## 5 Conclusions and Future Work  
This work focuses on the design and implementation of a scheduling agent using the AAS concept. The primary objective was to enable a generic agent model capable of supporting multiple scheduling methods and exploiting them with respect to the corresponding problem. The proposed AAS model allows the agent interactions to be described in an abstract way, enables interoperability with external software via AAS platform-hosted API, and, finally, provides a description of the agent's capability. The research aimed to  
Figure 7: Screenshot from UI tab for resulted schedule evaluation with the use of DES. highlight a way to model the capabilities of scheduling algorithms and their compatibility with different problems based on three basic notations. Concentrating on an adaptable scheduling agent approach, this method allows the agent to classify problems based on these three notations by applying rules over the provided information. As such, the agent may be able to create context out of the input information and selectively allocate a problem to a specific algorithm. The proposed approach, in addition, seems promising on the scalability aspect that may enable the addition of an arbitrary set of scheduling methods and compatible environments along with the specific classification rules for the agent. The proposed information model and agent architecture were implemented within the Janus open-source framework for developing MAS in order to establish the basic agent, capacities, skills, and event format for its deployment within an MAS. There are some fundamental differences in using the AAS concept for an agent versus other types of assets. The differences mainly occur because agents are already supported by the AOP language for exchanging information, and this is, in essence, another communication language different from the AAS. As such, this work proposes the establishment of the message-based interaction within the MAS as well as provides communication functionalities with other systems that were not built on top of AOP and are not able to participate within the decentralized communication framework of the agents. In the current research work, agents are described mainly on the optimization and communication functionalities, and it aims to allow the system to identify the capabilities that the agent can provide and thus redirect the scheduling request to the right algorithm. This was supported by the description of the scheduling problem based on three notations, namely the environment, constraints, and objectives, which is a widely adopted practice for production scheduling problems. The framework can, in this way, be scalable to different problems as long as the agent is able to make a rule-based classification of the input data for each problem type. This may boost its applicability in different industrial scenarios, providing the ability to implement different scheduling algorithms and services in a single scheduling agent architecture. Future work will focus on establishing a common information format that will be used to exchange production schedule-related information to be more abstract and does not depend on the system's ability to read a custom data model that may be applied only in individual industrial use cases. Nowadays, although the aspects of accessibility and compatibility to define where and how a system can exchange information and request services from the scheduling agent are specified, the specific way in which information should be structured within the message has not yet been defined. Conceptualization, V.S., N.N. and K.A. ; methodology, V.S., E.B. and P.M.; software, V.S. ; validation, V.S., E.B. and P.M.; resources, N.N. and K.A. ; writing--original draft preparation, V.S., E.B. and P.M.; writing--review and editing, N.N. and K.A. ; visualization, V.S. ; supervision, K.A. ; project administration, N.N. and K.A. ; funding acquisition, K.A. All authors have read and agreed to the published version of the manuscript.