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.

Trajectory planning is essential for ensuring the safe operation of autonomous vehicles. However, existing methods rarely consider the vehicle’s multi-coupled dynamics, including lateral-longitudinal motion coupling, tire force coupling, and lateral instability. This omission can result in infeasible trajectories, vehicle instability, or even accidents under extreme conditions. To address this challenge, this study presents a multi-coupled dynamics trajectory planning (MCTP) scheme. MCTP establishes a coupled kinematics model to accurately represent vehicle motion states and constructs a tire force representation model, which based solely on vehicle motion states, facilitating seamless integration into trajectory planning. By incorporating coupled tire force characteristics and lateral stability analysis, a set of coupled dynamic constraints is formulated to ensure trajectory feasibility and lateral stability. Additionally, a multi-objective function is designed to further optimize trajectory safety, dynamic feasibility, and lateral stability, with the optimal trajectory obtained through receding horizon optimization. Closed-loop validation on both hardware-in-the-loop and real-vehicle experimental platforms demonstrates that, MCTP generates trajectories with enhanced safety and feasibility. It also improves tracking stability margins and dynamics performance, highlighting its effectiveness in handling extreme conditions.

In highly dynamic and complex autonomous driving environments, accurately predicting agents’ future multimodal trajectories still faces challenges such as modeling diverse social interactions, capturing dynamic intents, and ensuring prediction consistency. To address these issues, this paper proposes a novel trajectory prediction model that integrates a Hierarchical Recursive Interaction Network (HRINet) and a multi-stage goal-guided mechanism (GoalNet), aiming to improve prediction accuracy, stability, and plausibility. Specifically, we design a HRINet with local and global attention mechanisms to recursively model various social interactions, while progressively integrating map semantic information to enhance the model’s understanding of traffic scenes. Meanwhile, inspired by the divide-and-conquer approach, the proposed GoalNet first estimates fine-grained multi-stage goal lane segments along the path. These goals are then used to continuously guide and constrain the trajectory generation process, effectively reducing error accumulation and improving stability. In addition, we construct a dynamic goal candidate area that combines domain knowledge and traffic rules to filter out unreasonable goals, thereby enhancing the plausibility and consistency of the predictions. Experimental results on nuScenes, INTERACTION, and Waymo Open Motion Dataset (WOMD) show that our model achieves state-of-the-art performance in multiple key metrics, maintains a trade-off between prediction accuracy, model complexity, and inference latency, and shows high stability and consistency in predictions.

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.

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.

The optical electronic nose (e-nose) is drawing more and more attention from the academia and industry in Internet of Things applications. Since the charge-coupled Device (CCD)-based portable spectrometer is commonly used in gas sensing system of e-nose, the baseline drift interference is easy to be introduced, which reduces the accuracy and sensitivity of the system and makes the sensing performance worse. For this issue, first, the baseline drift data are analyzed and the characteristics of the baseline drift interference brought by the portable spectrometer are found in this article. Then, according to the characteristics of the drift, a baseline drift suppression technique for portable spectrometer is proposed. Specifically, it mainly includes four steps: data preprocessing, dimension reduction using principal component analysis, sources separation using independent component analysis, and reconstruction after interference suppression. Finally, experimental results show that the proposed technique suppressed the interference effectively and even better compared with classical existing methods.

This paper investigates the lane keeping control of autonomous ground vehicles (AGVs) considering the rollover prevention and input saturation. An enhanced state observer-based sliding mode control (SMC) strategy is proposed to achieve the control purpose and maintain the lane keeping errors as well as the roll angle within the prescribed performance boundaries. Three contributions are made in this paper. First, a prescribed performance function (PPF) is proposed in the controller design, aiming to implement the error transformation so as to constrain the controlled variables within the prescribed performance boundaries. Second, a modified sliding surface is developed incorporating two nonlinear functions, whose specialities and benefits are taken advantage of: one is a barrier function to restrict the load transfer ratio (LTR) in a safe boundary to guarantee the roll stability; another is a monotonely decreasing function to adaptively change the damping ratio of the closed-loop system to improve the transient performance, including reducing the transient overshoots and steady-state errors. Third, a modified multivariable adaptive SMC controller is proposed to achieve the integrated lane-keeping and roll control in the presence of the input saturation and bound-unknown disturbances. The stability of the closed-loop system is rigorously proved via the Lyapunov function. Finally, the effectiveness of the proposed control strategy is verified with a high-fidelity and full-car model via the CarSim platform.