In the complex supply chain logistics environment, enterprises face core challenges such as multi-commodity transportation, multi-trip distribution planning, and supply chain stability maintenance. However, existing studies often fail to fully balance workload allocation equilibrium and dynamic coordination of supply chain Safety Inventory (SI) when addressing multi-commodity and multi-trip distribution issues, leading to practical operational problems such as cost fluctuations, low distribution efficiency, and inventory imbalance. Thus, achieving workload balance, optimizing vehicle routes, and setting appropriate SI are crucial for improving supply chain efficiency and stability. This study constructs a multi-commodity and multi-trip supply chain logistics vehicle routing optimization model considering SI and Workload Balance Constraint (WBC), and uses an Improved Ant Colony Optimization (IACO) for solution. Internet of Things (IoT) technology enables real-time inventory monitoring, data collection, and demand forecasting using historical data, supporting intelligent decision-making. SI is critical for supply chain stability; its proportion positively correlates with optimal cost, path length, and vehicle count, with specific dynamics analyzed in simulations. The WBC reduces total costs and overtime expenses, with larger scale scenarios (70 collection points) showing the most significant improvement (13.09% cost reduction). Taking some areas in Runan County, Zhumadian City as examples, the results verify the model’s comprehensiveness and the algorithm’s effectiveness.

Given the transport efficiency of large vehicles in urban environments, an increasing number of enterprises are adopting the factory-warehouse-store transportation model. To address the limitations of previous models in practical transport operations, this study formulates a Two-Echelon Vehicle Routing Problem with Simultaneous Pickup and Delivery for Multi-Commodity. Furthermore, the model uniquely emphasizes certain considerations, such as prioritizing product distribution based on production batches and allowing vehicles to engage in flexible product redistribution. Considering the computational challenges arising from the substantial data involved in real-world problem instances, a hybrid heuristic algorithm is proposed. Initially, a hybrid Fuzzy C-Means is employed to decompose the problem by clustering chain stores, effectively reducing the solution space. Subsequently, an enhanced Multi-Population Genetic Algorithm, integrated with Variable Neighborhood Search, is introduced to solve the decomposed sub-problems. Experimental validation conducted with a food enterprise located in Zhengzhou, China, provides empirical support for the efficacy of the proposed model. Multiple test scenarios further illustrate the superior performance of the proposed algorithm. This study holds significant practical and theoretical implications, offering insights to aid decision-makers in reducing transportation costs and advancing the development and application of Vehicle Routing Problem models.

In the process of goods delivery, it is often necessary to split and deliver (pick-up and delivery) multiple products from multiple orders. How to economically and reasonably formulate vehicle delivery routes is a challenge. Firstly, the order splitting and vehicle delivery path formulation was analyzed from the perspective of enterprises. Meanwhile, the impact of order splitting and delivery on customer satisfaction was analyzed from the perspective of consumers. Based on the above analysis, a multi-objective optimization model was established with the objectives of minimizing costs and maximizing customer satisfaction. Then, an improved NSGA-II algorithm was proposed to solve the model. In this algorithm, K-means and ant colony algorithm were used to obtain the optimal paths for two objectives, respectively. Then, the customer satisfaction problem caused by splitting was quantified using the information entropy TOPSIS method. Subsequently, the improved VNS algorithm was used to expand the solution sets of the two optimal paths for dual objective optimization. The experimental results show that the algorithm obtained Pareto front and achieved ideal experimental results.

The implementation of Vehicle-to-Grid technology enables bidirectional communication and power flow in the Internet of Electric Vehicles (IoEV) context, facilitating the extensive application of electric vehicles in the logistics industry. In response to escalating urban traffic congestion, simultaneously, many cities have implemented widespread traffic restriction policies. Scientifically optimizing the charging strategies for electric logistics fleets and formulating rational distribution plans are pivotal pathways for developing more efficient and intelligent IoEV. To address the problem, an Electric Vehicle Routing Problem of heterogeneous fleet with time window under traffic constraints is formulated, featuring strategies for demand-driven charging within the IoEV and staggered traffic restriction periods. Given the intricate nature of this mathematical model, it is divided into two subproblems, from which two integer programming models are derived. To tackle this model, a two-tier optimization approach is employed, and an improved Ant Colony Optimization algorithm integrated with Variable Neighborhood Search is proposed. Experimental results show that the proposed model reduces the cost by 9.80%-15.68%, confirming the effectiveness of the proposed charging and staggered traffic restriction strategies, as well as the influence of different traffic restriction factors. This research holds practical and theoretical significance in aiding local governments in formulating rational traffic restriction policies, assisting businesses in effectively reducing the costs of electric logistics fleets, and advancing the development of IoEVs.

Suffering from environmental distress like carbon emissions, traffic restrictions have been enforced extensively in distribution logistics. Reasonable arrangement of urban freight transportation can effectively improve distribution efficiency, reduce distribution costs, and alleviate the impact of traffic restrictions in distribution logistics. In response to increasingly stringent traffic restrictions, we establish a multi-objective optimization model, including the minimum distributions and the minimum carbon emissions. Given that the limits of battery capacity and cargo capacity, we build a green vehicle routing problem with soft time windows (GVRPTW) model with heterogenous fleets. In this study, three different factors, that is restricted area, travel time of vehicles, and carbon tax prices, are discussed in details. In order to solve the NP-hard model, we propose an improved ant colony optimization algorithm (IACO) by optimizing the state transition probability, and verifies the worth of the algorithm. The experimental results can explore the impacts of traffic restriction policies on the formulation of distribution scheme and offer reference opinions for the government to formulate reasonable restriction policies and better guide logistics enterprises to reduce carbon emissions.

With the construction of smart cities and the continuous improvement of people's living standards, residents’ demand for fresh agricultural products (FAPs) has increased dramatically. Therefore, reasonable arrangement for intelligent distribution of FAP in smart cities can effectively guarantee product quality, improve distribution efficiency, reduce distribution cost, and increase customer satisfaction. In actual distribution in smart city, road conditions are one of the important factors that affect the distribution. Therefore, according to the influence of road conditions on refrigerated vehicle's (RV's) speed, the RV's speed characteristic models are established. Meanwhile, according to the characteristics of FAP, the penalty cost function based on the time window is constructed. According to the idea of fuzzy logic, the customer satisfaction evaluation model is established. Then, in order to minimize the distribution costs and maximize customer satisfaction as the optimization goal of intelligent distribution in smart city, the mathematical model is built. For solving this model, an improved quantum-behaved particle swarm optimization algorithm (IQPSO) is proposed. Finally, the effectiveness of IQPSO is verified by simulation. The results show that IQPSO also achieves good results, and the model constructed can effectively balance the relationship between the distribution costs and customer satisfaction when distributing FAP in smart city.

With the advance of artificial intelligence and communication technology in the smart city, various location-based data of users can be collected via location-based social networks (LBSNs). How to make full use of these data for accurate point-of-interest (POI) recommendation is challenging because POI selection is influenced by various factors. In this article, we propose a network representation learning-enhanced multisource information (MSI) fusion model for POI recommendation in the context of LBSNs. The proposed model jointly considers various factors, including user preference, geographical influence, and social influence for a recommendation. Specifically, the social influence is modeled by performing network representation learning methods on the constructed co-visiting user networks so that the hidden complex social relationships among users can be measured automatically. Moreover, considering the significance of user preference and geographical influence, a fusion model is designed to jointly consider user preference, social influence, and geographical influence for POI recommendation. Our method is evaluated based on two publicly available data sets and extensive experimental results demonstrate that the proposed MSI fusion model outperforms several state-of-the-art algorithms for POI recommendation in terms of precision, recall, and F1.

To reduce carbon footprint, a hybrid energy powered cellular network (HybE-Net) in the Internet-of-Things (IoT) environment is widely sought after. Different from cellular network powered on-grid energy, the base station (BS) wakeup in HybE-Net needs to consider the solar energy of the BS and the traffic load in the network. Thus, in this article, a fuzzy logic-based wakeup strategy is proposed, which comprehensively considers the energy wakeup level and the available network resource ratio. Then, the solar energy states are analyzed mathematically by using the diffusion approximation method. Finally, to prevent BSs from switching frequently between the sleeping and nonsleeping model, the awakening threshold is optimized by the penalty function method. The simulation results demonstrate that in the proposed wakeup strategy, the wakeup threshold based on the energy state is used to avoid the phenomenon of frequent handoff of the BSs, resulting in fewer handoffs. The proposed BS wakeup strategy can be further applied to both the current and sixth-generation (6G) mobile communication networks, which will be powered by other forms of renewable energy and on-grid energy in the future.

Nowadays, the issue of information overload is gradually gaining exposure in the Internet of Things (IoT), calling for more research on recommender system in advance for industrial IoT scenarios. With the ever-increasing prevalence of various social networks, social recommendations (SoR) will certainly become an integral application that provides more feasibly personalized information service for future IoT users. However, almost all of the existing research managed to explore and quantify correlations between user preferences and social relationships, while neglecting the correlations among item features which could further influence the topologies of some social groups. To tackle with this challenge, in this article, a deep graph neural network-based social recommendation framework (GNN-SoR) is proposed for future IoTs. First, user and item feature spaces are abstracted as two graph networks and respectively encoded via the graph neural network method. Next, two encoded spaces are embedded into two latent factors of matrix factorization to complete missing rating values in a user-item rating matrix. Finally, a large amount of experiments are conducted on three real-world data sets to verify the efficiency and stability of the proposed GNN-SoR.

Corners, highly important local features of images and corner finding, play a crucial role in computer vision and image processing, such as object tracking and vehicle detection. Proposing effective and efficient corner detectors is the aim of corner detection. In this study, the authors first present a new measure of corner sharpness termed as the point‐to‐centroid distance (PCD) and then examine its behaviours, which display beneficial characteristics that help distinguish corners from non‐corners. Based on PCD behaviours, the authors propose a novel corner detector. Extensive experimental results demonstrate that the PCD technique is effective and simultaneously efficient for corner detection compared with six other contour‐based corner detectors in terms of two commonly used evaluation metrics – average repeatability and localisation error.

To reduce the fossil energy consumption and the operational expenses, the hybrid energy powered cellular network (HybENet) with BSs powered by on-grid or renewable energy is studied. To minimize the total expenses of energy consumption (EEC) and guarantee the quality of service of HybENet, a liquid cell management algorithm, which adaptively and cooperatively adjusts the service coverage of BSs according to the actual load, is proposed. Specifically, the problem of minimizing the total EEC under constraints of the fluctuating arrivals of the renewable energy and the QoS is formulated as a combinatorial optimization problem. Then, we prove that the problem can be decomposed into two sub-problems: 1) the mean of per-link energy minimization problem, from which the closed expression of power allocation is derived; 2) the traffic block assignment problem, which is solved by ant colony optimization. Simulation results show that the total EEC can be effectively reduced.