Search Results (10)

Smart aviation has received significant attention from various stakeholders in China as its advancement holds crucial implications for the aviation industry, and there is a growing need for aviation authorities to assess the extent of its development. The evaluation of smart aviation development processes rely on various factors that reflect the smart aviation development level, and these factors could help pave the way for the successful development of smart aviation. However, few studies have focused on the identification and prioritization of the key factors for smart aviation development, especially considering the uncertain nature of the problem. To this end, this study employs the grounded theory and the fuzzy best–worst method (BWM) to identify and prioritize the factors for smart aviation development. Through the utilization of grounded theory, 37 factors are determined to be critical for smart aviation development. Then, the fuzzy BWM is employed to evaluate and prioritize the identified factors considering their importance. The findings of this study reveal that the 4D track development level, proportion of R&D investment, and data resources sharing degree are the most influential factors for smart aviation development. By integrating grounded theory, fuzzy sets, and BWM, this study identifies and prioritizes the significant factors for smart aviation for the first time. In general, the outcomes of this study hold the potential to guide practitioners in focusing on the pivotal factors that contribute to smart aviation development. Full article

High-throughput technologies produce large-scale omics datasets, and their integration facilitates biomarker discovery and predictive modeling. However, challenges such as data heterogeneity, high dimensionality, and block-wise missing data complicate the analysis. To address these issues, optimization techniques, including regularization and constraint-based approaches, have been already employed for regression and binary classification problems. Building on these methods, we extended this framework to support multi-class classification. Indeed, applied to a multi-class classification task for breast cancer subtypes, our model achieves accuracy between 73% and 81% under various block-wise missing data scenarios. Additionally, we assess its performance on a regression problem using the exposome dataset, integrating a larger number of omics datasets. Across different missing data scenarios, our model demonstrates a strong correlation (75%) between true and predicted responses. Furthermore, we have updated the bwm R package, which previously supported binary and continuous response types, to also include multi-class response types. Full article

Under the combined influences of climate change and human activities, the western Jilin (WJ) Province, as a typical ecologically fragile area, has experienced ecological degradation and resource depletion. Therefore, it is urgently needed to assess its ecological carrying capacity (ECC) to provide scientific support for regional ecological protection and resource management. This study integrated the “Pressure-State-Response” (P-S-R) model with the “production, living, and ecological spaces” (PLES) conceptual model to construct a comprehensive evaluation indicator system for ECC. The indicator weights were calculated using a Bayesian BWM-CRITIC-CWDF linear combination method, and the spatial–temporal distribution of ECC was then assessed using an improved TOPSIS and gray relational analysis (GRA). This evaluation model overcomes the limitations of traditional methods in weight allocation, indicator correlation, and non-linear effects, providing a more accurate, reliable, and objective assessment of ECC. Furthermore, a bivariate spatial autocorrelation model was applied to reveal the interaction between the “coupling coordination degree (CCD) of PLES” and ECC. The results indicate that the ECC value was divided into a period of decline (2000–2010) and a period of growth (2010–2020); spatially, the ECC level transitioned from a high-west, low-east to a high-east, low-west pattern. This change was primarily driven by factors such as fertilizer usage, per capita GDP, and per capita output. The “CCD of PLES” and ECC indicated positive spatial correlation, primarily forming “high-high” and “high-low” clusters. This study provides a reliable evaluation index system and an evaluation model for evaluating ECC in WJ. The findings provide a theoretical foundation for the region’s sustainable development and offer valuable insights for ecological carrying capacity research. Full article

Most studies in recent decades focused on transforming linear economics into circular through recovering and remanufacturing the products. Circular Economies (CE) aim to minimize the usage of resources by utilizing the waste in production as new or raw materials. Interconnectivity between parties in the industrial system provides decision-makers with rich information and anticipation of failure. Industry 4.0 technologies (I4.0) allow for handling such issues, protecting the environment by utilizing resources efficiently, and restructuring the industry to be smarter as well. This paper contributes to achieving cleaner production (CP), CE, and social for manufacturers through the linkage between 6R methodology with new technologies of I4.0 such as Blockchain technology (BCT) and big data analytical technology (BDA). In this paper, the authors proposed a Multi-criteria decision-making (MCDM) decision framework based on the best-worst method (BWM), Decision-Making trial and evaluation laboratory (DEMATEL), Technique for order of preference by similarity to ideal solution (TOPSIS), and Complex Proportional Assessment (COPRAS). The authors contributed to addressing the weaknesses and problems of these subjective MCDM methods through the cooperation of the neutrosophic theory with the usage of MCDM methods in this work. In the first stage, all criteria that influence sustainable manufacturer selection are specified using literature research on this topic. BWM-based neutrosophic theory was combined to get the criteria’s weights with the aid of DEMATEL-based neutrosophic to obtain the least and best criteria used in BWM in the second stage. The optimal sustainable manufacturer was selected based on TOPSIS and COPRAS under neutrosophic theory in the third and fourth stages, respectively. Furthermore, a case study performed indicated manufacturer 2 (A₂) is an optimal sustainable manufacturer in two ranking methods otherwise, manufacturer 4 (A₄) is the worst sustainable manufacturer. The contribution of this work is to propose a hybrid MCDM with an uncertainty theory of neutrosophic for sustainable manufacturer selection based BDA-BCT with 6R. Sensitivity analyses were carried out to show the decision’s flexibility in various scenarios. Finally, the consequences for management viewpoints were considered. Full article

Dengue is a continuous health burden in Laos and Thailand. We assessed and mapped dengue vulnerability in selected provinces of Laos and Thailand using multi-criteria decision approaches. An ecohealth framework was used to develop dengue vulnerability indices (DVIs) that explain links between population, social and physical environments, and health to identify exposure, susceptibility, and adaptive capacity indicators. Three DVIs were constructed using two objective approaches, Shannon’s Entropy (SE) and the Water-Associated Disease Index (WADI), and one subjective approach, the Best-Worst Method (BWM). Each DVI was validated by correlating the index score with dengue incidence for each spatial unit (district and subdistrict) over time. A Pearson’s correlation coefficient (r) larger than 0.5 and a p-value less than 0.05 implied a good spatial and temporal performance. Spatially, DVI_WADI was significantly correlated on average in 19% (4–40%) of districts in Laos (mean r = 0.5) and 27% (15–53%) of subdistricts in Thailand (mean r = 0.85). The DVI_SE was validated in 22% (12–40%) of districts in Laos and in 13% (3–38%) of subdistricts in Thailand. The DVI_BWM was only developed for Laos because of lack of data in Thailand and was significantly associated with dengue incidence on average in 14% (0–28%) of Lao districts. The DVI_WADI indicated high vulnerability in urban centers and in areas with plantations and forests. In 2019, high DVI_WADI values were observed in sparsely populated areas due to elevated exposure, possibly from changes in climate and land cover, including urbanization, plantations, and dam construction. Of the three indices, DVI_WADI was the most suitable vulnerability index for the study area. The DVI_WADI can also be applied to other water-associated diseases, such as Zika and chikungunya, to highlight priority areas for further investigation and as a tool for prevention and interventions. Full article

A park-and-ride (P&R) system is a set of facilities where private vehicle users can transfer to public transport to continue their journey. The main advantage of the system is decreasing the congestion in the central business district. This paper aims to analyze the most significant factors related to a Park-and-Ride facility location by adopting a combined model of Analytic Hierarchy Process (AHP) and Best Worst Method (BWM). The integrated model is applicable for complex problems, which can be structured as a hierarchy with at least one 5 × 5 pairwise comparison matrix (PCM) (or bigger). Applying AHP for at least 5 × 5 PCM may generate inconsistent matrices, which may cause a loss of reliable information. As a solution for this gap, we conducted BWM, which generates more consistent comparisons compared to the AHP approach. Moreover, the model requires fewer comparisons compared to the classic AHP approach. That is the main reason of adopting the AHP-BWM model to evaluate Park-and-Ride facility location factors for a designed two-level hierarchical structure. As a case study, a real-world complex decision-making process was selected to evaluate the Park-and-Ride facility location problem in Cuenca city, Ecuador. The result shows that the application of multi-criteria methods becomes a planning tool for experts when designing a P&R system. Full article

In the context of Chinese innovation-driven strategy, the role of suppliers has been attracting much attention. Since not every supplier can contribute to the buyer’s innovation, scientifically selecting an innovative supplier is highly valued by decision-makers from the new energy vehicle (NEV) manufacturers. This paper focuses on proposing a novel decision framework in the context of collaborative innovation, which helps NEV manufacturers to select an innovative supplier who can work hand in hand with them to enhance their innovation performance. First, a novel capability-willingness-risk (C-W-R) evaluation indicator system is established, considering supply risk from a multi-proximity perspective which is tightly tied to collaborative innovation performance, only considered from geographical proximity in previous supplier selection research. Then a hybrid fuzzy-symmetrical multicriteria decision-making (MCDM) model is proposed that integrates fuzzy linguistic sets, best–worst method (BWM), prospect theory (PT) and VIKOR. With this approach, a final ranking is obtained for innovative supplier selection by NEV manufacturers in China. Moreover, sensitivity analysis and comparison analysis illustrate the proposed decision framework’s effectiveness and reliability and dig deep into the buyer−supplier collaborative innovation. Finally, some managerial suggestions are given for supplier selection from the standpoint of NEV manufacturers. Full article

The Park and Ride (P&R) system is a set of facilities available to private vehicle users to transfer to public transportation in order to complete their journey. The location of the facilities is determined by the purpose for which they have been created, for example, to reduce traffic in the central business district (CBD), reduce pollution, or increase the use of public transportation. Thus, a set of six main criteria and 19 sub-criteria are considered that are particularly important for decision-makers about the location of P&R facilities in a city. In order to identify which criteria are relevant, a method belonging to the multiple criteria decision is needed. The central point of this study is to evaluate the problem of the location of the facilities of the P&R system according to the point of view of the experts. For this aim, the Best Worst Method (BWM) is adopted to estimate the location of the facilities of the P&R system. The questionnaire survey has been designed estimated by ten transport experts in the related field. The recently created BWM was conducted. The results highlighted that “accessibility of public transportation” is the most important aspect of the problem of the location of P&R facilities. The results obtained provide greater accuracy in the location of facilities problem than the pure analytic hierarchy process method (AHP). Full article

Failure mode and effects analysis (FMEA) is a risk assessment method that effectively diagnoses a product’s potential failure modes. It is based on expert experience and investigation to determine the potential failure modes of the system or product to develop improvement strategies to reduce the risk of failures. However, the traditional FMEA has many shortcomings that were proposed by many studies. This study proposes a hybrid FMEA and multi-attribute decision-making (MADM) model to establish an evaluation framework, combining the rough best worst method (R-BWM) and rough technique for order preference by similarity to an ideal solution technique (R-TOPSIS) to determine the improvement order of failure modes. In addition, this study adds the concept of aspiration level to R-TOPSIS technology (called R-TOPSIS-AL), which not only optimizes the reliability of the TOPSIS calculation program, but also obtains more potential information. This study then demonstrates the effectiveness and robustness of the proposed model through a multinational audio equipment manufacturing company. The results show that the proposed model can overcome many shortcomings of traditional FMEA, and effectively assist decision-makers and research and development (R&D) departments in improving the reliability of products. Full article

High-tech companies are rapidly growing in the world. Research and development (hereafter R&D) department strength is the main asset that allows a firm to achieve a competitive advantage in high-tech businesses. The allocated budget to this sector is finite; thus, integration, human resource, risk and budget limitations should be considered to choose the most valuable project in the best portion of time. This paper investigates a case study from a high-tech company in Iran to prioritize the most attractive technologies for the R&D department. The case consists of twenty three technology options and the goal is to find the most attractive projects to sort them out for implementation in the R&D department. In this research, scholars proposed the best–worst method (henceforth BWM) to find the weight of the criteria of the attractive technologies in first step and utilize the newly developed method total area based on orthogonal vectors (henceforward TAOV) to sort the selected technologies based upon the identified criteria. Project integration is one of the least-noticed subjects in scientific papers; therefore, the researchers presented a zero or one linear programming (ZOLP) model to optimize and schedule the implementation procedure on the project risk, budget and time limitation simultaneously. The results indicate that starting few but attractive projects in the first years and postponing the rest to the future, helps a firm to manage funds and gain profit with the least amount of risk. Full article