Program Scheduling With Multi-Skill and External Resource Coordination Consideration Using Improved NSGA-II: Case Study

Technology-driven startups often experience fluctuations in business volume and their delivery peak exceeds the conventional functional department's human resource staffing. Resource allocation is crucial for ensuring timely completion of project deliverables within resource constraints. This study tackles the scheduling challenge under the condition of composite skills, with the goal of efficiently allocating diverse human resources while adhering to constraints such as external labor coordination and skill compatibility. Initially, an integer programming model was formulated to minimize the overall project duration and equalize the workload among employees. Subsequently, an enhanced fast and elitist non-dominated sorting genetic algorithm (NSGA-II) was developed by integrating heuristic-based population initialization and adaptive genetic strategies. A case study was then developed based on the R program of technology-driven company S. The allocation model was solved using both the classic and improved versions of the algorithm. The outcomes indicate that the fitness of the final generation significantly exceeds that of the initial generation, and the refined algorithm outperforms the conventional one in reducing the project duration and enhancing balance, with the potential to reduce project duration by 9% and enhance the balance by 40%. Cluster analysis and statistical methods were applied to extract three pivotal traits of the optimal allocation scheme, providing a scientific reference and decision-making foundation for managerial resource-allocation strategies.