Optimal Procurement Contract Selection with Price Optimization under Uncertainty for Process Networks B.A. Calfa a , I.E. Grossmann a,∗ a Department of Chemical Engineering. Carnegie Mellon University. Pittsburgh, PA 15213, USA. Abstract In this work, we propose extending the production planning decisions of a chemical process network to include optimal contract selection under uncertainty with suppliers and product selling price optimization. We use three quantity-based contract models: discount after a certain purchased amount, bulk discount, and fixed duration contracts. We propose the use of general regression models to describe the relationship between selling price, demand, and possibly other predictors, such as economic indicators. For illustration purposes, we consider three demand-response models (i.e., selling price as a function of demand) that are typically encountered in the literature: linear, constant-elasticity, and logit. We develop a mixed- integer nonlinear two-stage stochastic programming that accounts for uncertainty in both supply (e.g., raw material spot market price) and demand (random nature of the residuals of the regression models) for the planning of the process network. The proposed method is illustrated with two numerical examples of chemical process networks. Keywords: Optimal Contract Selection, Price Optimization, Uncertainty, Process Network Production Planning 2000 MSC: 90C15, 90C90 1. Introduction Manufacturing enterprises deal with uncertainty from both internal and external sources. Internally, production variability due to unplanned events may prevent the company to achieve its demand-driven production targets. Externally, fluctuations in supply and demand as well as market economic conditions pose challenges to efficient operation of the supply chain. One way to reduce the level of uncertainty on both the supply and the customer sides, and that is typically used by companies, is by making contractual agreements. In the context of this paper, a contract is a binding agreement in which the seller provides the specified product and the buyer pays for it under specific terms and conditions. A different approach to managing uncertainty is pricing analytics, also known as price optimization. In formulating such a problem, selling prices become decision variables, and the demand of a product is modeled as a function of its price. Nonetheless, this still typically * Corresponding author Email addresses: bacalfa@cmu.edu (B.A. Calfa), grossmann@cmu.edu (I.E. Grossmann) Preprint submitted to Elsevier July 19, 2015