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)

• Applicant: Professor Dr.-Ing. Uwe Clausen
• Subject Area: Traffic and Transport Systems, Intelligent and Automated Traffic
• Term: from 2013 to 2017
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 243235543

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.

• DFG Programme: Research Grants