A Deep Reinforcement Learning-Based Adaptive Fireworks Algorithm for Spatiotemporal Resource-Constrained Aircraft Pulsating Assembly Line Balancing

Abstract

This paper addresses the highly complex aircraft pulsating assembly line balancing problem, strictly restricted by extreme spatiotemporal constraints, including rigid precedence for parallel operations, stringent limits on the total number of specialized workers and narrow physical area capacities. Traditional exact solvers suffer from combinatorial explosion when facing such constraints. Meanwhile, conventional metaheuristics frequently struggle with feasibility due to blind searches, and existing reinforcement learning methods relying on offline pre-training tend to suffer from generalization bottlenecks when confronted with unknown topological structures. To overcome these challenges, a Meta-Controlled Online Adaptive Fireworks Algorithm (MCOA-FWA) is proposed. This online framework utilizes a deep Q-network as a meta-controller to dynamically schedule heterogeneous topological actions without offline pre-training. Furthermore, a Partition-Guided Spatiotemporal Collaborative Decoder (PG-STCD) based on dualchromosome encoding is developed to decouple spatiotemporal conflicts. Extensive experiments on real-world industrial instances with up to 3182 operations demonstrate that MCOA-FWA effectively avoids deadlocks and guarantees physical feasibility. Compared to baseline algorithms, the proposed method achieves a significant performance improvement in takt time compression and robustness under tight computational budgets.