Curvature-based distribution algorithm: rebalancing bike sharing system with agent-based simulation

2019 ◽  
Vol 22 (3) ◽  
pp. 587-607 ◽  
Author(s):  
Seonghoon Ban ◽  
Kyung Hoon Hyun
Keyword(s):  
Agent Based Simulation ◽  
Agent Based ◽  
Bike Sharing

Effect of Policy and Entrepreneurship on Innovation and Growth: An Agent-based Simulation Approach

2010 ◽  
Vol 40 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Zheng WANG ◽  
Tao LIU ◽  
Xiaoye DAI
Keyword(s):  
Simulation Approach ◽  
Agent Based Simulation ◽  
Agent Based

Deriving and Testing Democratization Micro Mechanisms Using Agent-Based Simulation and Data

2014 ◽  
Author(s):  
Viktoria Spaiser ◽  
David J.T. Sumpter
Keyword(s):  
Agent Based Simulation ◽  
Agent Based

Bubbles and Absence of Crashes: A Multi-Agent-Based Simulation

2017 ◽  
Author(s):  
Alexandre Madelaine
Keyword(s):  
Agent Based Simulation ◽  
Agent Based ◽  
Multi Agent

scholarly journals Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Lennart Adenaw ◽  
Markus Lienkamp
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
User Behavior ◽  
Transport Sector ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Charging Station ◽  
Spatio Temporal

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

scholarly journals Process Modeling and Simulation of Tableting—An Agent-Based Simulation Methodology for Direct Compression

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 996
Author(s):  
Niels Lasse Martin ◽  
Ann Kathrin Schomberg ◽  
Jan Henrik Finke ◽  
Tim Gyung-min Abraham ◽  
Arno Kwade ◽  
...  
Keyword(s):  
Raw Materials ◽  
Pilot Scale ◽  
Physical Models ◽  
Agent Based Simulation ◽  
Validation Data ◽  
Agent Based ◽  
Industrial Processing ◽  
Digital Twins ◽  
Product And Process ◽  
The Impact

In pharmaceutical manufacturing, the utmost aim is reliably producing high quality products. Simulation approaches allow virtual experiments of processes in the planning phase and the implementation of digital twins in operation. The industrial processing of active pharmaceutical ingredients (APIs) into tablets requires the combination of discrete and continuous sub-processes with complex interdependencies regarding the material structures and characteristics. The API and excipients are mixed, granulated if required, and subsequently tableted. Thereby, the structure as well as the properties of the intermediate and final product are influenced by the raw materials, the parametrized processes and environmental conditions, which are subject to certain fluctuations. In this study, for the first time, an agent-based simulation model is presented, which enables the prediction, tracking, and tracing of resulting structures and properties of the intermediates of an industrial tableting process. Therefore, the methodology for the identification and development of product and process agents in an agent-based simulation is shown. Implemented physical models describe the impact of process parameters on material structures. The tablet production with a pilot scale rotary press is experimentally characterized to provide calibration and validation data. Finally, the simulation results, predicting the final structures, are compared to the experimental data.

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