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In the recent decade, significant growth of internet-based platforms and changes in people's moving preferences has led to an increase in the electronic taxis businesses. Hence, investigating the factors affected by such businesses can help increase their profits and, at the same, time their customers' satisfaction level. In this study, a hybrid fu...
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... the other hand, in this research, three active online taxi business companies in Iran with nicknames A, B, and C have been considered as alternatives. Based on the above definitions, the main criteria/sub-criteria of the research problem are given in Table 1. ...Context 2
... on the results, among criteria, the cost is the best criteria (with D+R=1.893) and quality is the worst criteria (with D+R=1.568). By utilizing a similar manner, for each criterion, the best and the worst sub-criteria are given in Table 10. ...Context 3
... this section, the FBWM is applied to calculate the weights of the criteria/sub-criteria. The average of opinions of three groups of experts is given in Tables A.1-A.9 in Appendix A. The obtained results from solving the FBWM model using LINGO software are given in Tables 11- 15. ...Context 4
... section is dedicated to ranking the alternatives applying the FTOPSIS method. To form the decision matrix, the opinions of three teams of experts have been gathered based on the linguistic variables proposed by Chen et al. (2010), which is shown in Table 16. The decision matrix based on the linguistic variables is presented in Table 17, and the average fuzzy decision matrix is given in Table 18. ...Context 5
... form the decision matrix, the opinions of three teams of experts have been gathered based on the linguistic variables proposed by Chen et al. (2010), which is shown in Table 16. The decision matrix based on the linguistic variables is presented in Table 17, and the average fuzzy decision matrix is given in Table 18. In Table 17, in expression (a1, a2, a3), a1 denotes the first expert group opinion, a2 shows the second expert group opinion and a3 represents the third expert group opinion. ...Context 6
... form the decision matrix, the opinions of three teams of experts have been gathered based on the linguistic variables proposed by Chen et al. (2010), which is shown in Table 16. The decision matrix based on the linguistic variables is presented in Table 17, and the average fuzzy decision matrix is given in Table 18. In Table 17, in expression (a1, a2, a3), a1 denotes the first expert group opinion, a2 shows the second expert group opinion and a3 represents the third expert group opinion. ...Context 7
... decision matrix based on the linguistic variables is presented in Table 17, and the average fuzzy decision matrix is given in Table 18. In Table 17, in expression (a1, a2, a3), a1 denotes the first expert group opinion, a2 shows the second expert group opinion and a3 represents the third expert group opinion. ...Citations
... One of the popular decision-making techniques that widely used in recent years is the Best-Wors Method (BWM) presented by (Rezaei 2015). This method has several significant benefits compared to the similar methods like AHP such as increasing the reliability and decreasing the computational burden (Aria et al. 2020). Besides its merits, the BWM could not deal with the uncertain environment of the decision-making problems. ...
One of the critically important tasks of supply chain managers is to evaluate the performance of the raw material providers, especially in today's modern and dynamic business environment. In this regard, the current study focuses on the evaluation process of the raw material providers based on some crucial metrics named the customer-based LARG paradigm. For this purpose, based on a real-world case study in the agri-food industry, the main criteria and sub-criteria are determined. Afterward, to evaluate the performance of the potential raw material providers, a machine learning-based method by combining the stochastic best-worst method and weighted decision tree is developed. In general, this research contributes to the literature by proposing an efficient machine learning-based model to investigate the raw material provider selection problem for the agri-food industry based on the customer-based LARG paradigm. The results obtained from the implementation of the developed approach show that the general, leagility, resilience, customer-based, and green criteria are the most significant ones, respectively. Also, among the sub-criteria, "Service level", "Robustness", "Cost", "Quality", "Manufacturing flexibility", "Delivery speed", "Waste management", and "Restorative Capacity" are specified as the best ones. Additionally, based on the achieved outcomes, the effectiveness, reliability, and validity of the proposed machine learning-based approach are confirmed.
... A relatively new technique that has garnered interest from researchers is the Fuzzy Best-Worst Method (FBWM). The primary benefits of the BWM compared to similar methodologies (e.g., AHP), as highlighted by Rezaei et al. [25] and Aria et al. [1], include: (i) a significant reduction in computational demands, (ii) enhanced reliability of results, (iii) decreased time required for pairwise comparisons, and (iv) ease of integration with other methods. However, in general, the use of FBWM in this study is due to the ease of understanding and comprehending its questionnaire for experts, as well as the high speed of completing the questionnaire data. ...
In today's world, businesses and, in general, supply chains have undergone extensive transformations, and relying solely on traditional metrics such as cost and quality cannot provide a comprehensive and complete evaluation of companies active in various sections of supply chains. One of the main concerns of supply chain managers is to create an integrated and comprehensive structure for evaluating the performance of active branches. In this context, this study presents a structure that, by simultaneously considering agility and sustainability metrics within the context of the industry 4.0, which has brought about fundamental changes in the supply chain environment in recent years, aims to evaluate the active branches in the dairy product supply chain. On the other hand, the increase in the volume of data produced in the supply chain environment and the development of the applications of machine learning algorithms in various fields, which offer better applications compared to intuitive approaches, have led this study to use hybrid data-driven approaches, which are a combination of expert-based methods and documented organizational data, to evaluate the performance of supply chain branches. Therefore, this study is innovative in terms of the evaluation metrics and the data-driven approach developed. In the first step, evaluation metrics appropriate to the dimensions of agility, sustainability, Industry 4.0, and general metrics were identified, and then the fuzzy best-worth method (FBWM) approach was used to weight the metrics. According to the findings, data-driven, marketing, overhead costs, delivery timeframe, and product quality were selected as the most important metrics. Subsequently, using the developed artificial neural network algorithm, which calculates the input weights of the metrics using the FBWM method, a model for evaluating the supply chain was presented, and the findings show that the developed approach performs better than other algorithms on the problem data with more than 92 percent accuracy.
... This method has many advantages compared to similar approaches (like AHP) which these are led to increase reliability and compatibility of the results. The main advantages of FBWM are (1) structured pairwise comparison, (2) less requirement for data, and (3) high reliability due to not considering all comparison vectors and only two of them (Rezaei et al. 2016;Aria et al. 2020). In other words, when the number of criteria/sub-criteria is high, using FBWM leads to reducing the cognitive burden and also increasing the reliability of the outputs. ...
The supplier selection problem is one of the most important issues in supply chain management. So, many papers have investigated the mentioned problem. However, the related literature shows that researchers had less attention to the sustainability and resilience aspects based on the customer preferences in supplier selection problem. To cover this gap, this research tries to investigate the customer-based sustainable-resilient supplier selection problem. In this way, a Markovian-based fuzzy decision-making method is proposed. At the outset, the customer preferences are evaluated using a combination of the quality function deployment and the Markov transition matrix. Then, by combining the transition matrix and the fuzzy best–worst method, the weights of the indicators are calculated. Finally, the decision matrix is formed and the performance of suppliers is measured based on the multiplication of the decision matrix and vector of sub-criteria weights. Regarding the recent pandemic disruption (COVID-19), the importance of online marketplaces is highlighted more than the past. Hence, this study considers an online marketplace as a case study. Results show that in a pandemic situation, the preferences of customers when they cannot go shopping normally will change after a while. Based on the Markov steady state, these changes are from the priority of price, availability, and performance in initial time to serviceability, reliability, and availability in the future. Finally, based on the FBWM results, from the customer point of view, the top five sub-criteria for sustainable-resilient supplier selection include cost, quality, delivery, responsiveness, and service. So, based on these priorities, the case study potential suppliers are prioritized, respectively.
... One of the relatively novel methods that attracted researchers' attention is the fuzzy best-worst method (FBWM) (Sofuoglu 2020). The main advantages of the BWM over similar approaches (e.g., AHP) are as follows (Rezaei et al. 2016;Aria et al. 2020;Abualigah et al. 2022): (i) this method significantly reduced the compute burden, (ii) this approach increases the reliability of the outputs, (iii) this needs less time to pairwise comparison, and (iv) this approach can easily combine with other methods. Before presenting the steps of the FBWM, it should be noted that e a ¼ ðl; m; uÞ is a triangular fuzzy number and the Graded Mean Integration Representation (GMIR), represented by R e a ð Þ; is calculated by relation (1). ...
Since the COVID-19 outbreak has led to drastic changes in the business environment, researchers attempt to introduce new approaches to improve the capability and flexibility of the industries. In this regard, recently, the concept of the viable supply chain, which tried to incorporate the leagile, resiliency, sustainability, and digitalization aspects into the post-pandemic supply chain, has been introduced by researchers. However, the literature shows that there is lack of study that investigated the viable supplier selection problem, as one of the crucial branches of viable supply chain management. Therefore, to cover this gap, the current work aims to develop a decision-making framework to investigated the viable supplier selection problem. In this regard, owing to the crucial role of the oxygen concentrator device during the COVID-19 outbreak, this research selects the mentioned product as a case study. After determining the indicators and alternatives of the research problem, a novel method named goal programming-based fuzzy best-worst method (GP-FBWM) is proposed to compute the indicators' weights. Then, the potential alternatives are prioritized employing the Fuzzy Vlse Kriterijumsk Optimizacija Kompromisno Resenje method. In general, the main contributions and novelties of the present research are to incorporate the elements of the viability concepts in the supplier selection problem for the medical devices industry and to develop an efficient method GP-FBWM to measure the importance of the criteria. Then, the developed method is implemented and the obtained results are analyzed. Finally, managerial and theoretical implications are provided.
Supplementary information:
The online version contains supplementary material available at 10.1007/s00500-022-07572-0.
... (5)) is applied. The GMI has already been employed in many previous fuzzy multi-criteria decision-making studies due to its simplicity and accuracy (Chen & Hsieh 1999;Aria et al. 2020;and Alamroshan et al. 2021). ...
According to recent studies in the field of human resource management (HRM), especially in project-based organizations (PBOs), stress is recognized as a factor that has a paramount significance on the performance of staff. Previous studies in organizational stress management have mainly focused on identifying job stressors and their effects on organizations. Contrary to the previous studies, this paper aims to propose a comprehensive decision-support system that includes identifying stressors, assessing organizational stress levels, and providing solutions to improve the performance of the organization. A questionnaire is designed and distributed among 170 senior managers of a major project-based organization in the field of the energy industry in Iran to determine organizational stressors. Based on the questionnaire results and considering the best worst method (BWM) as an approach to determine the weighting vector, the importance degree of each stressor is calculated. In the next stage, a decision-support model is developed to assess the stress level of a PBO through fuzzy inference systems (FIS). Some main advantages of the proposed hybrid decision-support model include (i) achieving high-reliable results by not-so-time-consuming computational volume and (ii) maintaining flexibility in adding new criteria to assess the occupational stress levels in PBOs. Based on the obtained results, six organizational stressors, including job incongruity, poor organizational structure, poor project environment, work overload, poor job promotion, and type A behavior, are identified. It is also found that the level of organizational stress is not ideal. Finally, some main recommendations are proposed to manage occupational stresses at the optimum level in the considered sector.
