GOALI: Multiscale Decomposition Techniques for the Integration of Optimal Planning and Scheduling of Batch and Continuous Multiproduct Process Systems

这笔目标赠款为多尺度分解技术的开发提供了资金，这些技术将使流程工业中出现的规划和调度问题能够在时间和空间上统一起来。拟议的工作将首先解决多产品连续工厂的同时计划和调度的情况。在第二阶段，它将解决陶氏化学公司提供的一个现实世界问题，该问题涉及地理上分散的制造系统的计划和调度，该系统由必须满足一系列市场需求的间歇反应器组成。对于时间集成，我们打算开发一种双层迭代方案，该方案包括综合MILP计划和详细的MILP调度模型的求解。为了缩小每个问题所预测的界限的差距，将研究一组新颖的逻辑和混合整数切割。对于空间集成，我们将研究一种通过拉格朗日对偶性的多尺度分解方法，该方法将结合双层时间格式。如果成功，拟议的研究有可能在计划和调度领域做出重大贡献，这是实现流程工业企业范围优化目标的基本构件。预计的一个主要成果是采用一种新的方法来整合计划和调度，以实现长期和短期生产目标的一致性和协调性，这可转化为重大的经济节省。虽然拟议的GOALI项目将主要涉及与陶氏化学公司的合作，但它也将涉及与石油、化工和工程/软件公司的潜在合作和传播，这些公司是卡内基梅隆大学高级工艺决策中心(CAPD)的成员。这项研究的结果将作为案例研究被记录下来，并将通过互联网提供，可能使大学和公司受益。

This GOALI grant provides funding for the development of multiscale decomposition techniques that will allow the temporal and spatial integration of planning and scheduling problems that arise in the process industry. The proposed work will initially address the case of simultaneous planning and scheduling of multiproduct continuous plants. In the second phase, it will address a real-world problem provided by The Dow Chemical Company that deals with planning and scheduling of geographically distributed manufacturing systems consisting of batch reactors that must satisfy demands for a set of markets. For the temporal integration we intend to develop a bilevel iterative scheme that involves the solution of aggregate MILP planning and detailed MILP scheduling models. To reduce the gap in the bounds predicted by each of these problems, a novel set of logic and mixed-integer cuts will be investigated. For the spatial integration we will investigate a multiscale decomposition approach through Lagrangean duality that will incorporate the bilevel temporal scheme. If successful, the proposed research has the potential of making significant contributions in the areas of planning and scheduling, which are basic building blocks for achieving the goal of Enterprise-wide Optimization in the process industry. A major result that is expected is a new methodology for integrating planning and scheduling in order to achieve consistency and coordination in long and short term production targets, which can translate into major economic savings. While the proposed GOALI project will primarily involve collaboration with Dow Chemical, it will also involve potential collaboration and dissemination of the research results with the petroleum, chemical and engineering/software companies that are members of the Center for Advanced Process Decision-making (CAPD) at Carnegie Mellon. The findings of this research will be documented as case studies that will be made available through the internet, potentially benefiting universities and companies.