Aiming at the problem of efficiency and cost optimization in the production of intelligent robots, traditional methods are difficult to solve complex problems such as multi-layer capacity limitation, assembly delay and equipment maintenance collaborative optimization, and the research is simplified.The innovation of this study lies in constructing a three-layer capacity-constrained batch production model, breaking through the single-level optimization limitations of previous research. This model realizes cross-layer coordination among production scheduling, assembly delay handling, and equipment maintenance. Meanwhile, a multi-algorithm synergy system is established: a dynamic programming heuristic algorithm is used to solve the model, reducing costs; a genetic algorithm is integrated to cope with assembly delays; a simulated annealing method optimizes the maintenance plan; and time series analysis accurately predicts requirements. This integration of a systematic model and multiple algorithms provides a holistic solution to complex production constraints. The results show that the model reduces inventory and delay costs and ensures production continuity. The performance is better than the traditional method and reduces the loss of prediction error. This study provides a systematic optimization strategy for intelligent robots and similar production systems to help production automation and intelligence.
Xu et al. (Tue,) studied this question.