Vehicle routing, which is effective and efficient, is a dominant aspect of supply chain management in general and deep learning (DL) in particular. It is also a right step towards the fuel conservation and environmental concern in disposing used commodities. Economics of logistics and transportation plays a major role in deciding the competitiveness of the product, either new or used, in the market. With the upward trends of fuel and logistics costs, manufacturing industries have little option other than keeping the cost of transportation the lowest. Many organizations now started implementing lesser expensive and proper transport modes to keep the maintenance of supply chain cost to the minimum. Proper handling of returned commodities to recover value without affecting the environment may need appropriate techniques or methodologies. This paper deals with the routing of vehicles with energy conservation as the agenda in the value recovering method named as repair service work. It is done through a big data-based deep learning model, in a multicommodity environment. Here, the transportation of commodities to repair service facilities is given an in-depth focus to reduce the energy use. The minimization of the distance traveled by the truck fleet reduces the energy consumption by the trucks. This article deals with the optimization of emergency logistic with the assistance of deep learning approach, whereas this approach attains 57.19 km with 6 optimized routes. The process of emergency flow control is attained effectively using deep learning approach.

This work aims to perform the unified management of various departments engaged in smart city construction by big data, establish a synthetic data collection and sharing system, and provide fast and convenient big data services for smart applications in various fields. A new electricity demand prediction model based on back propagation neural network (BPNN) is proposed for China?s electricity industry according to the smart city?s big data characteristics. This model integrates meteorological, geographic, demographic, corporate, and economic information to form a big intelligent database. Moreover, the K-means clustering algorithm mines and analyzes the data to optimize the power consumers? information. The BPNN model is used to extract features for prediction. Users with weak daily correlation obtained by the K-means clustering algorithm only input the historical load of adjacent moments into the BPNN model for prediction. Finally, the electricity market is evaluated by exploring the data correlation in-depth to verify the proposed model?s effectiveness. The results indicate that the K-mean algorithm can significantly improve the segmentation accuracy of power consumers, with a maximum accuracy of 85.25% and average accuracy of 83.72%. The electricity consumption of different regions is separated, and the electricity consumption is classified. The electricity demand prediction model can enhance prediction accuracy, with an average error rate of 3.27%. The model?s training significantly speeds up by adding the momentum factor, and the average error rate is 2.13%. Therefore, the electricity demand prediction model achieves high accuracy and training efficiency. The findings can provide a theoretical and practical foundation for electricity demand prediction, personalized marketing, and the development planning of the power industry.

With the increasingly fierce market competition, only by relying on high-quality products and high customer satisfaction can enterprises survive in the fierce competition. Among many evaluation methods, Data Envelopment Analysis (DEA), as a non-parametric statistical method to effectively deal with multi-input and multi-output problems, has received more and more attention in evaluating the relative efficiency of decision-making units. In the process of bank efficiency evaluation based on DEA method, there will be a situation that banks have both dual role factors and unexpected output factors. The Two-stage DEA model provides an effective analysis method to solve the problem of bank efficiency evaluation of complex organizational structure. In order to evaluate the efficiency of unexpected output with uncertain information, a stochastic DEA model of unexpected output is established.

The aims are to unify big data management among various departments in smart city construction, establish a centralized data collection and sharing system, and provide fast and convenient big data services for smart applications in various fields. The national grid industry is taken as the research object. A new electricity demand prediction model is proposed based on smart city big data’s characteristics. This model integrates meteorological, geographic, demographic, corporate, and economic information to form an intelligent big database. The K-mean algorithm mines and analyzes the data to optimize the electricity user information. The electricity prediction model is established using the Backpropagation (BP) neural network algorithm. The electricity market is evaluated through an in-depth exploration of data relationships to verify the effectiveness of the model proposed. Results demonstrate that the K-mean algorithm can significantly improve electricity user segmentation accuracy, separate the different regional electricity consumption, and categorize different electricity users. The electricity demand network model constructed can significantly improve the prediction accuracy, and the mean error rate is 3.2671%. The model’s training time improved by the additional momentum factor is significantly reduced, and the mean error rate is 2.13%. The above results can provide a theoretical and practical basis for electricity demand prediction and personalized marketing, as well as development planning for the electricity sector.

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