Haoyi Tan

Adaptive Lagrangian Penalty-Enhanced Proximal Policy Optimization for Flexible Job Shop Rescheduling with Worker Workload Constraints Under Concurrent Dynamic Disturbances

This paper proposes ALP-PPO, an adaptive Lagrangian penalty-enhanced proximal policy optimization algorithm for real-time rescheduling under concurrent machine breakdowns and rush orders, and indicates that the adaptive Lagrangian mechanism reduces constraint violations by more than 40% relative to fixed-penalty alternatives while keeping the primary objectives competitive.