Coping With Shortages Caused By Disruptive Events In The Automotive Supply Chain

With China's accession to the World Trade Organization (WTO), automotive industry is more and more popular in our society. In 2009, the production and sales amount of China surpassed the USA firstly. However, such as 9.11 terrorist attacks, Wenchuan earthquake and Japan Tsunami, all these disruptive events promotes the development of the supply chain risk management(SCRM). How to cope with the disruptive events can improve the competitiveness of the company.Nowadays, supply chain could be disrupted by unplanned events, especially the automotive supply chain is geographically more diverse. In this thesis, we first propose a deterministic model with considering the disruptions of chips that could be substituted by higher types in the automotive supply chain. Based on this deterministic model, we next propose a robust optimization that captures the uncertainties. We analyze the complexity of this robust optimization model and identity a tractable model based on the L1-norm uncertainty set. We finally compare our model with both the deterministic model and the corresponding two-stage stochastic model in a rolling horizon simulation environment. The computational results demonstrated that robust model could generate higher profits than the deterministic model and the two-stage stochastic model.