Distributionally robust optimization for a capacity-sharing supply chain network design problem.

Capacity sharing, as a collaborative strategy among manufacturers, aims to alleviate this problem by sharing manufacturing capabilities to meet demand with large fluctuating ranges. This paper explores a novel supply chain network design problem considering capacity sharing with third-party manufacturers. Specifically, the problem involves location choices for owned manufacturing plants and distribution centers and the selection of third-party manufacturers. Third-party manufactured products can only be shipped to distribution centers, whereas products from plants can be shipped directly or through distribution centers to customer zones. In this network, customer demand and the product prices of third-party manufacturers are assumed to be uncertain. To address uncertainties, we formulate a distributionally robust chance-constrained model for the problem. The probability distributions of the uncertainties are characterized using Wasserstein ambiguity sets. The incorporation of chance constraints serves to enhance the level of satisfactory customer demand. To ensure tractability, we reformulate the distributionally robust model as a solvable model employing duality theory. Finally, we conduct numerical experiments based on a real-life manufacturing company to evaluate the effectiveness of the proposed model. The results confirm that the proposed model reduces the cost standard deviation by an average of 21.9% and increases reliability. Our study can present a reliable framework for designing a capacity-sharing supply chain network for manufacturing enterprises that optimizes cost while improving customer satisfaction by over 95%. [ABSTRACT FROM AUTHOR]