KMG:考虑逆向物流的无人机路径规划策略研究
KMG:Study on UAV path planning strategy by considering reverse logistics
查看参考文献27篇
|
文摘
|
物流领域无人机派送正成为一种快捷高效的派件方式和应用热点.针对于正向、逆向的物流数据,无人机派送是国内外大型物流企业实施高效物流派送的重要手段.本文提出了一种融合拓展性K-Means++算法和遗传算法的路径动态规划模型(KMG),实现包含逆向物流的无人机调度策略.KMG模型将逆向物流路径融入正向物流路径之中,采用加权聚类算法确定不同属性包裹所需派送无人机的最小数量.在每一簇坐标数据的连通图中,采用遗传算法求解TSP问题,并对可行解进行编码,最终求解出最小欧拉回路.在仿真实验中,KMG模型比独立逆向物流派送的成本减少20.08%,使用拓展性K-Means++聚类计算的时间比传统K-Means算法缩短了298.85%. |
|
其他语种文摘
|
Delivery by UAV in the logistics field is becoming a fast and efficient dispatch method and application hotspot.For forward and reverse logistics data,drone dispatch is an important means for large-scale logistics enterprises at home and abroad to implement efficient logistics delivery.A path dynamic programming model (KMG) that integrates the scalable K-Means++ algorithm and genetic algorithm to implement a UAV scheduling strategy including reverse logistics.The KMG model integrates the reverse logistics path into the forward logistics path,using weighted clustering.The algorithm determines the minimum number of dispatched drones required for different attribute packages.For each connected graph of coordinate data,the genetic algorithm is used to solve the TSP problem and the feasible solution is coded,and finally the minimum Euler loop is solved.The simulation results show that the cost of the KMG model is 20.08% lower than that of the independent reverse logistics.The time of using the scalable K-Means++ clustering calculation is 298.85% shorter than the traditional K-Means algorithm. |
|
来源
|
系统工程理论与实践
,2019,39(12):3111-3119 【核心库】
|
|
DOI
|
10.12011/1000-6788-2018-0679-09
|
|
关键词
|
无人机
;
逆向物流
;
拓展性K-Means++
;
遗传算法
;
路径规划
|
|
地址
|
1.
复旦大学管理学院, 上海, 200433
2.
上海理工大学光电信息与计算机工程学院, 上海, 200093
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1000-6788 |
|
学科
|
数学 |
|
基金
|
上海市浦江人才
;
中国博士后科学基金
;
国家自然科学基金重点项目
;
国家自然科学基金面上项目
;
上海市自然科学基金
|
|
文献收藏号
|
CSCD:6697613
|
参考文献 共
27
共2页
|
|
1.
Lin C E. Airspace risk assessment in logistic path planning for UAV.
Integrated Communications,Navigation and Surveillance Conference (ICNS),2017:1-9
|
CSCD被引
1
次
|
|
|
|
|
2.
Doherty P. A UAV search and rescue scenario with human body detection and geolocalization.
Australian Conference on Artificial Intelligence,2007:1-13
|
CSCD被引
1
次
|
|
|
|
|
3.
Remy M A. The first UAV-based P-and X-band interferometric SAR system.
2012 IEEE International Geoscience and Remote Sensing Symposium,2012:5041-5044
|
CSCD被引
2
次
|
|
|
|
|
4.
Kim J. On the concerted design and scheduling of multiple resources for persistent UAV operations.
Journal of Intelligent Robotic Systems,2014,74:479-498
|
CSCD被引
3
次
|
|
|
|
|
5.
Song B D. Towards real time scheduling for persistent UAV service:A rolling horizon MILP approach,RHTA and the STAH heuristic.
Unmanned Aircraft Systems,2014:506-515
|
CSCD被引
1
次
|
|
|
|
|
6.
Song B D. Persistent UAV service:An improved scheduling formulation and prototypes of system components.
Journal of Intelligent & Robotic Systems,2013,74(1/2):221-232
|
CSCD被引
3
次
|
|
|
|
|
7.
El-Sayed M. A stochastic model for forward-reverse logistics network design under risk.
Computers & Industrial Engineering,2010,58(3):423-431
|
CSCD被引
8
次
|
|
|
|
|
8.
Kannan D. A carbon footprint based reverse logistics network design model.
Resources Conservation and Recycling,2012,67:75-79
|
CSCD被引
14
次
|
|
|
|
|
9.
Alumur S A. Multi-period reverse logistics network design.
European Journal of Operational Research,2012,220(1):67-78
|
CSCD被引
9
次
|
|
|
|
|
10.
Yu H. Improving the decision-making of reverse logistics network design part I:A MILP model under stochastic environment.
Advanced Manufacturing and Automation VII,2018:431-438
|
CSCD被引
1
次
|
|
|
|
|
11.
Zokaee S. Robust supply chain network design:An optimization model with real world application.
Annals of Operations Research,2014,257(1/2):15-44
|
CSCD被引
3
次
|
|
|
|
|
12.
Eskandarpour M. A large neighborhood search heuristic for supply chain network design.
Computers & Operations Research,2017,80:23-37
|
CSCD被引
1
次
|
|
|
|
|
13.
Govindan K. fuzzy multi-objective optimization model for sustainable reverse logistics network design.
Ecological Indicators,2016,67:753-768
|
CSCD被引
5
次
|
|
|
|
|
14.
Sun X. Analysis and design of the logistics system for rope manufacturing plant.
MATEC Web of Conferences,2017:139
|
CSCD被引
1
次
|
|
|
|
|
15.
Choi S G. 3D-based UAV path-planning algorithm considering altitude and reconnaissance areas.
International Journal of Transportation and Logistics Management,2017,1(1):9-16
|
CSCD被引
1
次
|
|
|
|
|
16.
Yang J F. Traffic detection system based on unmanned aerial vehicle integrated analysis (UAVIA) in e-business logistics.
IEEE International Conference on E-business Engineering,2015
|
CSCD被引
1
次
|
|
|
|
|
17.
Rana K. Unmanned aerial vehicles (UAVs):An emerging technology for logistics.
International Journal of Business and Management Invention,2016,5(5):86-92
|
CSCD被引
1
次
|
|
|
|
|
18.
Bahmani B. Scalable k-means++.
Proceedings of the VLDB Endowment,2012,5(7):622-633
|
CSCD被引
24
次
|
|
|
|
|
19.
Jain A K. Data clustering:50 years beyond K-means.
Pattern Recognition Letters,2010,31(8):651-666
|
CSCD被引
357
次
|
|
|
|
|
20.
Cui X L. Optimized big data K-means clustering using MapReduce.
The Journal of Supercomputing,2014,70(3):1249-1259
|
CSCD被引
14
次
|
|
|
|
|
了解气象卫星更多信息，请访问