Attention-Enhanced Dual-Critic DRL for Parallel-Machine Scheduling with Sequence-Dependent Setups and Mandatory Shutdown Windows in Plastic Woven Packaging

Scheduling at the printing bottleneck of plastic woven packaging production is complicated by asymmetric sequence-dependent setup times (SDST) caused by color transitions and by mandatory non-preemptive shutdown windows. This study focuses on assigning and sequencing orders on parallel printing machines under these coupled operational constraints. To address this problem, we propose an Attention-Enhanced Dual-Critic Deep Reinforcement Learning framework with Action Masking (AE-DMAC). The scheduling problem is formulated as a bi-objective parallel-machine Markov Decision Process that considers makespan and total setup time under a fixed preference setting. A cross-attention module models the compatibility between the current machine state and pending orders to capture asymmetric SDST effects. Two critics separately estimate the efficiency- and setup-related value signals before they are combined for policy optimization, reducing interference between the two objectives. In addition, a deterministic action-feasibility mask removes assignments that would overlap the known shutdown window before action sampling. The framework is evaluated on industrially calibrated synthetic instances with 50, 150, and 300 orders scheduled on eight parallel printing machines. Experimental results show that AE-DMAC consistently improves makespan and normalized setup time per machine compared with the implemented heuristic, meta-heuristic, and Vanilla PPO baselines under the tested operating conditions. In the large-scale instance, AE-DMAC achieves an average makespan of 223.8 h and a normalized setup time of 568.4 min per machine, corresponding to reductions of 7.4% and 27.4%, respectively, relative to Vanilla PPO. The feasibility mask maintains zero shutdown-window violations in the evaluated deterministic setting, while the attention mechanism substantially reduces high-cost sequence-dependent color transitions. These results indicate that the proposed framework is a promising scheduling approach for parallel printing systems with asymmetric changeovers and known machine-unavailability windows.