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.

题目：生产计划和调度中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存的因素，需要考虑经济最优性、需求满意度、产能限制、生产配方、顺序依赖任务等。现有模型试图在问题的现实描述和解决方案过程方面的可用功能之间建立一种折衷。这个项目的目标是处理生产计划中的不确定性的方法。所规划的集成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。主要结果是为决策者提供在面对不确定性时表现出不同特征的替代解决方案，即预期盈利能力、灵活性和稳健性。为了实现这一目标，将根据可获得的关于不确定性的信息水平来研究以下思想：不确定性空间中的灵敏度分析和参数规划。将研究新的想法，以便在没有足够的信息来描述不确定性的情况下有效地生成参数解。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。最终目标是在考虑不确定性问题的大型单设施生产工厂中优化生产计划操作。现实的案例研究和石化公司，快速消费品制造和化工公司的实际例子将被用来测试和验证提出的分析工具的结果。这项工作的影响超出了具体的工业部门的操作，因为一般的想法和发现可以应用于许多类型的生产设施。通过开发基于网络的环境来传播研究结果，将能够更好地与工业界相互作用。该项目的目标之一是在化工课程中整合过程操作、结果和案例研究的基本概念。重组的设计课程和新引入的跨学科系统工程课程将作为整合工具。