-Criteria Decision-Making (MCDM) method, Best-Worst Method (BWM), using fuzzy sets, known as Fuzzy Best–Worst Method (FBWM). Despite its versatility, FBWM has limitations, such as criteria-weights being independent of fuzzy comparison values’ shape and reliance on approximate fuzzy arithmetic operations. In response to these limitations, we introduce a novel model of Fuzzy Best–Worst Method based on α-cut intervals. In this model, the calculation of optimal weights involves formulating an optimization problem that utilizes exact fuzzy arithmetic operations defined in terms of α-cut intervals. This approach aims to optimize the entire shape of fuzzy comparison values simultaneously. However, it turns out that, although we have proven the existence of optimal solution for this problem, solving it proves to be challenging. To resolve this issue, we approximate optimal weight set(s) using triangular fuzzy weight sets and assess the Error of Approximation (EA). The approximate weight set with the desired EA is then defuzzified to obtain a crisp weight set. In situations where there are multiple approximate weight sets with the same EA, interval-weights are calculated. Following that, we establish the necessary conditions for a Fuzzy Pairwise Comparison System (FPCS) to be consistent. Using these conditions, we calculate a lower bound of the Consistency Index (CI), providing a measure of the accuracy of weights. Finally, we discuss numerical examples and a real-world scenario, ranking of risk factors in supply chain 4.0, to validate the effectiveness of the proposed model.

BWM Integrated VIKOR method using Neutrosophic fuzzy sets for cybersecurity risk assessment of connected and autonomous vehicles

Connected and autonomous vehicles (CAVs) have gained significant importance in intelligent transportation systems. However, CAVs are associated with significant cyber-risks caused by different threat-agents, which require robust mitigation strategies for secure operation. Different categories of threat-agents come with varying resources, motivation, and skills that must be addressed effectively for the secure operation of CAVs. This study offers a comprehensive analysis of cybersecurity risks from different categories of threat-agents targeting CAVs. A novel hybrid multi-criteria decision making (MCDM) technique has been developed to prioritize the threat-agent categories as part of the cyber defense mechanism. In this study, the major threat-agent categories are identified from the existing literature and cybersecurity reports. Then expert opinion is collected on the likelihood and severity of cyber-attacks undertaken by different threat-agent categories. Single-valued neutrosophic fuzzy sets (SVNFS) are implemented to consolidate the subjectivity of the linguistic opinions given by the experts. Then Best-Worst Method (BWM) is used to determine the relative criteria weightage on the cyber-attack consequences. These weights are then supplied to the ViseKriterijum-ska Optimizacija I Kompromisno Resenj (VIKOR) method for ranking the categories of threat-agents based on the risk perceived by the experts. A case study is presented with five categories of threat-agents and five loss criteria targeting CAVs to validate the viability of the proposed model. The result shows that insider attackers pose maximum risk for cybersecurity of CAVs. The proposed technique will provide helpful insights to the decision-makers for formulating effective defense strategies against cyber-attackers.

Machine learning modeling of base flow generation potential: A case study of the combined application of BWM and Fallback bargaining algorithm

The study of base flow is essential for sustainable water management. By understanding the dynamics of base flow, policymakers can make informed decisions to ensure the long-term health and availability of water resources. This study was conducted with the aim of modeling the spatial changes of Base Flow Generation Potential (BFGP) using integrated Machine Learning Algorithms (MLAs) including Decision Tree Regression (DTR), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Random Forest (RF), Simple Linear Regression (SLR), Support Vector Machine (SVM) and Support Vector Regression (SVR) and optimal Multi-Criteria Decision Making (MCDM) methods including Best-Worst Method (BWM) and Fallback bargaining in the Cheshmeh-Kileh watershed, Iran. BFGP conditioning factors were selected for the Sub-Watershed (SW) and weighed using MCDM (BWM and Fallback bargaining algorithm) methods. BFGP maps of five classes were generated and analyzed. MCDM methods were also integrated with ML algorithms for optimum performance evaluation of the integrated unit. Based on the results, in the integrated model of BWM and Fallback bargaining algorithm with ML algorithms, SVR and RF algorithms was selected as the optimal model, respectively. In BWM in the Sehezar River, the upstream had the highest BFGP. The pattern of spatial changes based on the Fallback bargaining algorithm in the upstream of Sehezar River was similar to BWM. The combined approach of RF-GTA with the factors of minimum temperature (Tn), NDSI and elevation showed the highest correlation. It was observed that that the Fallback bargaining algorithm and BWM of the MCDM exhibited similarity in output of regarding the spatial change patterns of the BFGP. Also, Fallback bargaining exhibited a 67% similarity with the integrated RF-GTA in BFGP prioritization of the SWs, while BWM performed poorly. This suggests that the Fallback bargaining algorithm was more effective than BWM of the MCDM methods for SWs BFGP prioritization. The general summary indicated that the combined use of MCDM and MLAs were valuable tools in BF studies and helped in better understanding the complex hydrological processes. The findings of our research may be of help to Iran Water Resources Research Company using hydrological data arising from base flow studies to support their analysis. In conclusion, understanding the BF characteristics of SWs allows watershed managers to effectively prioritize conservation and restoration efforts aimed at protecting and enhancing the natural flow regime.

The Future of Energy Storage in Vietnam: A Fuzzy Multi-Criteria Decision-Making Approach to Metal-Ion Battery Assessments

Lithium-ion (Li-ion) batteries, despite their prevalence, face issues of resource scarcity and environmental concerns, prompting the search for alternative technologies. This study addresses the need to assess and identify viable metal-ion battery alternatives to Li-ion batteries, focusing on the rapidly industrializing context of Vietnam. It acknowledges the criticality of developing a sustainable, cost-effective, and resource-efficient energy storage solution that aligns with the country’s growth trajectory. The primary objective is to evaluate the suitability of emerging metal-ion batteries—specifically sodium-ion (SIB), sodium-ion saltwater (SIB-S), magnesium-ion (MIB), and zinc-ion (ZIB)—for Vietnam’s energy storage needs, guiding future investment and policy decisions. A Fuzzy Multiple-Criteria Decision-Making (MCDM) approach is employed, incorporating both quantitative and qualitative criteria. This study utilizes the Fuzzy Best-Worst Method (BWM) to determine the relative importance of various performance indicators and then applies the Bonferroni Fuzzy Combined Compromise Solution (Bonferroni FCoCoSo) method to rank the battery alternatives. The SIBs emerged as the most promising alternative, scoring the highest in the overall evaluation. The MIBs and SIB-saltwater batteries displayed competitive potential, while the ZIBs ranked the lowest among the considered options. This research provides a strategic framework for energy policy formulation and investment prioritization. It contributes to the field by applying a fuzzy-based MCDM approach in a novel context and offers a structured comparative analysis of metal-ion batteries, enhancing the body of knowledge on sustainable energy storage technologies.

A biocompatible hybrid approach for the selection of gas drainage methods in coal mines based on the Best-Worst Method and Analytic Network Process

A biocompatible hybrid approach for the selection of gas drainage methods in coal mines based on the Best-Worst Method and Analytic Network Process

Building a Hierarchical Enablers Framework for Service Business Model Innovation for Sustainable Performance: Evidence from Ghana’s Electricity Sector

Selecting an appropriate business model innovation for sustainable performance is a complex decision that requires a decision support tool. However, despite the importance of business model innovation (BMI) for sustainable performance, there has been limited investigation into how a hierarchical enabler framework grounded in service-dominant logic contributes to the sustainability of service firms. This study examines the critical enablers of service business model innovation (SBMI) for sustainable performance within the utility sector, particularly the electricity supply sector in Ghana. Using the best–worst method (BWM), this study identifies and prioritizes three main enablers and eleven sub-enablers, addressing a notable gap in understanding their impact on sustainable performance. The findings reveal that service value creation innovation is the most critical primary enabler, with human capital, technological platforms, and value-based pricing constituting the top three sub-enablers for sustainability performance. This study contributes to the service-dominant logic and BMI discourse by providing a novel hierarchical framework that aids managerial decision making in service-oriented firms, particularly in developing economies. The results underscore the need for utility companies to prioritize investments in key areas, such as human capital, technological advancements, and customer-centric approaches, to drive sustainable business practices and improve overall performance.

Evaluation of renewable energy projects based on sustainability goals using a hybrid pythagorean fuzzy-based decision approach

Due to the growth in energy demand and the need to pay attention to sustainable development, many developing countries face the challenge of making policies for growing sustainable energy. With attention to industrial expansion and population growth, Iran, a developing country, is confronting an energy shortage problem. Despite the appropriate capacity of renewable resources in Iran, due to the lack of proper planning for adopting renewable resources in Iran, these resources have a small share of the energy supply. This study aims to present a decision framework for assessing renewable energies in developing countries, in the case of Iran. In addition to technical aspects, sustainability aspects such as economic, social, and environmental factors are proposed to assess renewable sources in this work. The Best-Worst Method (BWM) and the Interval-Valued Pythagorean Fuzzy Weighted Aggregated Sum Product Assessment (IVPF-WASPAS) techniques are suggested to prioritize renewable sources. The findings indicated that solar energy was Iran's most suitable renewable resource. Also, the validation and sensitivity analysis confirmed the findings. This research provides a comprehensive perspective for policy-making and planning of sustainability in the energy industry.