A data-driven scheduling approach to smart manufacturing

Traditional methods of scheduling are mostly based on the use of pieces of information directly related to the performance of schedules, as for instance processing times, delivery dates, etc., assuming that the production system is operating normally. In the case of malfunctions, the literature concentrates on the ensuing corrective operations, like scheduling with machine breakdowns or under remanufacturing considerations. These event-driven approaches are mainly used in dynamic scheduling or rescheduling systems. Unlike those, Smart Manufacturing and Industry 4.0 production environments integrate the physical and decision-making aspects of manufacturing processes in order to achieve their decentralization and autonomy. On these grounds we propose a data-driven architecture for scheduling, in which the system has real time access to data. Then, scheduling decisions can be made ahead of time, on the basis of more information. This promising approach is based on the architecture of cyber-physical systems, with a data-driven engine that uses, in particular, Big Data techniques to extract vital information for Industry 4.0 systems.Fil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: Tohmé, Fernando Abel. Universidad Nacional del Sur. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: Frutos, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentin

Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization

With the rapid development of Industrial 4.0, the modern manufacturing system has been experiencing profoundly digital transformation. The development of new technologies helps to improve the efficiency of production and the quality of products. However, for the increasingly complex production systems, operational decision making encounters more challenges in terms of having sustainable manufacturing to satisfy customers and markets’ rapidly changing demands. Nowadays, rule-based heuristic approaches are widely used for scheduling management in production systems, which, however, significantly depends on the expert domain knowledge. In this way, the efficiency of decision making could not be guaranteed nor meet the dynamic scheduling requirement in the job-shop manufacturing environment. In this study, we propose using deep reinforcement learning (DRL) methods to tackle the dynamic scheduling problem in the job-shop manufacturing system with unexpected machine failure. The proximal policy optimization (PPO) algorithm was used in the DRL framework to accelerate the learning process and improve performance. The proposed method was testified within a real-world dynamic production environment, and it performs better compared with the state-of-the-art methods

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202