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.

Optical electronic nose (e-nose) are drawing more and more attention from the academia and industry in Internet of Things applications. Since the 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, firstly, the baseline drift data were analyzed and the characteristics of the baseline drift interference brought by the portable spectrometer were found. Then, according to the characteristics of the drift, a baseline drift suppression technique for portable spectrometer was proposed. Specifically, it mainly includes four steps: data preprocessing, dimension reduction using principal component analysis (PCA), sources separation using independent component analysis (ICA) and reconstruction after interference suppression. At last, experimental results show that, the proposed technique suppressed the interference effectively, and even better compared with classical existing methods.

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.

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.

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) wake-up in HybE-Net needs to consider the solar energy of the BS and the traffic load in the network. Thus, in this paper, a fuzzy logic based wake-up strategy is proposed, which comprehensively considers the energy wake-up level and the available network resource ratio. Then, the solar energy states are analysed 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 wake-up strategy, the wake-up 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 wake-up 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.