Systematic Management of Uncertainties in Process Operations

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Planning, scheduling and supply chain management represent the key operational activities in the process industry. They play a major role in reducing costs and inventories, as well as increasing profits and responsiveness. In reality, uncertainty widely exists in those process operations problems. Its appearance ranges from the manufacturing process to the dynamic market. Successful process operations rely heavily on the ability to handle uncertainty since decisions made under deterministic assumptions can be suboptimal or even infeasible for practical implementations. However, addressing uncertainty in process operations still faces the following major challenges: 1) even a moderate number of uncertain parameters can lead to a huge number of uncertainty scenarios that exceed current computational capabilities; 2) correlations between uncertain parameters often exist, which require novel mathematical modeling and solution techniques for analysis and optimization; 3) many process operations problems involve combinatorial and nonlinear optimization, and it is difficult to solve such problems under uncertainty. To address the above challenges, a systematic framework for managing uncertainties in process operations will be explored. The proposed research will contain several innovative elements. First, we will investigate various uncertainty analysis techniques to quantify the impact of uncertainties. Second, a generic scenario reduction algorithm will be developed to select representative realizations of uncertain data and to account for the overall uncertainty distribution. Third, we will develop novel robust and stochastic optimization algorithms that can rigorously address both independent and correlated uncertainties for decision making in a computationally efficient manner. Finally, the proposed research program will generate a set of extended optimization tools for comprehensive analysis and optimization of process operations under uncertainty.