A Novel Multi-Objective Evolutionary Algorithm for Proxy-Based Energy-Aware Scheduling in Aircraft Assembly Line

The evolution of aircraft design has resulted in increasingly intricate aircraft manufacturing processes characterized by increased performance requirements and stringent quality standards. Consequently, the aeronautical assembly industry is confronted with substantial new challenges in fulfilling these demands. This work analyzes aircraft final assembly line scheduling including three critical issues: proxy-based energy-aware scheduling, worker assignment, and process planning. Specifically, it addresses an integrated assembly line problem focused on complete assembly tasks, with optimization criteria aimed at minimizing total delay and alleviating workload imbalance along the assembly line. To formalize the practical optimization problem, a multi-objective nonlinear mathematical model is developed. Then, an improved multi-objective genetic algorithm is proposed, which directs the convergence of the Pareto front by incorporating an enhanced hierarchical clustering mechanism that incorporates both fitness and chromosome dimensions. An adaptive factor and an objective function are introduced to increase the convergence speed of the algorithm to align with real production needs. Finally, through independent experiments, the proposed algorithm is rigorously compared to other multi-objective algorithms, demonstrating that the proposed algorithm achieves respective improvements of 4.0% and 8.5% in the inverted generational distance (IGD) metric and 10.6% and 18.1% in the hypervolume (HV) metric, as compared to two state-of-the-art algorithms.