TY - GEN
T1 - Scheduling Heterogeneous Fleets with Skill and Temporal Synchronisation for Automotive Testing
AU - Fina, Robert
AU - Gattringer, Hubert
AU - Müller, Andreas
AU - Reischl, Daniel
AU - Fritz, Martin
PY - 2025/6/25
Y1 - 2025/6/25
N2 - The increased complexity of vehicle testing can be attributed to the rapid development of technical advancements within the automotive industry, thereby prolonging the time to market of a product. The process of allocating and coordinating vehicle tests at proving grounds (PGs) is a complex and time-consuming task. Currently, this process is still performed manually, which is inefficient. This study proposes a methodology for assigning scenarios to designated sites, taking into account travel aspects between locations and fulfilling participant requirements. The allocation procedure is formulated as an Open Job Shop Scheduling problem with temporal synchronisation and skill matching, and is solved by the Constraint Programming tool Google OR-Tools. The efficacy of the approach is demonstrated by its ability to generate a close-to-optimal schedule to fulfil customer requests. Case studies demonstrate that a combination of two distinct objectives are essential to meet the demands of compactness and time efficiency. The findings of this study provide a solid foundation for enhancing automation at a PG, thereby improving efficiency and optimising testing processes.
AB - The increased complexity of vehicle testing can be attributed to the rapid development of technical advancements within the automotive industry, thereby prolonging the time to market of a product. The process of allocating and coordinating vehicle tests at proving grounds (PGs) is a complex and time-consuming task. Currently, this process is still performed manually, which is inefficient. This study proposes a methodology for assigning scenarios to designated sites, taking into account travel aspects between locations and fulfilling participant requirements. The allocation procedure is formulated as an Open Job Shop Scheduling problem with temporal synchronisation and skill matching, and is solved by the Constraint Programming tool Google OR-Tools. The efficacy of the approach is demonstrated by its ability to generate a close-to-optimal schedule to fulfil customer requests. Case studies demonstrate that a combination of two distinct objectives are essential to meet the demands of compactness and time efficiency. The findings of this study provide a solid foundation for enhancing automation at a PG, thereby improving efficiency and optimising testing processes.
UR - https://ieeexplore.ieee.org/document/11097588/
UR - https://www.scopus.com/pages/publications/105014241274
U2 - 10.1109/IV64158.2025.11097588
DO - 10.1109/IV64158.2025.11097588
M3 - Conference proceedings
SN - 979-8-3315-3804-0
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 2054
EP - 2060
BT - IV 2025 - 36th IEEE Intelligent Vehicles Symposium
PB - IEEE
T2 - 2025 IEEE Intelligent Vehicles Symposium (IV)
Y2 - 22 June 2025 through 25 June 2025
ER -