The interdependence of nesting and production scheduling problems in Additive Manufacturing (AM) systems poses a significant computational challenge when considering traditional optimization methods. This work addresses the AM scheduling problem (AMSP), with a particular focus on the nesting component, which remains the major computational bottleneck in existing approaches. Current nesting...
Nowadays, companies desire to offer customised products and services to their customers. At the same time, they want to address customers’ requests as fast as possible. In addition, operations are often subject to high uncertainty and frequent disruptions, such as urgent order arrivals, resource unavailability, and product defects. Under these conditions, companies need to schedule tasks...
Early success of Deep Reinforcement Learning (DRL) was rooted in arcade and board games, where expert behavior could be readily captured from top players. In these settings, demonstrations were used to bootstrap learning and accelerate policy convergence. In contrast, in combinatorial optimization problems, such as the Flexible Job-shop Scheduling Problem (FJSP), optimal demonstrations are...
