A chemical supply chain is an integrated network of facilities and transportation options for the procurement of raw materials, transformation of raw materials into intermediate and final products, and distribution of the final products to customers. Due to the increasing pressure to remain competitive in the global marketplace, it has become one of the major goals for most chemical companies to ensure optimal design and operation of not only the chemical production processes but also the entire chemical supply chains. This involves optimizing the network and process design, capacity and production planning, distribution and allocation planning, detailed scheduling and inventory control of a chemical supply chain to reduce the overall cost, and to maximize the profits, responsiveness and customer satisfaction. Furthermore, the supply, manufacturing, and distribution activities of a chemical supply chain have to deal with many uncertainties, such as demands, process yields, prices, breakdowns and natural disasters. Explicitly considering these uncertainties in the design and operation will add more complexity to the optimization models and consequently lead to greater algorithmic challenges. We are interested in the development of mathematical and computational tools that address the following areas: 1) Modeling of the design, planning and scheduling problems for chemical supply chains; 2) Multi-scale optimization to coordinate decision-making across geographically distributed locations and across time horizons spanning from days to years; 3) Optimization under uncertainty to account for stochastic variations and to manage the risks; 4) Algorithms and decomposition methods to support the three previous points.

In this talk, we will focus on three examples about chemical supply chain optimization under uncertainty. The first example is about the design, planning and scheduling of chemical supply chains under responsive criterion and economic criterion with the presence of demand uncertainty. A bi-criterion mixed-integer nonlinear programming (MINLP) model is developed to take into account multiple tradeoffs and to simultaneously predict the optimal locations of manufacturing sites and distribution centers, process technology, production profiles, detailed schedules, and inventory levels under different specifications of supply chain responsiveness. The second one addresses the design of chemical supply chains with multi-echelon inventory under uncertainty while taking into account risk-pooling effect. The steady-state network design and transportation decisions are integrated well with the stochastic inventory decisions by using an MINLP model, of which the large-scale instances are solved effectively by a tailored global optimization algorithm. The last example is a general computational framework for global chemical supply chain planning under uncertainty. The model formulation, computational strategies and simulation method will be discussed. Real-world industrial applications with up to 12,000 uncertain parameters are investigated to illustrate the economic benefits of considering uncertainties. A few other related projects will also be discussed along with these three examples. We will conclude this talk with some future extensions of these works to address the problems in energy & sustainability areas.