Development of a combined approach that links discrete mathematical optimization and stochastic simulation for planning and operating logistics nodes (applied to transshipment terminals in the parcel delivery industry)

离散数学优化和离散事件(物流)模拟是两种非常有效的方法，已经被成功地用于解决交通运输物流中广泛的战略、战术和操作问题。然而，这两种方法主要是针对不同类型的问题单独应用的。离散事件仿真使物流系统的建模具有几乎无限的复杂性(包括随机过程)，非常接近实际。然而，找到最佳的系统配置是非常困难和耗时的，因为有许多替代方案需要评估和比较。相比之下，离散数学优化能够为物流问题的(接近)最优解决方案做出非常复杂的决定。由于物流系统的复杂性，在没有随机行为的情况下，现实世界的物流系统只能在较不精确和详细的水平上建模和求解。这一研究项目的主要目标是开发一种新的求解方法，将离散数学优化和离散事件模拟这两种方法以迭代的方式紧密联系在一起。作为实际应用，我们使用了包裹和快递服务商的转运终端。它们可以被认为是我们方法的近乎理想的，因为它们结合了手动处理活动、自动分类技术和必须做出的大量分配决策。这一研究项目的目的不仅是通过缩小现有的科学和方法差距并超越以往的方法，为组合模拟和优化方法领域贡献宝贵的成果。此外，我们想要证明我们的新方法在物流节点决策支持中的具体实际效益。利用它们的互补优势，数学优化和离散事件仿真相结合，最终得到比两种方法中的任何一种方法都更好的结果。

Discrete mathematical optimization and discrete-event (material flow) simulation represent two very powerful methods that have been successfully used in order to solve a wide range of strategic, tactical and operational problems in traffic and transport logistics. The two methods, however, are mainly applied separately for different types of problems. The discrete-event simulation allows the modeling of logistics systems with almost unlimited complexity (including stochastic processes) very close to reality. However, finding the best system configuration is very difficult and time-consuming since there are many alternative scenarios that have to be evaluated and compared. In contrast, discrete mathematical optimization has the ability to make very complex decisions for (near) optimal solutions of logistical problems. Due to their complexity, real world logistic systems can only be modeled and solved on a less accurate and detailed level without stochastic behavior. The main objective of this research project is the development of a new solution approach that closely links the two methods discrete mathematical optimization and discrete-event simulation in an iterative way. As practical application we use transshipment terminals of a parcel and express service provider. They can be considered as almost ideal for our approach because of the combination of manual handling activities, automatic sorting technology and a large number of assignment decisions that have to be made. This research project does not only aim at contributing valuable results to the field of combined simulation and optimization approaches by closing existing scientific and methodological gaps and going beyond previous approaches. Furthermore, we want to prove the concrete practical benefit of our new approach for the decision support in logistics nodes. By making use of their complementary advantages, the combination of the two methods mathematical optimization and discrete-event simulation eventually leads to better results than one of both methods could achieve alone.