Development of future industry has become an active research area in the recent years. A structured national workgroup, called IMS2, has emerged in France over the last decade. The aim of this paper is, regarding four highlighted general research topics (namely agility, new technologies, sustainability, and industrial dissemination), to synthetize the general research works on intelligent manufacturing and to demonstrate the principal contributions developed within the framework of this national workgroup. After a critical evaluation of the proposed approaches toward alternative solutions, this paper suggests a 2030 roadmap for the workgroup.

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.