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

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概述单个生产设施的过程操作优化涉及大量相互依赖的因素，需要考虑经济最优化、需求满足、能力限制、生产配方、顺序相关任务等。现有模型试图在问题的现实描述和解决程序方面的可用能力之间找到折衷方案。这个项目的目标是处理生产计划中的不确定性的方法。计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，即预期的盈利能力、灵活性和稳健性。为了实现这一目标，将根据关于不确定性的现有信息水平，研究以下想法：不确定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表征不确定性的情况下，能够有效地产生参数解。在早期决策阶段考虑风险和盈利能力的多目标优化，这将导致解决方案适当地平衡不同的目标。这种方法将在有可用信息来描述不确定参数的情况下使用。最终目标是在考虑不确定性问题的情况下，优化大型、单设施生产工厂的生产计划作业。实际案例研究和来自石化企业、快速消费品制造企业和化工企业的实际案例将被用来检验和验证所提出的分析工具的结果。广泛影响这项工作的影响超越了具体的工业部门业务，因为一般性的想法和结论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结果，将能够更好地与工业界互动。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。重组后的设计课程和新推出的跨学科系统工程课程将用作整合工具。

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.