Simulation-based optimization of radiotherapy: Agent-based modeling and reinforcement learning

2017 ◽  
Vol 133 ◽  
pp. 235-248 ◽  
Author(s):  
Ammar Jalalimanesh ◽  
Hamidreza Shahabi Haghighi ◽  
Abbas Ahmadi ◽  
Madjid Soltani
Keyword(s):  
Reinforcement Learning ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Simulation Based ◽  
Simulation Based Optimization

Identification of Superior Improvement Trajectories for Production Lines via Simulation-Based Optimization with Reinforcement Learning

2021 ◽  
pp. 405-413
Author(s):  
Günther Schuh ◽  
Andreas Gützlaff ◽  
Matthias Schmidhuber ◽  
Jan Maetschke ◽  
Max Barkhausen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Production Lines ◽  
Simulation Based ◽  
Simulation Based Optimization

A distributed agent-based approach for simulation-based optimization

2012 ◽  
Vol 26 (4) ◽  
pp. 814-832 ◽  
Author(s):  
Van Vinh Nguyen ◽  
Dietrich Hartmann ◽  
Markus König
Keyword(s):  
Agent Based ◽  
Simulation Based ◽  
Simulation Based Optimization

scholarly journals Using deep reinforcement learning to speed up collective cell migration

2019 ◽  
Vol 20 (S18) ◽  
Author(s):  
Hanxu Hou ◽  
Tian Gan ◽  
Yaodong Yang ◽  
Xianglei Zhu ◽  
Sen Liu ◽  
...  
Keyword(s):  
Cell Migration ◽  
Reinforcement Learning ◽  
Real Life ◽  
Time Lapse ◽  
Research Process ◽  
Agent Based Modeling ◽  
Collective Cell Migration ◽  
Modeling Framework ◽  
Agent Based ◽  
Number Of Cells

Abstract Background Collective cell migration is a significant and complex phenomenon that affects many basic biological processes. The coordination between leader cell and follower cell affects the rate of collective cell migration. However, there are still very few papers on the impacts of the stimulus signal released by the leader on the follower. Tracking cell movement using 3D time-lapse microscopy images provides an unprecedented opportunity to systematically study and analyze collective cell migration. Results Recently, deep reinforcement learning algorithms have become very popular. In our paper, we also use this method to train the number of cells and control signals. By experimenting with single-follower cell and multi-follower cells, it is concluded that the number of stimulation signals is proportional to the rate of collective movement of the cells. Such research provides a more diverse approach and approach to studying biological problems. Conclusion Traditional research methods are always based on real-life scenarios, but as the number of cells grows exponentially, the research process is too time consuming. Agent-based modeling is a robust framework that approximates cells to isotropic, elastic, and sticky objects. In this paper, an agent-based modeling framework is used to establish a simulation platform for simulating collective cell migration. The goal of the platform is to build a biomimetic environment to demonstrate the importance of stimuli between the leading and following cells.

scholarly journals Simulation-Based and Risk-Informed Assessment of the Effectiveness of Tsunami Evacuation Routes Using Agent-Based Modeling: A Case Study of Seaside, Oregon

2021 ◽  
Author(s):  
Zhenqiang Wang ◽  
Gaofeng Jia
Keyword(s):  
Transportation Network ◽  
Agent Based Modeling ◽  
Assessment Framework ◽  
Evacuation Simulation ◽  
Agent Based ◽  
Simulation Based ◽  
Evacuation Risk ◽  
Evacuation Routes ◽  
Evacuation Route

AbstractTypically, tsunami evacuation routes are marked using signs in the transportation network and the evacuation map is made to educate people on how to follow the evacuation route. However, tsunami evacuation routes are usually identified without the support of evacuation simulation, and the route effectiveness in the reduction of evacuation risk is typically unknown quantitatively. This study proposes a simulation-based and risk-informed framework for quantitative evaluation of the effectiveness of evacuation routes in reducing evacuation risk. An agent-based model is used to simulate the tsunami evacuation, which is then used in a simulation-based risk assessment framework to evaluate the evacuation risk. The route effectiveness in reducing the evacuation risk is evaluated by investigating how the evacuation risk varies with the proportion of the evacuees that use the evacuation route. The impacts of critical risk factors such as evacuation mode (for example, on foot or by car) and population size and distribution on the route effectiveness are also investigated. The evacuation risks under different cases are efficiently calculated using the augmented sample-based approach. The proposed approach is applied to the risk-informed evaluation of the route effectiveness for tsunami evacuation in Seaside, Oregon. The evaluation results show that the route usage is overall effective in reducing the evacuation risk in the study area. The results can be used for evacuation preparedness education and hence effective evacuation.

scholarly journals A storage expansion planning framework using reinforcement learning and simulation-based optimization

2021 ◽  
Vol 290 ◽  
pp. 116778
Author(s):  
Stamatis Tsianikas ◽  
Nooshin Yousefi ◽  
Jian Zhou ◽  
Mark D. Rodgers ◽  
David Coit
Keyword(s):  
Reinforcement Learning ◽  
Expansion Planning ◽  
Planning Framework ◽  
Simulation Based ◽  
Simulation Based Optimization
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