Solution of an Optimal Routing Problem by Reinforcement Learning with Generalization Ability

2019 ◽  
Vol 139 (12) ◽  
pp. 1494-1500
Author(s):  
Hitoshi Iima ◽  
Hiroya Oonishi
Keyword(s):  
Reinforcement Learning ◽  
Optimal Routing ◽  
Routing Problem ◽  
Generalization Ability

scholarly journals The optimal routing problem in Vietnam telecommunication network.

2012 ◽  
Vol 17 (1) ◽  
Author(s):  
Đỗ Trung Tá ◽  
Lê Văn Phùng ◽  
Lê Đắc Kiên
Keyword(s):  
Telecommunication Network ◽  
Optimal Routing ◽  
Routing Problem

scholarly journals A Deep Reinforcement Learning Approach for Global Routing

2019 ◽  
Vol 142 (6) ◽  
Author(s):  
Haiguang Liao ◽  
Wentai Zhang ◽  
Xuliang Dong ◽  
Barnabas Poczos ◽  
Kenji Shimada ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Integrated Circuits ◽  
Printed Circuit Boards ◽  
Greedy Algorithms ◽  
Global Routing ◽  
Hydraulic Systems ◽  
Routing Problem ◽  
Future Data ◽  
Circuit Components ◽  
Optimization Mechanism

Abstract Global routing has been a historically challenging problem in the electronic circuit design, where the challenge is to connect a large and arbitrary number of circuit components with wires without violating the design rules for the printed circuit boards or integrated circuits. Similar routing problems also exist in the design of complex hydraulic systems, pipe systems, and logistic networks. Existing solutions typically consist of greedy algorithms and hard-coded heuristics. As such, existing approaches suffer from a lack of model flexibility and usually fail to solve sub-problems conjointly. As an alternative approach, this work presents a deep reinforcement learning method for solving the global routing problem in a simulated environment. At the heart of the proposed method is deep reinforcement learning that enables an agent to produce a policy for routing based on the variety of problems, and it is presented with leveraging the conjoint optimization mechanism of deep reinforcement learning. Conjoint optimization mechanism is explained and demonstrated in detail; the best network structure and the parameters of the learned model are explored. Based on the fine-tuned model, routing solutions and rewards are presented and analyzed. The results indicate that the approach can outperform the benchmark method of a sequential A* method, suggesting a promising potential for deep reinforcement learning for global routing and other routing or path planning problems in general. Another major contribution of this work is the development of a global routing problem sets generator with the ability to generate parameterized global routing problem sets with different size and constraints, enabling evaluation of different routing algorithms and the generation of training datasets for future data-driven routing approaches.

A deep reinforcement learning-based approach for the home delivery and installation routing problem

2021 ◽  
pp. 108362
Author(s):  
Huaxin Qiu ◽  
Sutong Wang ◽  
Yunqiang Yin ◽  
Dujuan Wang ◽  
Yanzhang Wang
Keyword(s):  
Reinforcement Learning ◽  
Home Delivery ◽  
Routing Problem

scholarly journals A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes

2020 ◽  
Vol 54 (5) ◽  
pp. 1467-1494
Author(s):  
Binhui Chen ◽  
Rong Qu ◽  
Ruibin Bai ◽  
Wasakorn Laesanklang
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Search Algorithm ◽  
Real Life ◽  
Planning Horizon ◽  
Neighborhood Search ◽  
Variable Neighbourhood Search ◽  
Container Transportation ◽  
Solution Quality ◽  
Routing Problem

This paper studies a real-life container transportation problem with a wide planning horizon divided into multiple shifts. The trucks in this problem do not return to depot after every single shift but at the end of every two shifts. The mathematical model of the problem is first established, but it is unrealistic to solve this large scale problem with exact search methods. Thus, a Variable Neighbourhood Search algorithm with Reinforcement Learning (VNS-RLS) is thus developed. An urgency level-based insertion heuristic is proposed to construct the initial solution. Reinforcement learning is then used to guide the search in the local search improvement phase. Our study shows that the Sampling scheme in single solution-based algorithms does not significantly improve the solution quality but can greatly reduce the rate of infeasible solutions explored during the search. Compared to the exact search and the state-of-the-art algorithms, the proposed VNS-RLS produces promising results.

Using GPU and OpenACC to Accelerate the Maze Optimal Routing Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 1338-1341
Author(s):  
Yu Liu ◽  
Yi Xiao
Keyword(s):  
Ant Colony Algorithm ◽  
Programming Model ◽  
Routing Algorithm ◽  
Operation Time ◽  
Ant Colony ◽  
Gpu Acceleration ◽  
Optimal Routing ◽  
Task Mapping ◽  
Routing Problem ◽  
High Level

in order to improve the efficiency of maze optimal routing problem, a GPU acceleration programming model OpenACC is used in this paper. By analyzing an algorithm which solves the maze problem based on ant colony algorithm, we complete the task mapping on the model. Though GPU acceleration, ant colony searching process was changed into parallel matrix operations. To decrease the algorithm accessing overhead and increase operating speed, data were rationally organized and stored for GPU. Experiments of different scale maze matrix show that the parallel algorithm greatly reduces the operation time. Speedup will be increased with the expansion of the matrix size. In our experiments, the maximum speedup is about 6.1. The algorithm can solve larger matrices with a high level of processing performance by adding efficient OpenACC instruction to serial code and organizing the data structure for parallel accessing.

scholarly journals Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach

2021 ◽  
Vol 4 ◽  
Author(s):  
Marina Dorokhova ◽  
Christophe Ballif ◽  
Nicolas Wyrsch
Keyword(s):  
Reinforcement Learning ◽  
Electric Vehicles ◽  
Mathematical Formulation ◽  
Route Planning ◽  
Learning Approach ◽  
Training Procedure ◽  
Routing Problem ◽  
Policy Model ◽  
Model Free ◽  
Charging Stations

In the past few years, the importance of electric mobility has increased in response to growing concerns about climate change. However, limited cruising range and sparse charging infrastructure could restrain a massive deployment of electric vehicles (EVs). To mitigate the problem, the need for optimal route planning algorithms emerged. In this paper, we propose a mathematical formulation of the EV-specific routing problem in a graph-theoretical context, which incorporates the ability of EVs to recuperate energy. Furthermore, we consider a possibility to recharge on the way using intermediary charging stations. As a possible solution method, we present an off-policy model-free reinforcement learning approach that aims to generate energy feasible paths for EV from source to target. The algorithm was implemented and tested on a case study of a road network in Switzerland. The training procedure requires low computing and memory demands and is suitable for online applications. The results achieved demonstrate the algorithm’s capability to take recharging decisions and produce desired energy feasible paths.

scholarly journals A Fuzzy Optimal Routing Problem for Sightseeing

2005 ◽  
Vol 125 (8) ◽  
pp. 1350-1357 ◽  
Author(s):  
Yoshitaka Matsuda ◽  
Morikazu Nakamura ◽  
Dongshik Kang ◽  
Hayao Miyagi
Keyword(s):  
Optimal Routing ◽  
Routing Problem
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