scholarly journals Research on E-commerce Logistics Distribution Path Planning Based on Improved Genetic Algorithm

2022 ◽  
Vol 16 ◽  
pp. 202-208
Author(s):  
Lan Lan
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Rapid Development ◽  
Optimal Path ◽  
Route Planning ◽  
Planning Method ◽  
Improved Genetic Algorithm ◽  
Logistics Distribution ◽  
Centralized Planning ◽  
Distribution Efficiency

With the rapid development of the Internet, e-commerce business has gradually emerged. However, its logistics distribution route planning method has problems such as redundancy of logistics data, which cannot achieve centralized planning of distribution paths, resulting in low e-commerce logistics distribution efficiency and long distribution distances, higher cost. Therefore, in order to improve the ability of logistics distribution path planning, this paper designs an e-commerce logistics distribution path planning method based on improved genetic algorithm. Optimize the analysis of e-commerce logistics distribution nodes, establish a modern logistics distribution system, and optimize the total transportation time and transportation cost under the location model of the logistics distribution center. Using hybrid search algorithm and improved genetic algorithm parameters, an improved genetic algorithm distribution path planning model is established to select the optimal path of logistics distribution, and realize e-commerce logistics distribution path with high accuracy, low error and good convergence. planning. According to the experimental results, the method in this paper can effectively shorten the distance of e-commerce logistics distribution path, reduce the number of distribution vehicles, reduce distribution costs, improve distribution efficiency, and effectively achieve centralized planning of logistics distribution. Therefore, the e-commerce logistics distribution route planning method based on improved genetic algorithm has high practical application value.

scholarly journals Research on the Food Logistic Optimal Path Planning Method of Improving the Genetic Algorithm

2016 ◽  
Vol 11 (4) ◽  
pp. 269-273
Author(s):  
Li Si ◽  
Wang Yuan ◽  
Li Xinzhong ◽  
Liu Shenyang ◽  
Li Zhen
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Optimal Path ◽  
Planning Method ◽  
Optimal Path Planning

scholarly journals Selection of Cold Chain Logistics Distribution Center Location Based on Improved Genetic Algorithm

2018 ◽  
Vol 227 ◽  
pp. 02018
Author(s):  
Jianchang Lu ◽  
Yaxin Zhao
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Rapid Development ◽  
Cold Chain ◽  
Distribution Model ◽  
Improved Genetic Algorithm ◽  
Actual Case ◽  
Logistics Industry ◽  
Penalty Cost ◽  
Logistics Distribution

With the rapid development of food refrigeration and freezing technology, food cryogenic storage and vehicle transportation scheduling technology, the cold chain logistics industry has entered a period of rapid development. According to the problem of urban cold chain distribution route, based on the vehicle distribution model with time window, the minimum cost of transportation, cost of energy, cost of goods, penalty cost is the objective function, and the urban cold chain logistics distribution path is established. Optimized mathematical model. According to the actual case, the analysis of the cold chain distribution model is carried out by using the analytical genetic algorithm, and the optimal combination of the distribution paths with the lowest total cost is obtained, which has certain reference significance for the urban cold chain logistics distribution route problem.

A novel evacuation path planning method based on improved genetic algorithm

2021 ◽  
pp. 1-11
Author(s):  
Longzhen Zhai ◽  
Shaohong Feng
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Path Planning ◽  
Simulated Annealing Algorithm ◽  
Planning Method ◽  
Improved Genetic Algorithm ◽  
Genetic Operator ◽  
Probability Operator ◽  
Annealing Algorithm ◽  
Evacuation Route

In order to solve the problem of finding the best evacuation route quickly and effectively, in the event of an accident, a novel evacuation route planning method is proposed based on Genetic Algorithm and Simulated Annealing algorithm in this paper. On the one hand, the simulated annealing algorithm is introduced and a simulated annealing genetic algorithm is proposed, which can effectively avoid the problem of the search process falling into the local optimal solution. On the other hand, an adaptive genetic operator is designed to achieve the purpose of maintaining population diversity. The adaptive genetic operator includes an adaptive crossover probability operator and an adaptive mutation probability operator. Finally, the path planning simulation verification is carried out for the genetic algorithm and the improved genetic algorithm. The simulation results show that the improved method has greatly improved the path planning distance and time compared with the traditional genetic algorithm.

scholarly journals Multi-UAV cooperative Route planning based on decision variables and improved genetic algorithm

2021 ◽  
Vol 1941 (1) ◽  
pp. 012012
Author(s):  
Jie Zhang ◽  
Ningzhou Li ◽  
Danyu Zhang ◽  
Xiaojuan Wei ◽  
Xiaojuan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Route Planning ◽  
Improved Genetic Algorithm ◽  
Decision Variables ◽  
Multi Uav

Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment

2021 ◽  
pp. 095441002110073
Author(s):  
Zhe Zhang ◽  
Jian Wu ◽  
Jiyang Dai ◽  
Cheng He
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Optimal Path ◽  
Radar System ◽  
Planning Method ◽  
Optimal Path Planning ◽  
Aerial Vehicles ◽  
3D Network ◽  
Planning Scheme ◽  
Estimation Function

For stealth unmanned aerial vehicles (UAVs), path security and search efficiency of penetration paths are the two most important factors in performing missions. This article investigates an optimal penetration path planning method that simultaneously considers the principles of kinematics, the dynamic radar cross-section of stealth UAVs, and the network radar system. By introducing the radar threat estimation function and a 3D bidirectional sector multilayer variable step search strategy into the conventional A-Star algorithm, a modified A-Star algorithm was proposed which aims to satisfy waypoint accuracy and the algorithm searching efficiency. Next, using the proposed penetration path planning method, new waypoints were selected simultaneously which satisfy the attitude angle constraints and rank-K fusion criterion of the radar system. Furthermore, for comparative analysis of different algorithms, the conventional A-Star algorithm, bidirectional multilayer A-Star algorithm, and modified A-Star algorithm were utilized to settle the penetration path problem that UAVs experience under various threat scenarios. Finally, the simulation results indicate that the paths obtained by employing the modified algorithm have optimal path costs and higher safety in a 3D complex network radar environment, which show the effectiveness of the proposed path planning scheme.

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