Multi-Skilled Project Scheduling for High-End Equipment Development Considering Newcomer Cultivation and Duration Uncertainty

Traditional off-the-job training is becoming ineffective in high-end equipment research and development (R&D) projects due to the contradiction between rapid technological progress and the slow growth of newcomers, calling for “on-the-job mentoring” to enable synchronized advancement of project execution and newcomer cultivation. For this, we propose the multi-skilled project scheduling problem with newcomer cultivation under uncertain durations (MSPSP-NCU) and abstract it as a stochastic programming model. The model aims to minimize expected makespan and maximize newcomers’ skill efficiency by optimizing workforce assignment that enables experienced workers to mentor newcomers while simultaneously optimizing task scheduling. Solving the model is blocked by the inherently NP-hard nature of the project scheduling problem and the stochasticity of the durations. Therefore, we put forward an adaptive simulation–optimization approach featuring two-fold: a simulation module capable of dynamically adjusting sample sizes based on convergence feedback and evaluating solutions with improved efficiency and stable accuracy; a tailored non-dominated sorting genetic algorithm II (NSGA-II) with adaptive evolutionary operators that enhance search effectiveness and ensure the identification of a well-distributed Pareto front. By using data from an aerospace component R&D project, the proposed approach is validated for its performance in identifying Pareto-optimal solutions. Several personalized rules are designed by integrating workforce development strategies into the selection process, providing actionable guidelines for cultivating newcomers in technology-intensive projects.