The development of e-commerce has led to a surge in the quantity of online shopping parcels. However, given the lack of “scale effect” last mile delivery is inefficient, expensive and produces a considerable amount of carbon emissions, which has become an obstacle to the development of a sustainable economy. This work proposes a travelling salesman problem with carbon emission reduction in last mile delivery. The proposed problem aims to reduce the total costs and carbon emissions of last mile delivery by deciding on the allocation of parcel lockers whilst scheduling delivery routes. In addition, we take the customer self-collection intention into consideration and translate it into self-collection costs, which are included in the objective. An iterated local search (ILS) algorithm is proposed, and four new local search operators are designed to improve customer allocation. The proposed method is tested on a set of scattered and clustered instances, including a real-world instance. Computational results show the superiority and competitiveness of the proposed algorithm.

Accurate tourist flow prediction is key to ensuring the normal operation of popular scenic spots. However, one single model cannot effectively grasp the characteristics of the data and make accurate predictions because of the strong nonlinear characteristics of daily tourist flow data. Accordingly, this study predicts daily tourist flow in Huangshan Scenic Spot in China. A prediction method (GA-CNN-LSTM) which combines convolutional neural network (CNN) and long-short-term memory network (LSTM) and optimized by genetic algorithm (GA) is established. First, network search data, meteorological data, and other data are constructed into continuous feature maps. Then, feature vectors are extracted by convolutional neural network (CNN). Finally, the feature vectors are input into long-short-term memory network (LSTM) in time series for prediction. Moreover, GA is used to scientifically select the number of neurons in the CNN-LSTM model. Data is preprocessed and normalized before prediction. The accuracy of GA-CNN-LSTM is evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE), Pearson correlation coefficient and index of agreement (IA). For a fair comparison, GA-CNN-LSTM model is compared with CNN-LSTM, LSTM, CNN and the back propagation neural network (BP). The experimental results show that GA-CNN-LSTM model is approximately 8.22% higher than CNN-LSTM on the performance of MAPE.

The aging population and an unhealthy lifestyle have led to a considerable proportion of chronic diseases in many countries. A new generation of emerging technologies has set off a new wave of revolution around the world, such as cloud computing, the Internet of Things, artificial intelligence and so on. In recent years, the literature associated with chronic disease research driven by emerging technologies has grown rapidly, but few studies have used bibliometrics and a visualization approach to conduct deep mining and reveal a panorama of this field. This paper is a bibliometric analysis of chronic disease research driven by emerging technologies, analyzed and visualized the time distribution, space distribution, literature co-citation and research focus. Moreover, this study visualized and determined the dynamic knowledge structure of chronic disease research driven by emerging technologies, which will be helpful in understanding current research status and identifying future research directions in this research field for e-health and medical informatics scholars.