This paper introduces genetic algorithms (GA) as a complete entity, in which knowledge of
this emerging technology can be integrated together to form the framework of a design tool …

Due to the stubborn nature of the deterministic job-shop scheduling problem many solutions
proposed are of hybrid construction cutting across the traditional disciplines. The problem …

We propose a framework to learn to schedule a job-shop problem (JSSP) using a graph
neural network (GNN) and reinforcement learning (RL). We formulate the scheduling …

The job-shop scheduling problem (JSSP) is one of the best-known combinatorial
optimization problems and is also an essential task in various sectors. In most real-world …

A comprehensive guide to a powerful new analytical tool by two of its foremost innovators
The past decade has witnessed many exciting advances in the use of genetic algorithms …

Traditionally, assignment and scheduling decisions are made separately at different levels
of the production management framework. The combining of such decisions presents …

Deep reinforcement learning (DRL), as a promising technique, is a new approach to solve
the job shop scheduling problem (JSSP). Although DRL method is effective for solving …

Job-shop scheduling problem (abbreviated to JSP) is one of the well-known hardest
combinatorial optimization problems. During the last three decades, the problem has …

A job shop consists of a set of different machines (like lathes, milling machines, drills etc.)
that perform operations on jobs. Each job has a specified processing order through the …

The fourth edition of this bestselling textbook explains the principles of artificial intelligence
(AI) and its practical applications. Using clear and concise language, it provides a solid …