Supply Chain Optimization With Uncertainty and Hierarchical Decision-Makers

Supply chain models describe the activities carried out in the process industry. They are used to design and operate complex sequences of tasks that transform raw materials and deliver final products to markets. Many optimization models have been developed for supply chain planning because they offer the possibility of finding strategies that lead to greater economic benefits. The traditional models have focused on finding the optimal decisions of the supply chain planner in a deterministic context. However, it is widely recognized that uncertainty and external decisionmakers play a fundamental role in the economic success of industrial supply chains. This thesis proposes mathematical programming models for supply chain optimization that consider uncertainty and external decision-makers in a variety of industrial settings. Chapter 1 provides the motivation and the necessary background for our models. In Chapter 2, we study the design of resilient supply chains with risk of disruptions. Disruptions is a type of uncertainty that has not received much attention for supply chain planning, but it is known to have a significant effect in the performance of supply chains. We develop a stochastic programming model for supply chain design that includes disruptions at distribution centers, and a tailor-made solution method to address industrial instances of the problem. In Chapter 3, we present a novel cross-decomposition algorithm for investment planning under uncertainty. The algorithm integrates Benders and Lagrangean decomposition for two-stage stochastic programming formulations. Our computational experiments on instances of the resilient supply chain design problem show the superior performance of the cross-decomposition algorithm over Benders decomposition and direct solution with commercial MILP solvers. In Chapter 4, we propose a new approach for production planning and inventory management in process networks. Inventory management in these networks is a very challenging task because of the close interaction between production activities and the presence of diverse sources of uncertainty. Our planning strategy is based on implementing basestock policies