scholarly journals Platoon Route Optimization for Picking up Automated Vehicles in an Urban Network

2018 ◽  
Author(s):  
Mohamed Hadded ◽  
Jean-Marc Lasgouttes ◽  
Fawzi Nashashibi ◽  
Ilias Xydias
Keyword(s):  
Route Optimization ◽  
Automated Vehicles ◽  
Urban Network

Automated Vehicles Questionnaire

2018 ◽  
Author(s):  
Timo Liljamo ◽  
Heikki Liimatainen ◽  
Markus Pöllänen
Keyword(s):  
Automated Vehicles

Route Optimization of Construction Machine by Deep Reinforcement Learning

2019 ◽  
Vol 139 (4) ◽  
pp. 401-408
Author(s):  
Shunya Tanabe ◽  
Zeyuan Sun ◽  
Masayuki Nakatani ◽  
Yutaka Uchimura
Keyword(s):  
Reinforcement Learning ◽  
Route Optimization ◽  
Construction Machine

The Arrival of Automated Vehicles and Changes in Civil Liability

2017 ◽  
Vol 86 ◽  
pp. 361-411
Author(s):  
Jewoo Lee ◽  
Soon-Koo MYOUNG
Keyword(s):  
Civil Liability ◽  
Automated Vehicles

Cross-provincial Cross-region Power Trading Optimization Modeling

2020 ◽  
Vol 13 (8) ◽  
pp. 1183-1189
Author(s):  
Jing-wen Chen ◽  
Yan Xiao ◽  
Hong-she Dang ◽  
Rong Zhang
Keyword(s):  
Network Flow ◽  
Optimal Allocation ◽  
Route Optimization ◽  
Regional Power ◽  
Power Resources ◽  
Gray Theory ◽  
Power Trading ◽  
Study Results ◽  
Multi Agent ◽  
Regional Economic Disparities

Background: China's power resources are unevenly distributed in geography, and the supply-demand imbalance becomes worse due to regional economic disparities. It is essential to optimize the allocation of power resources through cross-provincial and cross-regional power trading. Methods: This paper uses load forecasting, transaction subject data declaration, and route optimization models to achieve optimal allocation of electricity and power resources cross-provincial and cross-regional and maximize social benefits. Gray theory is used to predict the medium and longterm loads, while multi-agent technology is used to report the power trading price. Results: Cross-provincial and cross-regional power trading become a network flow problem, through which we can find the optimized complete trading paths. Conclusion: Numerical case study results has verified the efficiency of the proposed method in optimizing power allocation across provinces and regions.

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