The impetus for an interconnected, efficient, and adaptive manufacturing system, as advocated by the Industry 4.0 revolution, together with the latest developments in information technology, advanced manufacturing has become a prominent research topic in recent years. One critical aspect of advanced manufacturing is how to incorporate real-time demand information with a manufacturer's resource information, including workforce data and machine capacity and condition information, among others, to optimally schedule manufacturing processes with multiple objectives. In general, optimized manufacturing scheduling is a non-deterministic polynomial-time hard problem. Due to the complexity, scheduling presents a number of challenges to find the best possible solutions. This research proposes an ontology-based framework to formally represent a synchronized, station-based flow shop with a multi-skill workforce and multiple types of machines. Based on the ontology, this research develops a multi-agent reinforcement learning approach for the optimal scheduling of a manufacturing system of multi-stage processes for multiple types of products with various machines and a multi-skilled workforce. By employing a learning algorithm, this approach enables real-time cooperation between the workforce and the machines, and adaptively updates production schedules according to dynamically changing real-time events.
Qu, S., Wang, J., Govil, S., & Leckie, J. O. (2016). Optimized Adaptive Scheduling of a Manufacturing Process System with Multi-skill Workforce and Multiple Machine Types: An Ontology-based, Multi-agent Reinforcement Learning Approach. In Procedia CIRP (Vol. 57, pp. 55–60). Elsevier B.V. https://doi.org/10.1016/j.procir.2016.11.011