Smart Asset Management

Real-time optimization of maintenance and production scheduling for an Industry 4.0-based manufacturing system

In collaboration with Axiom Group, we are dveloping models for real-time optimization of maintenance and production scheduling for Industry 4.0-based manufacturing systems. We studied to what extent real-time updates on the arrival of new jobs to the system as well as machinery breakdown can improve scheduling decisions. We also investigated when and how such information can be utilized to generate and update the schedules (i.e., schedule-reschedule). The paper "Real-time optimization of maintenance and production for an Industry 4.0-based manufacturing system", written based on this work received the best student paper award of the Reliability & Maintainability Symposium (RAMS) in 2020.

Real-time production scheduling with random machine breakdowns using deep reinforcement learning

We are applying reinforcement learning (RL) approaches for real-time scheduling (RTS). Our proposed RL based RTS uses a multiple dispatching rules (MDRs) strategy to enhance the production performance. A case study of a smart manufacturing firm is considered to apply the proposed approach. The firm is located in Ontario (Canada) and specializes in thermoplastic injection molding of various components and assemblies. The production schedules on the shop floor are sensitive to the changes resulting from random breakdowns and their associated maintenance activities. The production managers are using the data from the continuous monitoring system to update production schedules. The updating process is conducted manually based on their knowledge and a single dispatching rule (SDR) strategy. Our proposed RTS system helps the company utilize the installed Industry 4.0 concepts and achieve the Industry 4.0 vision in the production control. The performance of the proposed RTS system is compared to the current strategy applied in the company. Results show the efficiency of the proposed RTS system compared to the current strategy.