Order planning for manufacturing systems with load sequence-dependent degradation under human errors and imperfect inspection

Daily order planning is the core of dynamic decision-making that ensures the reliability and cost-effectiveness of manufacturing system delivery. However, order planning faces significant challenges due to the interplay of load sequence-dependent degradation, human errors, and imperfect inspection. The interplay of these factors introduces significant uncertainty, often leading to overestimated system performance and suboptimal planning outcomes. Thus, a comprehensive order planning framework is developed in this paper to optimise order scheduling. First, an Extended Stochastic Flow Manufacturing Network (ESFMN) model is introduced to characterise the dynamic interactions among processing machines, inspection machines, buffers, operators, and heterogeneous feedstocks (qualified and unqualified feedstocks) in manufacturing systems with load sequence-dependent degradation. Second, a semi-Markov model is employed to evaluate system mission performance that combines human errors and imperfect inspection. Third, a Clustering-Enhanced NSGA-II (CNSGA-II) algorithm is developed to tackle the order planning problem through the simultaneous optimisation of mission performance and cost. Comparative experimental results demonstrate that proposed order planning framework outperforms several advanced algorithms, including Memetic-NSGA-II, Reinforcement Learning-NSGA-II, Greedy algorithm-NSGA-II, and NSGA-II. Through a case study of a servo valve spool manufacturing system, the framework's effectiveness in practical applications is validated, confirming its ability to achieve reliable and cost-effective order planning.