Intelligent Scheduling with Reinforcement Learning

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.

Gated‐Attention Model with Reinforcement Learning for Solving Dynamic Job Shop Scheduling Problem

“…In the actual world of production, the time necessary to produce solutions is quite important because receiving plans late might impair a company's productivity and resource use. As a result, the performance of the scheduling strategy is defined not only by the quality of the solution but also by the time required to produce the solution [41]. Although DRL-based approaches deliver acceptable solutions timely, training the models takes a long time, especially for larger problem instances.…”

Phasic Policy Gradient Based Resource Allocation for Industrial Internet of Things

“…The nodes in TSCH network communicate using a scheduling matrix composed of cells which are designated by its time slot index and the channel index. Recently, learning algorithms such as machine learning (ML) and reinforcement learning (RL) methods are being adopted to address resource allocation problems in wireless networks [2]. Cobbe et al proposed the PPG, by modifying the traditional actor-critic policy gradient method [3].…”