## 2 Related Works
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Multi-Agent System (MAS)** & **Description** & **Related Works** \\
**Platforms/Frameworks** & & \\ \hline JADE (Java Agent & JADE is an open-source software framework that is Java-based & [15, 16, 17, 18] \\ Development Framework) & and used for MASs. & \\ \hline Janus & Janus is an agent-based platform for modeling simulations that & \\  & enables users to build and test complex systems. It offers a & [26, 27, 28] \\  & Graphical User Interface (GUI) and a variety of tools. & \\ \hline ZEUS & \begin{tabular}{c} ZEUS is an open-source agent-based platform implemented in \\ the Java programming language. It provides a GUI and a \\ runtime environment [67]. \\ \end{tabular} & [19, 20, 21] \\ \hline \multirow{3}{*}{\begin{tabular}{c} Mad-Kit (Multi-Agent Development Kit) \\ \end{tabular} } & \begin{tabular}{c} Mad-Kit is an agent-based framework for modeling and \\ simulating agents of an MAS. It provides APIs and tools in \\ order to create agents. \\ \end{tabular} & [25, 26, 27] \\ \hline Malaca & \
\begin{tabular}{c} Malaca is a gent architecture that combines Component-based \\ Software Engineering and Aspect-Oriented \\ Software Development. \\ \end{tabular} & [22] \\ \hline NetLogo &
\begin{tabular}{c} NetLogo is an MAS platform for modeling and simulating \\ agents. It provides a GUI and supports complex systems. \\ \end{tabular} & [29, 37] \\ \hline Other platforms & There are other platforms as well, for example, Rapast, & [30, 31, 32, 33, 34] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MAS platforms and frameworks. the model should provide granularity in order to exploit different schedule optimization agents that provide different capabilities and methods. The focus and contribution of this work is to provide an information model using the AAS concept for the description of a scheduling agent and its functionalities. The aim is to bridge the gap on how the interaction between a scheduling agent and other agents or assets can be formalized so that a third-party component can be aware of which services the agent can provide as well as where and how it can access it. This overall goal is to achieve that in an abstract manner, meaning that entities that interact with this agent will not rely on custom interaction methods and will have no dependencies on the implementation technologies of the agent. In detail, the contributions of this work are the following: (1) the use of the AAS concept, where AAS is a standard of Industry 4.0 and (2) the design and discussion of an AAS model to describe scheduling agents, environment, constraints, and objective. In conclusion, this section presented related works and also mentioned the contributions of this work to the community. Additionally, in the literature, there are several works that use the AAS concept, but they do not provide a generic model for controlling the agents and parameterizing them. The idea of designing the proposed AAS model is to overcome the problem of controlling and parameterizing scheduling agents (please see the Proposed Methodology). Moreover, the design of the AAS models was part of a high-level idea of creating an intelligence (meta-agent) based on the criteria, applicable environments, and objectives of each agent to select the agent that can solve a given scheduling problem.