A novel machine-learning rolling horizon heuristic for dynamic lot-sizing and job shop scheduling problems

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This study introduces an innovative approach to production planning and scheduling under uncertain customer demands by integrating uncertainty directly into the algorithmic framework. We present a novel rolling horizon simulation-based constructive heuristic to minimise total costs, encompassing production, setup, inventory, and backlog costs. We leverage priority rules to enable the method to adapt in real-time to changes. Our contributions include not only a novel heuristic but also the integration of a coevolutionary genetic programming-based hyper-heuristic, significantly improving solution quality and computational efficiency. Compared to other rule-based heuristics, our method consistently outperforms with an average reduction in total costs of 1.78%. Furthermore, it outperforms deterministic and two-stage stochastic programming models within a one-hour time limit, with reductions of 18.20% and 6.53%, respectively. Incorporating mathematical programming models into a rolling horizon scheme led to slightly lower total costs, with an average reduction of 0.13% and 1.16%. However, in less than half an hour, the proposed method reached better results in two out of three cases. The results of an efficiency and robustness analysis highlight the proposed method as a robust solution for dynamic and complex real-world applications.