Data- and goal-driven sequential decision making for time-dynamic logistics systems

时间动态物流系统的数据和目标驱动的顺序决策

• 批准号: 502552827
• 负责人: Professor Dr.-Ing. Kai Furmans
• 金额: --
• 依托单位: Institut für Fördertechnik und Logistiksysteme (IFL)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/502552827?language=en
• 关键词: Data; goal; driven; sequential; decision

The operational control of logistics systems consists of making decisions repetitively over time. Characteristic requirements comprise constantly changing data subject to uncertainty about future developments. Due to the complexity and dynamics of occurring decision situations, it is not sufficient to repeatedly execute methods from the two areas of data analysis and decision making one after another in order to ensure effective and efficient operations of logistics systems. Rather, an overlapping and mutually complementary coordination and integration of data analysis and optimization processes to method pipelines is required to facilitate goal- and data-driven decision making over time according to the specifics of the respective logistics application.Existing research lacks a consistent approach that would adequately take into account the described interactions between data and decision making in a dynamic context, as it is often found in operational logistics. Rather, these issues have been addressed in different communities so far: The analysis of (raw) data is done in the fields of statistics, machine learning and data engineering; time-dynamic optimization problems are treated in the mathematical disciplines of online optimization, multi-stage robust optimization, or multi-stage stochastic programming; logistics issues are mostly investigated by means of a fixed methodology for decision making without special or only insufficient consideration of associated dynamic data processes. Thus, the main objective of the research project is on developing the Dynamic Data-Driven Decisions for Logistics (4D4L)-metamodel that enables an integrated consideration of the entire chain of available data, overall goals, and methods of data analysis and decision making including their interaction effects in the context of time-dynamic logistics applications. The 4D4L-metamodel combines data analysis and decision making methods to method pipelines in a an adaptive and feedback-coupled manner that enables the data- and goal-oriented control of logistics systems. This results in the possibility to provide a structured method repository for time-dynamic logistics systems to support decision processes in a goal-oriented way linked with suitable data preparation methods. The metamodel is general enough in the sense that it allows adaptation to different types of logistics systems. To validate the metamodel, this project focuses on the area of warehouse operations which is considered to be a representative example of time-dynamic logistics systems. In the long run, the research project contributes to the realization of an automated data- and goal-driven composition of method pipelines for decision support.

物流系统的运行控制是由不断重复的决策组成的。特征需求包括不断变化的数据，受制于未来发展的不确定性。由于发生决策情况的复杂性和动态性，为了确保物流系统的有效和高效运行，仅仅重复执行数据分析和决策两个领域的方法是不够的。相反，根据各自物流应用的具体情况，需要将数据分析和优化过程与方法管道进行重叠和互补的协调和集成，以促进目标和数据驱动的决策。现有的研究缺乏一种一致的方法，无法充分考虑到动态环境中所描述的数据和决策之间的相互作用，就像在运营物流中经常发现的那样。相反，到目前为止，这些问题已经在不同的社区得到了解决：（原始）数据的分析是在统计学、机器学习和数据工程领域完成的；时间动态优化问题在在线优化、多阶段鲁棒优化或多阶段随机规划等数学学科中得到处理；物流问题大多是通过一种固定的决策方法来调查的，没有特别或仅仅没有充分考虑相关的动态数据过程。因此，研究项目的主要目标是开发物流动态数据驱动决策（4D4L）元模型，该模型能够综合考虑整个可用数据链、总体目标和数据分析和决策方法，包括它们在时间动态物流应用环境中的相互作用。4d4l元模型将数据分析和决策方法结合起来，以自适应和反馈耦合的方式对管道进行分析，从而实现物流系统的数据和目标导向控制。这使得有可能为时间动态的物流系统提供结构化的方法存储库，以与合适的数据准备方法相关联的面向目标的方式支持决策过程。元模型在某种意义上足够通用，它允许适应不同类型的物流系统。为了验证元模型，本项目将重点放在仓库操作领域，该领域被认为是时间动态物流系统的代表性示例。从长远来看，该研究项目有助于实现决策支持的自动化数据和目标驱动的方法管道组合。