Enhancing Hybrid Flow Shop Scheduling Problem with a Hybrid Metaheuristic and Machine Learning Approach for Dynamic Parameter Tuning

This paper addresses the Hybrid Flow Shop Scheduling Problem (HFSSP) by integrating metaheuristic (MHs) and machine learning (ML) approaches. Specifically, we propose a hybrid algorithm by combining Ant Colony Optimization (ACO) and Iterated Local Search (ILS) to form ACOILS. To further enhance the performance of this hybrid approach, we employ Proximal Policy Optimization (PPO), which is used for dynamic tuning of key parameters within the hybrid algorithm. The introduction of PPO allows real-time adjustment of key parameters, such as pheromone evaporation rates and local search intensity, to balance exploration and exploitation more effectively. Comparative experiments against the non-learning version of ACOILS and Simulated Annealing (SA) show that the learning based LACOILS significantly reduces the percentage deviation from the lower bound while maintaining stable performance through dynamic tuning. In terms of numerical results, LACOILS consistently outperforms SA and ACOILS. For smaller instances (N=20), it achieves up to 56.52% improvement over ACOILS and 12.5% over SA. For larger instances (N=150), LACOILS shows up to 29.82% improvement over ACOILS and 9.09% over SA, demonstrating its superior solution quality and efficiency.