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

物流系统的操作控制包括随着时间的推移反复做出决策。特性要求包括不断变化的数据，这些数据受未来发展不确定性的影响。由于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，一个接一个地重复执行数据分析和决策这两个领域的方法是不够的。相反，数据分析和优化过程与方法管道的重叠和相互补充的协调和集成，需要根据相应物流应用的具体情况，随着时间的推移，促进目标和数据驱动的决策制定。现有的研究缺乏一致的方法，该方法将充分考虑动态背景下数据和决策制定之间的相互作用，因为它经常出现在操作后勤中。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;时间动态优化问题是在在线优化，多阶段鲁棒优化或多阶段随机规划的数学学科中处理的;物流问题大多是通过一种固定的决策方法来研究的，而没有特别考虑或仅仅没有充分考虑相关的动态数据处理。因此，该研究项目的主要目标是开发动态数据驱动的物流决策（4D 4L）-元模型，该模型能够综合考虑整个可用数据链，总体目标以及数据分析和决策方法，包括它们在时间动态物流应用背景下的相互作用。4D 4L元模型将数据分析和决策方法以自适应和反馈耦合的方式结合到方法管道中，从而实现物流系统的数据和目标导向控制。这导致有可能提供一个结构化的方法库的时间动态物流系统，以支持决策过程中的目标为导向的方式与合适的数据准备方法。元模型是通用的，它允许适应不同类型的物流系统。为了验证元模型，该项目的重点是仓库业务领域被认为是一个有代表性的例子，时间动态物流系统。从长远来看，该研究项目有助于实现决策支持方法管道的自动化数据和目标驱动组合。

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.