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（物料流）模拟是两种非常强大的方法，已成功地用于解决交通和运输物流中广泛的战略、战术和操作问题。然而，这两种方法主要是分别应用于不同类型的问题。离散事件仿真使得具有几乎无限复杂性（包括随机过程）的物流系统的建模非常接近现实。然而，找到最佳的系统配置是非常困难和耗时的，因为有许多备选方案需要评估和比较。相反，离散数学优化有能力为物流问题的（接近）最优解做出非常复杂的决策。由于其复杂性，现实世界的物流系统只能在没有随机行为的情况下在较不准确和详细的水平上建模和求解。本研究项目的主要目标是开发一种新的解决方法，以迭代的方式将离散数学优化和离散事件模拟两种方法紧密联系起来。作为实际应用，我们使用转运终端的包裹和快递服务提供商。对于我们的方法来说，它们几乎是理想的，因为它们结合了人工处理活动、自动分拣技术和必须做出的大量分配决策。该研究项目不仅旨在通过缩小现有的科学和方法差距并超越以前的方法，为联合模拟和优化方法领域贡献有价值的结果。此外，我们希望证明我们的新方法在物流节点决策支持方面的具体实际效益。数学优化和离散事件仿真两种方法相结合，利用它们的互补优势，最终获得比单独使用两种方法更好的结果。

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.