Evolve-learn-adjust for seru production system

Seru production system (SPS) as an innovative production mode, has the advantages of good flexibility, and high efficiency. SPS includes two coupled NP-hard subproblems: seru formation and seru scheduling, making it challenging for existing methods to solve efficiently. We develop an innovative paradigm, called evolve-learn-adjust to efficiently address SPS, which combines the advantages of evolutionary algorithms, deep reinforcement learning and problem-specific heuristic. Specifically, we employ multi-population evolutionary algorithms to optimise seru formation, making the optimisation process more stable. Subsequently, we design a novel Transformer-based DRL to learn the optimal seru scheduling policy, which enables the method to adapt to unseen large-scale instances. Finally, a problem-specific heuristic is employed to adjust the solution generated by DRL, improving overall solution quality at low computational cost. To further accelerate training convergence while achieving superior generalisation, we develop a multi-environment parallel training strategy to train DRL. Extensive experiments validate the effectiveness of the innovative paradigm, particularly for large-scale instances. On real-world instances derived from JD.com, with scales of up to 50 workers and 3000 batches, our method surpasses existing algorithms with performance improvements ranging from 90.25% to 97.61%. Finally, we leverage explainable artificial intelligence to transform the DRL agent's ‘black-box’ policy into trustworthy managerial insights.