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https://doi.org/10.1007/978-3-...
Part of book or chapter of book . 2023 . Peer-reviewed
License: CC BY
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Improvement of the Scheduling of Automotive Testing Processes Based on Production Scheduling Methods

Authors: Leon Stütz; Timo König; Roman Bader; Markus Kley;

Improvement of the Scheduling of Automotive Testing Processes Based on Production Scheduling Methods

Abstract

AbstractIncreasing challenges in the automotive industry are caused by shorter development times for products, greater diversity of variants and increasing cost pressure. Testing plays an elementary role within the product development process (PDP). There are already many publications that deal with the early phases of the PDP, but relatively few that address testing. Inefficient scheduling leads to suboptimal use of development and testing resources.Automotive testing is characterized by high momentum and process complexity. The complexity of testing is determined, among other things, by the number of test rigs in a test field, the number and diversity of test objects, the type of testing and the preparatory setups. In addition, complex testing processes at the component and system level require a large number of human and material resources, whose time availability and sequence must be coordinated with the testing process. The sequence planning is subject to a high inherent dynamic because unexpected changes and disturbances of the process can occur during the testing. These changes require a rescheduling of the testing process. If done manually, the rescheduling results in high costs.Based on known production planning methods, a solution approach is derived for improved utilization of test field resources for the automotive sector. The planning is optimized with a multitude of product - and process-related dependencies and restrictions using mixed-integer linear programming, a standardized method from operations research. The test field is simulated via a discrete event simulation. The proposed method considers the availability of essential resources.

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
hybrid