Flexible scheduling of diagnostic tests in automotive manufacturing

Simone König (), Maximilian Reihn, Felipe Gelinski Abujamra, Alexander Novy and Birgit Vogel-Heuser
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Simone König: Institute of Automation and Information Systems, Technical University of Munich
Maximilian Reihn: Mercedes-Benz AG
Felipe Gelinski Abujamra: Mercedes-Benz AG
Alexander Novy: Mercedes-Benz AG
Birgit Vogel-Heuser: Institute of Automation and Information Systems, Technical University of Munich

Flexible Services and Manufacturing Journal, 2023, vol. 35, issue 2, No 2, 320-342

Abstract: Abstract The car of the future will be driven by software and offer a variety of customisation options. Enabling these customisation options forces modern automotive manufacturers to update their standardised scheduling concepts for testing and commissioning cars. A flexible scheduling concept means that every chosen customer configuration code must have its own testing procedure. This concept is essential to provide individual testing workflows where the time and resources are optimised for every car. Manual scheduling is complicated due to constraints on time, predecessor-successor relationships, mutual exclusion criteria, resources and status conditions on the car engineering and assembly line. Applied methods to handle the mathematical formulation for the corresponding industrial optimisation problem and its implementation are not yet available. This paper presents a procedure for automated and non-preemptive scheduling in the testing and commissioning of cars, which is built on a Boolean satisfiability problem on parallel and identical machines with temporal and resource constraints. The presented method is successfully implemented and evaluated on a variant assembly line of an automotive Original Equipment Manufacturer. This paper is the starting point for an automated workflow planning and scheduling process in automotive manufacturing.

Keywords: Non-preemptive scheduling; Multiple constraints; Boolean satisfiability problem; Automotive testing; Car manufacturing (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10696-021-09438-3

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