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Combining the super‐efficiency model based on data envelopment analysis (DEA) with the Malmquist index model, this paper evaluated the efficiency of the logistics distribution center comprehensively considering the truck traffic restriction and provided decision suggestions to improve the efficiency of the logistics distribution center. This paper takes 20 logistics distribution centers as the research objects and uses economic factors, transportation factors, quality of distribution center business activities, and quality of customer service as the primary input indicators; selects eight indicators such as construction cost, transportation cost, labor cost, road facilities, accessibility, business demand, number of laborers, and customer satisfaction as the secondary input indicators; chooses distribution time and profit as the output indicators; and measures the static efficiency of logistics distribution centers from two perspectives, including the traditional unconstrained super‐efficiency model and the truck‐ restricted conditions, using the super‐efficiency model of data envelopment analysis (DEA). The Malmquist index model was used to measure the dynamic efficiency and change trend efficiency of the logistics distribution center, and a unified and comprehensive analysis was also made. The results of the case study show that the average efficiency of the logistics distribution center in the driving and nondriving restriction area is 0.872 and 0.914, respectively, and the average efficiency in the driving restriction area is about 4.5% lower than that of the nondriving restriction area, and variance is 1.58 times of the latter. Therefore, it can be concluded that the measures of truck driving restriction have an impact on the efficiency of the logistics distribution center, and the results of the super‐efficiency model with the restriction constraint have a greater impact on the logistics efficiency of the logistics distribution center than the traditional unconstrained super‐efficiency model. According to the evaluation results, suggestions on reasonable assignment of labor and other resources input are put forward for logistics distribution centers in areas where driving is restricted to improve efficiency. [ABSTRACT FROM AUTHOR]

Flight delays are a major concern for both travelers and airlines, with significant financial and reputational consequences. Accurately predicting flight delays is crucial for enhancing customer satisfaction and airline revenues. In this paper, we leverage the power of artificial intelligence and machine learning techniques to build a framework for accurately predicting flight delays. To achieve this, we collected flight information from September 2017 to April 2023, along with weather data, and performed extensive feature engineering to extract informative features to train our model. We conduct a comparative analysis of various popular machine learning architectures with distinctive characteristics, aiming to determine their efficacy in achieving optimal accuracy on our newly proposed dataset. Based on our evaluation of various architectures, our findings demonstrate that CatBoost outperformed the others by achieving the highest test accuracy and the lowest error rate in the challenging use case of Saudi Arabia. Moreover, to simulate real-world scenarios, our framework evaluates the best-performing model that has been selected for deployment in a web application, which provides users with the ability to accurately forecast flight delays and offers a user-friendly dashboard with valuable insights and analysis capabilities. [ABSTRACT FROM AUTHOR]