# Intelligent Scheduling with Reinforcement Learning
## 6 Conclusions  
The scientific question of this paper is to investigate the benefits and usefulness of applying machine learning techniques to scheduling systems as an optimizer, with the goal of improving the overall performance of the final scheduling plans. The work presented in this paper confirms this possibility through the development of an intelligent agent. This agent takes advantage of its architecture to independently solve _Job Shop_ problems according to its beliefs about which actions are best. The agent aims to obtain an optimized scheduling plan efficiently, thus reducing waiting times and demonstrating adaptability by being prepared to deal with unforeseen or dynamic situations (where new tasks can appear at any time). This agent achieved a very satisfactory quality, offering an alternative for calculating solutions for _Job Shop_ scheduling problems that balances (in time) the cost of calculating solutions with the benefit of improving their quality.  
By analyzing the presented results, we consider considered that the proposed objective was fulfilled. In this process, and for this to be possible, it was necessary to achieve several objectives, of which we highlight:  
* Modeling the _Job Shop_ category scheduling problem into a reinforcement learning problem by adapting the Markov process, covering how rewards are calculated, the possible set of states and the achievement of an agent's goals or the set of available actions.
* Design and implementation of training environment for reinforcement learning algorithms to create intelligent agents capable of solving the category _Job Shop_ scheduling problem.
* Implementation of the complete scheduling system, composed of the artificial intelligence module, which includes the intelligent agent and the training environment used during learning, and the scheduling module, which establishes all the rules of the problem, supports the decoding of academic instances and ensures the correct definition of all the components of the problem: machines, tasks and operations.  
* Execution of a computational study for the analysis and validation of the results obtained by the intelligent agent developed in this work, which analyzes the performance, in efficiency and effectiveness, using measurements of the runtimes of the approaches studied and the quality of the solutions obtained, respectively.  
Thus, the main contribution of this work is the conclusion (with statistical significance) about the benefits of using an intelligent agent created with reinforcement learning techniques, in a proposed novel training environment, and incorporated into a complete scheduling system that operates as the component that solves the _Job Shop_ scheduling problems.  
Conceptualization, A.M., B.C.; methodology, B.C., B.F.; software, B.C.; validation, A.M. and B.F.; formal analysis, J.M.; investigation, A.M., B.C., B.F. and J.M.; resources, A.M.; data curation, B.C.; writing--original draft preparation, B.C.; writing--review and editing, A.M., B.C. and B.F.; visualization, B.C., J.M.; supervision, A.M., B.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.