Dynamic heuristic scheduling for aerospace flow-shop manufacturing under variable workloads

In aerospace manufacturing, where complicated production flows, high precision requirements, and workload unpredictability greatly affect operational performance, effective scheduling is essential. In order to minimize makespan in an aerospace manufacturing setting, this study assesses the efficacy of three well-known flow-shop scheduling heuristics: Nawaz-Enscore-Ham (NEH), Campbell-Dudek-Smith (CDS), and Palmer Slope Index (PSI). To compare heuristic performance across several jobs and machines under various workload situations, a computational framework was created. A Python-based graphical user interface (GUI) was created to improve industrial applicability by automating scheduling computations and giving decision-makers real-time visibility. Comparative findings show that NEH consistently achieves the lowest makespan, especially in circumstances with significant variability and uneven workloads with makespan values of NEH as 46.029. CDS as 46.030, PSI as 46.50 and Fast Surrogate-Assist Evolutionary Algorithm based on incremental learning as 46.030. While PSI offers benefits in terms of simplicity and computational speed for smaller-scale activities, CDS performs competitively in organized production settings. The creation of a condition-based heuristic performance framework, which connects heuristic suitability to operational elements like machine design, processing-time variability, and workload imbalance, is a significant contribution of this study. Instead of using static benchmarking, this approach allows for adaptive heuristic selection. The results offer both theoretical understanding and useful recommendations for enhancing aerospace flow-shop manufacturing systems’ scheduling effectiveness, resource utilization, and decision-support capabilities. Not applicable.