Systematic Management of Uncertainties in Process Operations

计划、调度和供应链管理是流程工业的关键业务活动。它们在降低成本和库存以及增加利润和响应能力方面发挥着重要作用。在实际生产中，不确定性广泛存在于生产过程中。它的外观范围从制造过程到动态市场。成功的过程操作在很大程度上依赖于处理不确定性的能力，因为在确定性假设下做出的决策可能是次优的，甚至对于实际实施是不可行的。然而，处理过程操作中的不确定性仍然面临以下主要挑战：1）即使是中等数量的不确定性参数也会导致超过当前计算能力的大量不确定性场景; 2）不确定性参数之间经常存在相关性，这需要新颖的数学建模和求解技术进行分析和优化; 3）许多过程操作问题涉及组合优化和非线性优化，在不确定性条件下求解这些问题是困难的。为了应对上述挑战，将探讨一个管理工艺操作中不确定性的系统框架。拟议的研究将包含若干创新内容。首先，我们将研究各种不确定性分析技术，以量化不确定性的影响。其次，一个通用的场景简化算法将被开发，以选择代表实现的不确定数据，并占整体的不确定性分布。第三，我们将开发新的鲁棒性和随机优化算法，可以严格解决独立和相关的不确定性，决策在计算效率的方式。最后，拟议的研究计划将产生一套扩展的优化工具，用于不确定性下的过程操作的综合分析和优化。

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.**