Nest smarter, not harder: a hybrid vision-based deep reinforcement learning agent for packing 2D irregular geometries by rotational placement
摘要
Nesting is pivotal in maximizing material use and productivity within manufacturing industries and involves the ordering, rotational placement, and translation placement of 2D irregular patterns onto raw material sheets. Despite the industrial significance, few methodologies tackle the challenging rotational placement problem due to its computational complexity. Unlike traditional search-based heuristics and meta-heuristics methods, this research pioneers a Deep Reinforcement Learning (DRL)-based framework that acquires a learning-based policy for flexible rotational placement and combines it with two rule-based policies to ensure a comprehensive nesting solution. Empowered by a bespoke Deep Learning (DL)-based geometric semantics extractor module, our approach achieves a