Abstract

Abstract

Abstract
In this paper, we present and discuss an innovative approach to solve Job Shop scheduling problems based on machine learning techniques. Traditionally, when choosing how to solve Job Shop scheduling problems, there are two main options: either use an efficient heuristic that provides a solution quickly, or use classic optimization approaches (e.g., metaheuristics) that take more time but will output better solutions, closer to their optimal value. In this work, we aim to create a novel architecture that incorporates reinforcement learning into scheduling systems in order to improve their overall performance and overcome the limitations that current approaches present. It is also intended to investigate the development of a learning environment for reinforcement learning agents to be able to solve the Job Shop scheduling problem. The reported experimental results and the conducted statistical analysis conclude about the benefits of using an intelligent agent created with reinforcement learning techniques. The main contribution of this work is proving that reinforcement learning has the potential to become the standard method whenever a solution is necessary quickly, since it solves any problem in very few seconds with high quality, approximate to the optimal methods.

Abstract
There is a growing presence of technology in the daily lives of elementary school students, with a recent exponential rise due to the constraints of remote teaching during the COVID-19 pandemic. It is important to understand how the education system can contribute to helping students develop the required skills for technological careers, without neglecting its obligation to create conditions that allow them to acquire transversal skills and to enable them to exercise full citizenship. The integration of Educational Robotics and block programming activities in collaborative learning environments promotes the development of computational thinking and other ICT skills, as well as critical thinking, social skills, and problem solving. This paper presents a theoretical proposal of a didactic sequence for the introduction to educational robotics and programming with Scratch Jr. It is composed of three learning scenarios, designed for elementary school teaching. Its main goal is to create conditions that favour the development of computational thinking in a collaborative learning environment. With increasing complexity and degree of difficulty, all the tasks root from a common problem: How can we create an algorithm that programs the robot/sprite to reach a predetermined position?

Abstract