Combining mathematical optimization and stochastic simulation for a robust integrated vehicle and crew scheduling in public transport

• Applicant: Professor Dr.-Ing. Uwe Clausen
• Subject Area: Traffic and Transport Systems, Intelligent and Automated Traffic
• Term: from 2015 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 238487308

As a part of the research unit "lntegrated planning in public transport" this subproject deals with the combination of mathematical optimization and stochastic simulation for a robust integrated vehicle and crew scheduling in public transport. We differentiate between two kinds of goals within the subproject. From a problem point of view, we want to generate robust vehicle and crew schedules in public transport. From a methodical point of view, we want to develop models and solution approaches for an integrated vehicle and crew scheduling problem in public transport that considers disturbances in operation. For this purpose, we focus on the development of an iterative approach that combines mathematical optimization with simulation in order to achieve robust schedules. 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. ln 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 Ievel without stochastic behavior. 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. Furthermore, we want to prove the concrete practical benefit of our new approach for an integrated planning problem in public transport. By making use of their complementary advantages, the combination of the two methods mathematical optimization and discrete-event Simulation eventually Ieads to better results than one of both methods could achieve alone.The collaboration with other subprojects within the research unit especially takes place in the fields of adjacent planning steps in public transport, algorithm design (decomposition), and the analysis and measurement of robustness.

• DFG Programme: Research Units
• Subproject of: FOR 2083: Integrated Planning for Public Transportation