Integrated Framework for Operational Planning and Scheduling Under Uncertainty

不确定条件下的作业计划与调度集成框架。Floudas摘要该基金为基于新型优化方法和工具的系统方法的开发提供资金，这些方法和工具用于在化学过程工业中大规模批量和连续过程的不确定性下有效集成操作规划，短期调度和中期调度。开发的计算方法和工具将确定最佳的操作计划和详细的批量和连续过程的生产进度，以及建立操作计划和调度之间的权衡存在的不确定性的产品需求，成本，加工时间和速率。介绍了一种新的生产分解作业计划建模方法，该方法可以解决大规模批量和连续生产的问题。将制定一个适用于大规模单地点和多地点分批和连续工厂的运营规划和中期调度一体化框架。在产品需求的不确定性下的操作计划的鲁棒优化方法将被开发并应用于大规模间歇和连续化工厂。如果成功的话，本研究的结果将导致显着改善的操作计划和生产调度下的不确定性的化工过程。这项工作的主要目标是开发新的优化方法和工具，解决由于不同类型的不确定性的复杂性;原材料，中间和最终产品的物料平衡;中间/最终存储和清理要求;可变的处理时间和速率;替代站的处理;中间到期日;和资源限制。拟议的工作还将有助于更快地响应化工、制药和离散制造工艺的市场需求。

Integrated Framework for Operational Planning and Scheduling under Uncertainty Professor C.A. Floudas Abstract This grant provides funding for the development of systematic approaches based on novel optimization methods and tools for the effective integration of operational planning, short-term scheduling and medium-term scheduling under uncertainty for large scale batch and continuous processes in the chemical process industry. The developed computational methods and tools will determine the optimal operational plan and detailed production schedule of batch and continuous processes, as well as establish the trade-offs between operational planning and scheduling in the presence of uncertainty in the product demands, costs, and processing times and rates. A new modeling approach for the operational planning with production disaggregation will be introduced that can address large scale batch and continuous plants. A framework for the integration of operational planning and medium-term scheduling, applicable to large-scale single-site and multi-site batch and continuous plants will be developed. A robust optimization approach for operational planning under uncertainty in product demands will be developed and applied to large scale batch and continuous chemical plants. If successful, the results of this research will lead into significant improvements in the operational planning and production scheduling under uncertainty of chemical processes. The primary goal of this work is to develop novel optimization methods and tools that address the complexity due to different types of uncertainty; material balances on raw materials, intermediate and final products; intermediate/final storage and cleanup requirements; variable processing times and rates; treatment of alternative stations; intermediate due dates; and resource constraints. The proposed work will also contribute to attaining faster response to market demand of chemical, pharmaceutical, and discrete manufacturing processes.