A Distributionally Robust Optimization Model for a Manufacturing Outsourcing Supply Chain Network Design Problem

In this paper, we explore the manufacturing outsourcing supply chain network design problem under uncertainties. Through outsourcing manufacturing, a proportion of the products are produced by third-party manufacturers, enabling quick response to demand and cost reduction. We present a data-driven distributionally robust optimization model that aims to minimize total costs and reduce the risk of uncertainty. The model considers the uncertainty of both demand and third-party manufacturer product prices at the same time, using the Wasserstein ambiguity set to deal with uncertain variables. Further, we reformulate the distributionally robust model as an equivalent model under uncertain parameters. Finally, a comprehensive numerical study is conducted to demonstrate the effectiveness of the proposed model.