In the context of Industry 4.0 and based on the demand of digital logistics construction of industrial enterprises, this paper integrates the concept of digital twin into Refined Logistics Supply Chain construction. Considering the constraints of multi-distribution center, heterogeneous vehicle performance, distribution cost, quasi-shipment certificate and humanized management, Refined Logistics Supply Chain System (RLSCS) and cross-regional scheduling optimization model of logistics vehicles with multidistribution center were established. The designed model can minimize the transportation cost, reduce the transportation time, and improve the vehicle load rate. An adaptive elite honey badger target algorithm based on cubic mapping mechanism (IHBA), is designed to solve the model. Further, performance evaluation by optimizing test functions, the convergence performance of IHBA algorithm was demonstrated. Finally, the simulation experiment is carried out according to the actual business data and it is compared to eight other optimistic algorithms. The experimental results show that the proposed algorithm is more effective and more robust, and the related models and algorithms can provide research basis for industrial products digital supply chain system.INDEX TERMS Industry 4.0, refined supply chain, multi-objective constraint, logistics distribution, honey badger algorithm, cubic mapping mechanism.
Unmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact costs. A kinematics and dynamics model of a quadcopter UAV is established, and the UAV’s flight state is analyzed. Due to the difficulties in addressing 3D UAV kinematic constraints and poor uniformity using traditional optimization algorithms, a lightning search algorithm (LSA) based on multi-layer nesting and random walk strategies (MNRW-LSA) is proposed. The convergence performance of the MNRW-LSA algorithm is demonstrated by comparing it with several other algorithms, such as the Golden Jackal Optimization (GJO), Hunter–Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA), and the Golden Eagle Optimization (GEO) using optimization test functions, Friedman and Nemenyi tests. Additionally, a greedy strategy is added to the Rapidly-Exploring Random Tree (RRT) algorithm to initialize the trajectories for simulation experiments using a 3D city model. The results indicate that the proposed algorithm can enhance global convergence and robustness, shorten convergence time, improve UAV execution coverage, and reduce energy consumption. Compared with other algorithms, such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), and LSA, the proposed method has greater advantages in addressing multi-UAV trajectory planning problems.
To solve the problem of "first-kilometer" distribution difficulties in rural areas, we propose a transportation method using unmanned aerial vehicles (UAVs) for delivery. The mountainous environment of Fengshan County in Guizhou is first simulated as the UAV delivery environment. A differential evolution strategy based on the improved whale optimization algorithm (DEIWOA) combined with multisource heterogeneous sensors is then proposed to solve the UAV obstacle avoidance path. After the UAV's delivery path is planned using the DEIWOA algorithm, the multisource heterogeneous sensor is used to perform obstacle avoidance among multiple UAVs and path correction of UAVs in actual situations. Afterwards, to minimize the delivery cost of UAVs, a multi-UAV cargo delivery model is built with the optimization goal of minimizing the transportation cost and time window violation cost. This UAV scheduling model is solved using the proposed DEIWOA algorithm. Finally, simulations are performed to compare the proposed method with the cutting-edge algorithms. The obtained results show that the proposed DEIWOA algorithm can provide a better plan of the UAV path and reduce the cost of logistics scheduling. It can also provide support for UAV logistics and distribution in mountainous areas in actual situations.
In the context of global novel coronavirus infection, we studied the distribution problem of nucleic acid samples, which are medical supplies with high urgency. A multi-UAV delivery model of nucleic acid samples with time windows and a UAV (Unmanned Aerial Vehicle) dynamics model for multiple distribution centers is established by considering UAVs’ impact cost and trajectory cost. The Golden Eagle optimization algorithm (SGDCV-GEO) based on gradient optimization and Corsi variation is proposed to solve the model by introducing gradient optimization and Corsi variation strategy in the Golden Eagle optimization algorithm. Performance evaluation by optimizing test functions, Friedman and Nemenyi test compared with Golden Jackal Optimization (GJO), Hunter-Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA) and Golden Eagle Optimization (GEO), the convergence performance of SGDCV-GEO algorithm was demonstrated. Further, the improved RRT (Rapidly-exploring Random Trees) algorithm is used in the UAV path planning, and the pruning process and logistic chaotic mapping strategy are introduced in the path generation method. Finally, simulation experiments are conducted based on 8 hospitals and 50 randomly selected communities in the Pudong district of Shanghai, southern China. The experimental results show that the developed algorithm can effectively reduce the delivery cost and total delivery time compared with simulated annealing algorithm (SA), crow search algorithm (CSA), particle swarm algorithm (PSO), and taboo search algorithm (TS), and the developed algorithm has good uniformity, robustness, and high convergence accuracy, which can be effectively applied to the multi-UAV nucleic acid sample delivery path optimization in large cities under the influence of an epidemic environment.
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