Best Worst Method Method Research Articles

Data Asset Quality Evaluation Framework Based on a Hybrid Multi‐Criteria Decision‐Making Method

ABSTRACTThis study presents a new framework for evaluating data asset quality using a hybrid multi‐criteria decision‐making (MCDM) approach that integrates the decision making trial and evaluation laboratory (DEMATEL), best–worst method (BWM), and fuzzy‐technique for order of preference by similarity to the ideal solution (TOPSIS) techniques. First, the framework considers data as both a product and an asset, leading to the development of quality indicators beyond the traditional dimensions. Subsequently, the interrelationships among indicators are addressed using the DEMATEL method, allowing for the identification of key indicators that significantly influence data asset quality in a given scenario. The BWM method is then employed to determine the weights of these key indicators, enabling a more precise assessment of their importance. After that, the TOPSIS method, incorporating triangular fuzzy numbers, is utilized to rank the data asset quality of different companies. Finally, the effectiveness of the framework is demonstrated by applying it to a group of companies, and the results of the company's evaluation are discussed, along with the corresponding data asset quality improvement initiatives.

Comparative Analysis of Paddy Cultivation Sustainability through Integrating Eco-efficiency and Best-Worst Method Approaches

To investigate the sustainability of agricultural systems, it is necessary to prioritize production systems from various viewpoints. The current research, as a new approach, aimed at prioritizing different paddy cultivation areas in Iran based on sustainability criteria using the best-worst method (BWM), which is regarded as one of the modern multi-criteria decision-making techniques. Mazandaran, Fars, and Khuzestan areas in Iran with different climatic classes, i.e., Mazandaran, Fars, and Khuzestan, were considered in this study. Sustainability criteria included energy, exergy, economic, and environmental indices, and the prioritization process was done based on the eco-efficiency index and BWM method. In the evaluation of the eco-efficiency index, the amount of net income and the total environmental index were considered as economic and environmental indices, respectively. In the prioritization of the BWM method, ranking was done based on energy and exergy efficiency, benefit-cost ratio, and the total environmental index. The results of the eco-efficiency index investigation demonstrated that paddy production in Mazandaran province had the most sustainability and compatibility with the environment, followed by Fars and Khuzestan provinces, respectively. The results of the BWM method prioritization also indicated that Mazandaran province was the best and most sustainable area with a final index of 0.98, followed by Fars and Khuzestan provinces, which were placed in the next ranks of sustainable paddy cultivation areas with final indices of 0.93 and 0.85, respectively. In conclusion, the paddy production systems located in the humid subtropical region were found as the most sustainable systems in this area.

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.

Supplying hydrogen for green steel through renewable energy sources: A case study of Turkiye

Steel is the most common industrial material worldwide due to its numerous applications, high strength and recyclability. Steel production involves alloying iron with various amounts of carbon and other elements to produce thousands of different grades and types of steel for various purposes. However, the intensive use of carbon in steel production is one of the factors contributing to global climate change. To use of hydrogen and renewable energy sources in steel production has emerged as a significant solution to reduce carbon emissions. The aim of this study is to obtain green hydrogen for the production of green steel. The focus is on utilizing offshore wind energy as a renewable energy source to give us the energy we need to electrolyze seawater. The study uses the seas of Turkiye for case analysis. In this paper, an integrated Multi-criteria decision-making method is used to integrate hybrid energy systems and their application for green industry and to select a location for a power generation plant. The study is a new contribution to the literature in terms of its scope. Green steel's location selection is addressed for the first time in the literature. The weights of the criteria are determined by employing the Best-Worst Method (BWM) as a multi-criteria decision-making technique, and the alternatives are evaluated using the Analytic Hierarchy Process (AHP). By using the BWM method, decision makers can have a clear understanding of the evaluation range for pairwise comparisons of criteria, which reduces potential biases. According to the study, the northern part of the Aegean Sea in Turkiye is the most suitable alternative for green steel production, with offshore wind energy being used for hydrogen production. The study also revealed that the most important main criterion for this hybrid system is “natural factors”, and the sub-criteria are “wind speed and power density” by far compared to other sub-criteria. The fact that the area determined for the facility is suitable for construction and the possibility that the customer of the energy produced is other industrial establishments here is the most important indicator that the methods applied give good results.

Groundwater quality modeling and determining critical points: a comparison of machine learning to Best-Worst Method.

In Iran, similar to other developing countries, groundwater quality has been seriously threatened. Therefore, this study aimed to apply Machine Learning Algorithms (MLAs) in Groundwater Quality Modeling (GQM) and determine the optimal algorithm using the Best-Worst Method (BWM) in Ardabil province, Iran. Groundwater quality parameters included calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), chlorine (Cl-), sulfate (SO4-), total dissolved solids (TDS), bicarbonate (HCO3-), electrical conductivity (EC), and acidity (pH). In the following, seven MLAs, including Support Vector Regression (SVR), Random Forest (RF), Decision Tree Regressor (DTR), K-Nearest Neighbor (KNN), Naïve Bayes, Simple Linear Regression (SLR), and Support Vector Machine (SVM), were used in the Python programming language, and groundwater quality was modeled. Finally, BWM was used to validate the results of MLAs. The results of examining the error statistics in determining the optimal algorithm in groundwater quality modeling showed that the RF algorithm with values of MAE = 0.28, MSE = 0.12, RMSE = 0.35, and AUC = 0.93 was selected as the most optimal MLA. The Schoeller diagram also showed that various ion ratios, including Na+K, Ca2+, Mg2+, Cl-, and HCO3+CO3, in most of the sampled points had upward average values. Based on the results of the BWM method, it can be concluded that a great similarity was observed between the results of the RF algorithm and the classification of the BWM method. These results showed that more than 50% of the studied area had low quality based on hydro-chemical parameters of groundwater quality. The findings of this research can assist managers and planners in developing suitable management models and implementing appropriate strategies for the optimal exploitation of groundwater resources.

Examining the facilitators of I4.0 practices to attain stakeholders’ collaboration: a circular perspective

ABSTRACT The fourth industrial revolution (I4.0) has changed the traditional business model, bringing various benefits, including increased efficiency and productivity in organizations. However, to attain success in I4.0 practices requires collaboration from various stakeholders. This study objectives to identify the facilitators of I4.0 practices that can lead to successful collaboration among stakeholders from a circular perspective. An extensive literature review is performed to identify 14 potential facilitators. Further, the study adopts a mixed methodology of Best-Worst Method (BWM) and Interpretive Structural Modeling (ISM) to analyze the interconnectedness among the identified facilitators. BWM method was used to determine the relative importance of the identified facilitators, while ISM technique was used to determine the relationships between the facilitators of I4.0 practices. The findings from the study reveal that to strengthen stakeholder collaboration, organizations need to focus more on training and capacity-building programs and create more opportunities for technology exchange.

Evaluation of decision support system for disaster management using multi-criteria decision techniques: a case study of Alappuzha, Kerala

ABSTRACT The monsoon pattern has shifted across Kerala. This, combined with significant deforestation and hill denuding, has resulted in catastrophic floods and landslides, particularly during the Southwest monsoon. Urgent relief services were to be delivered in a timely and accurate manner in order to sustain the lives of the impacted people. Even though resources were sent to several relief camps, they were either in excess or shortage on multiple instances. The conditions that prevailed during the monsoon time, as well as concerns and challenges during disaster relief efforts, must first be investigated for the effective operation of these supply chain activities. This research aims to develop a disaster logistics hub location selection decision support system, based on the Fuzzy Analytic Hierarchy Process (FAHP) and Best Worst Method (BWM), to meet the needs of disaster victims and rescue teams in the event of flooding, and to implement the proposed systems in Kuttanad, Kerala. Initially, the criteria are determined and structured in the hierarchy, and the weightage for the criteria is done via a questionnaire technique. Expert opinion on nine points scale was gathered from a set of experts working under different levels of positions during disaster management. The weights obtained by the FAHP and BWM methods were statistically analysed and compared for the reliability of the two techniques. The suggested model and application results may throw light on future work, particularly in the realm of disaster logistics management.

Combating of Associated Issues for Sustainable Agri-Food Sectors

Because of growing populations, more people living in cities, and higher incomes, the demand for food has changed in both quantity and quality. This has sped up the growth of different agricultural production and distribution chains. The necessary transition processes in the agri-food sectors, however, can often take a very long time to complete and have negative social and environmental effects, which puts the traditional development paradigm into question. This study looked at the trends affecting food systems in addition to the viability of sustainable development in Indian agriculture. In order to identify and then prioritize the related issues facing the agri-food sectors, this study comprised a thorough examination of the literature. Experts were consulted to rank the identified issues according to their degree of impact on the system with the use of an integrated “Best-Worst-Method (BWM)” and “Step-Wise Assessment and Ratio-Analysis (SWARA)” approach. The associated major issues were ranked using the BWM method, which revealed that “Issues with the lack of innovative systems” rated at the first level and was followed by “Issues with stability and availability of food; Issues of climate change and natural disasters; Food-governance-related issues; and Issues of accessing and utilizing food”, accordingly. The results of this study will help in developing appropriate intervention techniques at various service levels and in formulating policies, in addition to taking competent governments into consideration.

Identifying the Best Strategies for Improving and Developing Sustainable Rain-Fed Agriculture: An Integrated SWOT-BWM-WASPAS Approach

The practice of rain-fed agriculture plays a vital role in both the economy and food security, yet it is subject to various challenges such as climate change and institutional barriers. This study employs the Strengths, Weaknesses, Opportunities, and Threats (SWOT) analyses, Best-Worst Method (BWM), and Weighted Aggregated Sum Product Assessment (WASPAS) integrated approach to identify the most effective strategies for improving and developing sustainable rain-fed agriculture in Mashhad, Iran. The SWOT analysis identifies the essential sub-factors for improving and developing sustainable rain-fed agriculture. Then, the BWM method is utilized to assign weights to each sub-factor. Finally, the WASPAS method is used to rank the 19 strategies that can help achieve sustainable rain-fed agriculture. The findings of this study reveal that the strategy of establishing an institutional framework to promote sustainable rain-fed agriculture (WT7) has received the highest score. On the other hand, strategies related to supporting policies at the farm level (ST2, WO3, WT2, WT1) were placed in the middle and final priorities. Thus, it is recommended that in the current context of rain-fed agriculture in Mashhad, policymakers prioritize institutional policies related to rain-fed agriculture over farm-level policies. This study proposes a comprehensive and systematic approach to enhance and promote sustainable rain-fed agriculture.

Ranking Sub-Watersheds for Flood Hazard Mapping: A Multi-Criteria Decision-Making Approach

The aim of this paper is to assess the extent to which the Sad-Kalan watershed in Iran participates in floods and rank the Sad-Kalan sub-watersheds in terms of flooding potential by utilizing multi-criteria decision-making approaches. We employed the entropy of a drainage network, stream power index (SPI), slope, topographic control index (TCI), and compactness coefficient (Cc) in this investigation. After forming a decision matrix with 25 possibilities (sub-watersheds) and 5 evaluation indices, we used four MCDM approaches, including the analytic hierarchy process (AHP), best–worst method (BWM), interval rough numbers AHP (IRNAHP), picture fuzzy with AHP (PF-AHP), and picture fuzzy with linear assignment model (PF-LAM, hereafter PICALAM) algorithms, to rank the sub-watersheds. The study results demonstrated that PICALAM exhibited superior performance compared to the other methods due to its consideration of both local and global weights for each criterion. Additionally, among the methods used (AHP, BWM, and IRNAHP) that showed similar performances in ranking the sub-watersheds, the BWM method proved to be more time-efficient in the ranking process.

Comparison of AHP and BWM methods based on ArcGIS for ecological suitability assessment of Panax notoginseng in Yunnan Province, China

Panax notoginseng (P. notoginseng) is a traditional Chinese medicinal material with high economic and medicinal value. There is an urgent need to find new cultivation areas due to continuous cropping barriers and the effects of climate change. This study integrated the geographical environment of Yunnan with the necessary requirements for a suitable habitat for P. notoginseng and comprehensively analyzed climatic, topographical and soil factors. Furthermore, the criteria were weighted and prioritized by the analytic hierarchy process (AHP) and the best worst method (BWM) respectively, and the overlay analysis method was used to present the suitability map in the GIS environment. Two ecological suitability assessment models were established and compared to explore the ecological suitability regionalization of P. notoginseng in Yunnan Province, China. The results showed that:(1) Among the 12 driving factors, precipitation in the hottest season is the most important, followed by average temperature in the coldest season, average temperature in the hottest season, altitude, and annual precipitation. (2) The most suitable, suitable, and unsuitable regions obtained by the AHP-GIS model accounted for respective 17.98%, 33.41%, and 48.61% of the total area of Yunnan. The values for BWM-GIS model were 13.93%, 40.25%, and 45.81%, respectively. (3) The suitable regions obtained by the two models were overlayed and concentrated in Wenshan, Qujing, Kunming, Chuxiong, Baoshan, Dali, Lijiang, Honghe and Yuxi in central Yunnan, while the unsuitable regions were distributed in Xishuangbanna, Puer, Dehong, Diqing, and southwestern Zhaotong. The sensitivity of the assessment model was analyzed, and its reliability was verified by receiver operating characteristic (ROC) and area under curve (AUC). The BWM-GIS model (AUC=0.85) had higher accuracy than AHP-GIS model (AUC=0.822), and was more suitable for ecological suitability analysis of P. notoginseng. Our research results provide a theoretical basis for the rational expansion of cultivation regions of P. notoginseng and agricultural layout planning in Yunnan Province of China.

Effect of wear parameters and their optimization for wear characteristics of TiCrN thin film using novel MCDM method

This study focuses on experimental and statistical analysis to explore the influence of wear parameters and its optimization for TiCrN thin film deposited tool steel under dry sliding conditions. In this order, nine tribological experiments were performed on the TiCrN deposited tool steel against the SAPH370 steel counterbody. Taguchi L9 orthogonal array was applied to design the experimental setup. There was a total of three wear parameters chosen to perform the tribo-experiment and the parameters are applied load (ranges 10-20 N), sliding velocity (ranges from 0.05 m/s to 0.15 m/s), sliding distance (ranges from 500 m to 1500 m). These wear parameters were evaluated using three different wear characteristics of the TiCrN thin film namely: friction coefficient, mass loss of disc, and hardness of the worn surface. ANOVA was also performed to analyze the significant effect and contributions of the wear parameters on the wear characteristics. However, the wear parameter was optimized using the multicriteria decision-making (MCDM) method which includes entropy, best-worst method (BWM), and TODIM (Tomada de Decisao Interativa Multicriterio) method. Entropy and BWM methods were used to determine the compromised criteria weight while TODIM was used to rank the alternative (sets of wear parameters) based on their dominance score. The stability and robustness of the ranking method were examined using sensitivity analysis. Finally, the linear and nonlinear mathematical model was also developed and compared based on the value of R2.

A Novel Axial-Distance-Based Aggregated Measurement (ADAM) Method for the Evaluation of Agri-Food Circular-Economy-Based Business Models

Multicriteria decision making (MCDM) is a field that helps decision makers evaluate alternatives based on multiple criteria and encompasses scoring, distance-based, pairwise comparison, and outranking methods. Recent developments have aimed to solve specific problems and overcoming the limitations of previous methods. This paper proposes a new axial-distance-based aggregated measurement (ADAM) method, which is used in combination with the best-worst method (BWM) to evaluate agri-food circular economy (CE)-based business models (BMs) to create a more sustainable and efficient system for producing and consuming food. This paper proposes nine BMs, which were evaluated against eight criteria. The BWM method was used to obtain the criteria weights, while the ADAM method was used to obtain a final ranking of the BMs. The results indicate that a sustainable circular agri-food supply chain is a BM that can bring companies the most significant progress in business and strengthen their position in the market. We concluded that the ADAM method is effective for solving MCDM problems and that, overall, the model is an effective tool for solving the problem defined in this study. The main contributions are the development of a new MCDM method and a hybrid model, the establishment of the framework for evaluation and selection of CE-based BMs, and the identification of the most important ones.

A multi-criteria approach to decision-making in telecommunication network components selection

The problem of decision-making regarding the selection of the optimal composition of telecommunication network components is very urgent. The subject of the research in the article is a multi-criteria approach to the selection of telecommunication network components. This approach is related to the evaluation of possible options based on a set of indicators. The set of such indicators is determined by the specifics of a telecommunication network. One of the most dominant devices in telecommunication (mobile) systems is speech codecs. When choosing the appropriate codec configuration, there is a need to make a decision considering a set of contradicting criteria. The use of MCDM (Multiple Criteria Decision Making) methods when making decisions is a promising approach to solving such problems. The current article increases the efficiency of the multi-criteria approach to decision-making in the process of selecting telecommunication network components, in particular, a speech codec. The following methods were used: MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution), entropy, CRITIC (Criteria Importance Through Inter-criteria Correlation) and BWM (Best–Worst method). The following results were obtained. Determining the weight of the criteria by which alternatives are evaluated is one of the key problems that arise when making multi-criteria decisions. Objective methods of entropy, CRITIC, and subjective BWM methods were used to find weighting factors of criteria. Different methods of determining the weights of the criteria give different values. The use of the combined BWM-CRITIC method is proposed, which balances the subjective opinions of experts and assessments made solely based on the decision matrix data. The values of the correlation coefficients showed a close relationship between the weights of the criteria determined by different methods. However, the strongest connection with other methods was shown by the combined BWM-CRITIC method. The MARCOS method was used to rank the alternatives and select the best alternative. A ranking of the set of speech codecs is obtained, which allows for determining the best alternative Conclusion. A comprehensive approach to the telecommunication network component selection is proposed, namely the multi-criteria BWM-CRITIC-MARCOS model, based on a combination of MCDM methods. The integration of methods into the proposed model provides a systematic approach to the assessment and selection of telecommunication network components.

Comparison of the DRASTIC + L and modified VABHAT models in vulnerability assessment of Karaj aquifer, central Iran, using MCDM, SWARA, and BWM methods

One of the most important and perilous groundwater contaminants is nitrate. The study area is the unconfined (free) aquifer of Karaj, located in central Iran, which is subject to quantity and quality hazards due to unbalanced urban, industrial, and agricultural developments. Evaluation of the vulnerability condition of this aquifer against nitrate as the contamination index is essential since the most consumed water in the area is provided by groundwater. This paper describes a new model that integrates vulnerability of aquifer obtained by DRASTIC + L model (V), extraction rate (E), head changes (H), aquifer media (A), and transmissivity (T), i.e., Modified VEBHAT model. The study aims to detect vulnerable zones of the unconfined aquifer of Karaj using DRASTIC + L and Modified VEBHAT vulnerability models. The weighting of the parameters has been performed employing the recent Multiple-Criteria Decision-Making (MCDM) methods, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Best Worst Method (BWM). The results indicated that the highest levels of vulnerability occurred from the east to the south of the aquifer (high to super-critical), including urban and residential areas, regarding both of the vulnerability models’ plots. The plots of the two methods were similar and did not exhibit significant differences. In addition, the Modified VEBHAT had more certainty than the DRASTIC + L and has optimized and improved the DRASTIC + L model because the most vulnerable zone in the output plots of the Modified VEBHAT model was in the east of the aquifer, which had good agreement with the nitrate contaminant mass with the highest concentration.

Astronomical observatory site selection using fuzzy AHP and BWM methods

Establishing an observatory often involves complex decisions, such as choosing a site based on multiple conflicting criteria. In this study, we develop a multi-criteria decision analysis process by combining Geographic Information System (GIS) analysis with the (MCDA) Multi-Criteria Decision Analysis and use this process to determine the most suitable sites for the construction of an observatory in the Malatya urban area. GIS was used to calculate, classify, and analyze criteria, while FAHP (Fuzzy Analytic Hierarchy Processes, Buckley's method) and BWM(MCDA type Best-Worst Method) methods were used to weight the decision criteria and determine their effects on alternative sites. While the Cloud Cover criterion in the BWM method was the most important criterion with 28%, the most important criterion for the FAHP method had a comparable value of 27.8%. Meteorological criteria were the most important criteria group with values of 50.4% according to the FAHP method and 44.6% according to the BWM method. The study is based on meteorological, geographic, and anthropogenic datasets, suggesting the most appropriate sites for the astronomical observatory within the boundaries of the study area. The proposed sites are the result of site selection, which is the first phase of site selection for astronomical observatories. This site selection is important to limit the number of field alternatives. It is necessary to conduct field tests among the proposed areas and select the final site according to the results. The successful use of GIS and more than one MCDA method will pave the way for the development of various methods for astronomical observatory site determination.

Identifying and Prioritizing the Challenges and Obstacles of the Green Supply Chain Management in the Construction Industry Using the Fuzzy BWM Method

The construction supply chain network has been facing challenges in relation to reducing cost and delivery time, increasing the quality of the built assets, and reducing environmental pollution. These issues have caused contractors and project managers in this industry to note the concept of green construction supply chain management (GCSCM). This study examined the most important challenges and barriers to the implementation of GSCM in the construction industry. In this paper, the components and sub-components of GCSCM were identified using the literature review and opinions of the experts according to the supply chain management. The opinions of construction experts and project managers were collected through focus group meetings. The components were categorized into five main and supporting groups, with “Green Design”, “Green Procurement”, and “Green Production” as the main components and “Green Management” and “Green Information” as the supporting components. Subsequently, the sub-components, in regard to each component, were distinguished. Finally, the fuzzy best–worst method (BWM) was utilized to determine the importance weights of the identified components and sub-components through the opinions of five experts with practical relevant experience. The findings of the fuzzy BWM method show that “Green Design” is the most important component, followed by “Green Management” and “Green Implementation”. Additionally, “Lack of designers, contractors and planners” was ranked the first among the identified sub-components. This paper can assist construction managers, contractors, and policymakers with finding and overcoming the barriers and obstacles of implementing GCSCM.

A New Integrated Multi-Criteria Decision-Making Model for Sustainable Supplier Selection Based on a Novel Grey WISP and Grey BWM Methods

Supplier selection is an important task in supply chain management, as suppliers have a vital role in the success of organisations in a supply chain. Sustainability has emerged as a solution to decreasing resources and increasing environmental and social problems in the past few decades. It has been applied to various industrial operations, one of them is supplier selection, to mitigate unwanted effects in the future. Sustainable supplier selection is a complicated multi-criteria decision making problem, including several criteria from economic, environmental, and social perspectives. To deal with subjective judgements of decision makers, fuzzy and grey methods are widely used in multi-criteria decision making, In the case of small, limited, and incomplete data, the grey theory provides satisfactory results, compared to fuzzy methods. Therefore, this study is an integrated method including grey Best-Worst Method (BWM) and grey Weighted Sum-Product (WISP) for choosing the most sustainable supplier for a textile manufacturer, which includes three main criteria and twelve sub-criteria. According to the result of the proposed model, the supplier with the best performance was determined to be the supplier with the SP2 coded. The results of the developed model were shown to the experts, and the accuracy of the results was confirmed. According to the experts, a higher amount of product can be purchased from the supplier with the SP2 code, and a tighter relationship can be worked with this supplier. The contributions of this study are: (1) Develop a new grey MCDM model called Grey WISP. (2) Create a new integrated MCDM model with grey theory, BWM, and WISP methods that can be applied to assess supplier sustainability using this hybrid model. The proposed model can be used not just for selecting sustainable suppliers, but also for any other decision problems that have multiple criteria and alternatives. The findings suggest that the Grey WISP method achieved accurate results.

Multi-Criteria Group Decision-Making Models in a Multi-Choice Environment

The best–worst method (BWM) has recently demonstrated its applicability in addressing various decision-making problems in a practical setting. The traditional BWM method is based on deterministic information gathered from experts as pairwise comparisons of several criteria. The advantage of BWM is that it uses fewer calculations and analyses while maintaining good, acceptable consistency ratio values. A multi-choice best–worst method (MCBWM), which considers several options for pairwise comparison of preferences between the criteria, has recently been developed. The experts are given the option to select values from several comparison scales. The MCBWM technique has been shown to be better. Presenting the options for which an optimal solution has been found simplifies the calculation and establishes the ideal weight values. This study proposes two different mathematical programming models for solving multi-criteria decision-making problems having multiple decision-makers. The two methods are proposed considering the multi-choice uncertainty assumption in pairwise criteria comparisons. Additionally, it considers the best–worst method as the base model. The multi-choice uncertainty is applied to determine the best choice out of multiple choices. It gives a real-life scenario to the decision-making problems. Although there are many other forms of uncertainty, such as fuzzy, intuitionistic fuzzy, neutrosophic, probabilistic, etc., it focuses on choices instead of ambiguity in terms of the probabilistic or fuzzy nature of parameters. The parameter considered as multi-choice is the pairwise comparison. These parameters are handled by applying the Lagrange interpolating polynomial method. The proposed models are novel in terms of their mathematical structure and group decision-making approach. The models are formulated and further validated by solving numerical examples. It provides a framework for solving mcdm problems where the weightage to the decision-makers is also incorporated. The CR values for all the models of example 1 and 2, and the case study has been found acceptable.

Developing and ranking the strategies of Zarand power plant using quantitative strategic planning matrix and best-worst method

Developing the power plants in any country is considered an economic development strategy as it changes the country’s market despite the competitive pressures. However, to grow the electricity market, policymakers need to evaluate the domestic Strengths, Weaknesses, Opportunities, and Threats (SWOT) of electric power plants. So, the necessity of a strategic plan for the power plants is inevitable to reach the goals such as eliminating the shortcomings, and meeting the desired criteria of an organization, such as more market share, acceptable profitability, customer satisfaction, increasing profit growth rate, and productivity through economic and cost-effective operations. Therefore, in this paper, the Zarand power plant in Iran is chosen as a case study to develop the associated strategies and rank them. A power plant strategic plan was generated by using a SWOT matrix. Then, the developed strategies were prioritized using a quantitative strategic planning matrix (QSPM) and the best-worst method (BWM). The results confirm that developing the main product (electricity power), managing water consumption, and providing equipment are the most critical strategies for the Zarand power plant. Findings also demonstrated that the results of the BWM method are more accurate and correct than the QSPM method for prioritizing strategies