Systematic Management of Uncertainties in Process Operations

流程操作中不确定性的系统管理

基本信息

批准号：
435906-2013
负责人：
Li, Zukui
金额：
$ 1.82万
依托单位：
University of Alberta
依托单位国家：
加拿大
项目类别：
Discovery Grants Program - Individual
财政年份：
2018
资助国家：
加拿大
起止时间：
2018-01-01 至 2019-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=657549
关键词：
Systematic Management Uncertainties Process Operations

项目摘要

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.**

规划，调度和供应链管理代表了过程行业的关键运营活动。他们在降低成本和库存以及增加利润和响应能力方面发挥了重要作用。实际上，在这些过程操作问题中广泛存在不确定性。它的外观范围从制造过程到动态市场。成功的流程操作在很大程度上依赖于处理不确定性的能力，因为确定性假设下的决策对于实际实施而言可能是最佳的甚至是不可行的。但是，解决过程操作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参数也可能导致大量超过当前计算能力的不确定性方案； 2）经常存在不确定参数之间的相关性，这些参数需要新颖的数学建模和解决方案技术才能进行分析和优化； 3）许多过程操作问题涉及组合和非线性优化，并且很难在不确定性下解决此类问题。拟议的研究将包含几个创新元素。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，将开发一种通用方案降低算法，以选择不确定数据的代表性实现并说明整体不确定性分布。第三，我们将开发新颖的鲁棒和随机优化算法，这些算法可以严格解决以计算有效的方式解决决策的独立和相关的不确定性。最后，拟议的研究计划将生成一系列扩展优化工具，用于在不确定性下进行全面分析和优化过程操作。**