Learning-enabled Flexible Job-shop Scheduling for Scalable Smart Manufacturing

• URL: http://arxiv.org/abs/2402.08979v1
• Date: Wed, 14 Feb 2024 06:49:23 GMT
• Title: Learning-enabled Flexible Job-shop Scheduling for Scalable Smart Manufacturing
• Authors: Sihoon Moon, Sanghoon Lee, and Kyung-Joon Park
• Abstract summary: In smart manufacturing systems, flexible job-shop scheduling with transportation constraints is essential to optimize solutions for maximizing productivity. Recent developments in deep reinforcement learning (DRL)-based methods for FJSPT have encountered a scale generalization challenge. We introduce a novel graph-based DRL method, named the Heterogeneous Graph Scheduler (HGS)
• Score: 11.509669981978874
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In smart manufacturing systems (SMSs), flexible job-shop scheduling with transportation constraints (FJSPT) is essential to optimize solutions for maximizing productivity, considering production flexibility based on automated guided vehicles (AGVs). Recent developments in deep reinforcement learning (DRL)-based methods for FJSPT have encountered a scale generalization challenge. These methods underperform when applied to environment at scales different from their training set, resulting in low-quality solutions. To address this, we introduce a novel graph-based DRL method, named the Heterogeneous Graph Scheduler (HGS). Our method leverages locally extracted relational knowledge among operations, machines, and vehicle nodes for scheduling, with a graph-structured decision-making framework that reduces encoding complexity and enhances scale generalization. Our performance evaluation, conducted with benchmark datasets, reveals that the proposed method outperforms traditional dispatching rules, meta-heuristics, and existing DRL-based approaches in terms of makespan performance, even on large-scale instances that have not been experienced during training.

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