Systematic Mathematical Strategies for Stochastic Modeling and Uncertainty in Production Planning and Scheduling

生产计划和调度中随机建模和不确定性的系统数学策略

• 批准号: 0625515
• 负责人: Marianthi Ierapetritou
• 金额: --
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0625515&HistoricalAwards=false
• 关键词: Systematic; Mathematical; Strategies; Stochastic; Modeling

ABSTRACTPI: Marianthi Ierapetritou Institution: Rutgers UniversityProposal Number: 0625515Title: Systematic Mathematical Strategies for Stochastic Modeling and Uncertainty in Production Planning and SchedulingProject SummaryOptimization of process operations for a single production facility involves a large number of interdependent factors that need to be considered involving economic optimality, demand satisfaction, capacity limitations, production recipe, sequence dependent tasks, etc. Existing models attempt to develop a compromise between a realistic description of the problem, and available capabilities in terms of solution procedures. This project targets methodologies to deal with uncertainty in production planning. The integrated framework that is planned targets a key issue in the area of optimizing production systems, which is the problem of uncertainty. The main outcome is to provide alternative solutions to the decision maker that exhibit different characteristics in the face of uncertainty, namely expected profitability, flexibility, and robustness. To achieve this target, the following ideas will be investigated depending on the level of information available about uncertainty: Sensitivity analysis and parametric programming in the uncertainty space. New ideas will be investigated that will enable the efficient generation of parametric solutions for the case where there is not sufficient information to characterize uncertainty. Multi-objective optimization considering risk and profitability in the early decision stages that will result in solutions that balance appropriately the different objectives. This approach will be employed in the cases where there is available information to describe the uncertain parameters. The ultimate goal is to optimize the production planning operations in large-scale, single-facility production plants considering the issues of uncertainty. Realistic case studies and actual examples from petrochemical companies, fast moving consumer goods manufacturing and chemical companies will be utilized to test and verify the results of the proposed analysis tools. Broad Impact The impact of the work goes beyond specific industrial sector operations since the general ideas and findings can be applied to many types of production facilities. The dissemination of the results through the development of a web-based environment will enable better interactions with industry. Among the targets of this project is the integration of the basic concepts of process operations, results and case studies within the chemical engineering curriculum. A restructured design course and a newly introduced interdisciplinary systems engineering course will be used as integration tools.

摘要PI：Marianthi Ierapetritou 机构：罗格斯大学提案编号：0625515标题：生产计划和调度中的随机建模和不确定性的系统数学策略项目摘要单个生产设施的工艺操作优化涉及大量相互依赖的因素，需要考虑包括经济优化、需求满足、产能限制、生产配方、顺序相关任务等。现有模型试图制定折衷方案问题的现实描述与解决方案程序方面的可用能力之间的关系。该项目的目标是处理生产计划中的不确定性的方法。规划中的综合框架针对的是优化生产系统领域的一个关键问题，即不确定性问题。主要成果是为决策者提供替代解决方案，这些解决方案在面对不确定性时表现出不同的特征，即预期盈利能力、灵活性和鲁棒性。为了实现这一目标，将根据不确定性的可用信息水平研究以下想法：不确定性空间中的敏感性分析和参数规划。我们将研究新的想法，以便在没有足够信息来表征不确定性的情况下有效生成参数解决方案。 在早期决策阶段考虑风险和盈利能力的多目标优化，将产生适当平衡不同目标的解决方案。这种方法将用于有可用信息来描述不确定参数的情况。最终目标是考虑不确定性问题，优化大型、单一设施生产工厂的生产计划运营。将利用来自石化公司、快速消费品制造和化工公司的现实案例研究和实际例子来测试和验证所提出的分析工具的结果。广泛的影响 这项工作的影响超出了特定工业部门的运作，因为一般想法和发现可以应用于多种类型的生产设施。通过开发基于网络的环境来传播结果将有助于与业界更好的互动。该项目的目标之一是将过程操作、结果和案例研究的基本概念整合到化学工程课程中。重组的设计课程和新引入的跨学科系统工程课程将作为集成工具。