The Internet of Everything paradigm is being rapidly adopted in developing applications for
different domains like smart agriculture, smart city, big data streaming, and so on. These IoE …

Reinforcement learning (RL) is one of the most remarkable branches of machine learning
and attracts the attention of researchers from numerous fields. Especially in recent years, the …

Decision-making in construction planning and scheduling is complex because of budget
and resource constraints, uncertainty, and the dynamic nature of construction environments …

Flexible job-shop scheduling problem (FJSP) is an extension of the simple JSP with
additional features of routing flexibility. It is an essential class of sequencing and planning …

This work investigates the applicability of a reinforcement learning (RL) approach,
specifically AlphaGo Zero (AZ), for optimizing sheet-metal (SM) production schedules with …

Production scheduling is the task of assigning job operations to processing resources such
that a target goal is optimized. constraints on job structure and resource capabilities …

This paper proposes an Encoder-Decoder neural network architecture with Attention
Mechanism for solving the DRC-FJSSP using Deep Q-Learning. In the DRC-FJSSP the …

Abstract Machine learning encompasses several approaches, including reinforcement and
deep learning. The main objective of RL is to maximize the rewards obtained by an agent …

With recent advances in artificial intelligence (AI), it is time to take a review of learning
process as an approach for production scheduling. Neural networks, reinforcement learning …

In the manufacturing industries, the most challenging problems are mostly related to time
efficiency and customer satisfaction. This is mainly translated to how efficient is the frequent …