Optimization of Production Scheduling for the Additive Manufacturing of Ship Models Using a Hybrid Method

Abstract

This paper introduces a hybrid optimization method that leverages either linear programming (LP) or a genetic algorithm (GA) based on the problem size to enhance the parallel additive manufacturing (AM) process for ship models. The LP ensures optimality but can experience exponential increases in the computation time as the problem size grows. To address this limitation, the GA is employed for larger problems, providing optimal solutions within reasonable quality and time constraints. The method optimizes the module allocation to AM machines and determines the build processing sequence for each machine, while also considering the availability of workers preparing for consecutive module production. Applied to a case study, the proposed method achieves a 14% reduction in the completion time compared to a heuristic method from a previous study. Furthermore, the method is validated by benchmarking against the heuristic method across various problem sizes, consistently demonstrating superior performance.