A Mean-Risk Multi-Period Optimization Model for Cooperative Risk in the Shipbuilding Supply Chain

This study addresses the cooperative development problem between shipbuilding enterprises and suppliers under supply risk by improving and optimizing the Markowitz model. A mean-risk multi-period linear programming decision model and a nonlinear programming decision model are constructed under uncertain conditions. First, the connotation of supply chain cooperation risks in shipbuilding enterprises is analyzed, and key risk characteristics are identified. Then, based on lifecycle theory, the cooperation process is examined, and corresponding risk prevention strategies are proposed. Finally, an enhanced mean-risk multi-period linear programming model is developed, and a nonlinear programming decision model is introduced. Numerical experiments and sensitivity analysis using real-world shipbuilding enterprise data validate the correctness and effectiveness of the proposed models. The results demonstrate that, compared to linear programming models, nonlinear programming models achieve a lower risk for equivalent returns. The findings suggest that the proposed approach can effectively optimize cooperative decision-making under uncertainty, providing valuable insights for risk management in the shipbuilding supply chain.