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This study not only investigates the main effects of this Q-learning application but also provides recommendations for factor settings and useful guidelines for future applications of Q-learning to agent-based production scheduling.
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This study experiments with the manufacturing efficiency by layout change of a factory by means of agent-based autonomous production scheduling, using the virtual factory on a multi-agent simulation system.
Agent-based technologies in production control systems could facilitate seamless reconfiguration and integration of mechatronic devices/modules into systems.
The paper is aimed at summarizing the current state-of-the-art in development of the agent-based solutions in three areas connected with industrial production, namely in holonic control, production management and virtual enterprises.
In this study, we loaded Vilar et al's SBML model into COPASI, added some minor modifications and a few simple chemical reactions simulating the cascade of reactions leading to the production of the latent form of TGF-β1 based on information passed from the agent-based model, and created a two directional communication channel between COPASI and the agent-based model (Figure 4).
The agent-based AgriPoliS model was used to simulate the structural dynamics of agricultural production.
The agent-based model was then linked with a subcellular model of TGF-β1 production and its action on keratinocytes simulated with a complex pathway simulator.
This study proposes a multi-agent system framework called agent-based manufacturing control and coordination (AMCC) system, a agent-based framework using ontology and RFID technology to monitor and control dynamic production flows and also to improve the traceability and visibility of mass customization manufacturing processes.
In this agent-based system, there are four communities of agents: specification community of agents, function community of agents, physical solution community of agents and production constraint community of agents.
Classification of Agent-based Mobile Assistants.
Agent-Based Simulation Models of the College Sorting Process.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com