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Home» Products » Aurora » Customer Applications » Aurora-VT (Vehicle Testing)

Aurora-VT (Vehicle Testing)

Aurora Optimizes the Vehicle Testing Process by Selecting the Best Vehicle Configurations to Minimize the Vehicle Count & Overall Schedule Duration

To optimize a major automobile manufacturer’s vehicles for both nondestructive and crash testing, Stottler Henke infused its intelligent planning and scheduling system, Aurora, with domain-specific heuristics used in the vehicle task planning process, resulting in a circa 10% decrease in the total number of vehicles needed to complete the same test/crash suite within the same time constraints. That is, Aurora-VT (Vehicle Testing) meets the same goals as the auto manufacturer already set, but with about 10% fewer vehicles; Aurora-VT has also increased the capability and ease to ask ‘what-if’ questions to further improve the schedule: for example, how much faster the test suite could be completed with only 5% fewer cars, or how many cars would be needed to complete the suite 15% quicker. Some new vehicle models may require over 100 vehicles; to date, Aurora-VT has saved over 10 vehicles, which translates into millions of dollars in savings for only one new vehicle model.

Aurora-VT is fueled by sophisticated artificial intelligence that optimizes automotive test schedules in order to complete work in a given time window while minimizing the total number of vehicles required. Aurora determines how to use fewer vehicles while providing improved transparency into what types of delays will affect the actual end date of the test suite, which not only allows for fewer vehicles to be built, but for enough safety to be built into the schedule so that the project goals of completing all tests within the desired timeframe, and, at the end of the day, verifying the vehicle performs as designed, can be achieved even with the inevitable unexpected events that will occur during the testing process.

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Background

This Motortrend article, 2016 Honda Pilot Named Top Safety Pick + by IIHS (W/Video), describes aspects of vehicle testing, including an informative video with a vehicle impact test.

Publications

Ludwig, J., A. Kalton, R. Richards, B. Bautsch, C. Markusic, C. Jones (2016) Deploying a Schedule Optimization Tool for Vehicle Testing. International Conference on Automated Planning & Scheduling (ICAPS) 2016. London, U.K. June 12-17, 2016. Paper Presentation View Presentation

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Ludwig, J., A. Kalton, R. Richards, B. Bautsch, C. Markusic, J. Schumacher (2014) A Schedule Optimization Tool for Destructive and Non-Destructive Vehicle Tests. Proceedings of the Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI 2014). Paper Presentation View Presentation

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Example of a smaller test suite

Example of a smaller test suite

Example of a larger test suite; note that there is a variable build pitch for the test vehicles being constructed

Example of a larger test suite; note that there is a variable build pitch for the test vehicles being constructed

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Stottler Henke is headquartered at
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The company also operates software development offices in Seattle, WA, Colorado Springs, CO, and Boston, MA.

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Specializing in artificial intelligence since 1988, Stottler Henke delivers software systems that solve problems which defy traditional approaches.
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Aurora may be used as a complement or replaced for Microsoft Project, Primavera P6, Deltek Open Plan, PowerProject, Lynx TameFlow, Being Management 3, Exepron, ProChain, Concerto, Smartsheet, Wrike, Projectmanager, Teamwork, TeamGantt, Clarizen, LiquidPlanner, ProWorkFlow, Workzone, Bitrix24, Easy Project.

The problem solved by Aurora may be referred to as: optimized resource allocation, production scheduling; production planning of batch plants; parallel machine scheduling problem with setups, release and due dates and additional constraints related to the scarce availability of tools and human operators; multi-product multistage batch plant scheduling; multi-stage multi-product batch scheduling; campaign and lot size scheduling problem; economic lot scheduling problem (ELSP); parallel machine scheduling problem with sequence dependent setup times; scheduling problems with setup times and/or costs; sequence-dependent job scheduling; job scheduling using parallel non-identical machines with sequence dependent clearance times.

With the objective function normally being the minimization of makespan or project duration / maximization of throughput, with limited resources. Other solutions provide resource leveling, Aurora provides intelligent resource scheduling optimization.

Approaches used by others that Aurora outperforms, include the following: discrete event simulation; tabu search algorithm; mixed integer programming (MIP) scheduling / mixed integer mathematical model; Iterative two-stage decomposition solution strategy; genetic algorithms; multi-grid continuous-time formulations.