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A Schedule Optimization Tool for
Destructive and Non-Destructive
Vehicle Tests
Jeremy Ludwig, Annaka Kalton, and
Robert Ri...
Overview
• Introduction
• Scheduling Framework
• Scheduling UI
• Domain Customization
• Methods
• Results
• Deployment
• C...
Introduction
• Create a schedule for testing new and
refreshed vehicle models
• Only some tasks are destructive
• Most tas...
Constraints
• Temporal
• Calendar
• Ordering
• Resource
• Build Pitch
• Exclusive
• Destructive
Aurora Scheduling Framework
• Create a high-quality schedule
• Based on a model of temporal, calendar,
ordering, and resou...
6
7
Scheduling UI
Initial Schedule by Resource
Scheduling Framework
Components
• Schedule Initialization
• Preprocessor
• Prioritizer
• Scheduling Loop
• Priortizier
• S...
Differences From Prototype
• Testing on more complex models that
require over 100 vehicles
• Utilizing facility and person...
Domain Specific Customization
• User Interface
• Wizard
• Model Verification during Import
• Build Pitch
• Manage Vehicles...
Wizard
Build Pitch
Manage Vehicles
Long Tasks
Optimization Dashboard
Scheduling Component
Customization
• Preprocessor
• Override project end date
• Prioritizer
• Exclusive Task
• Long Task
•...
Methods
• Actual Model
• ~340 tasks
• ~4000 days of work
• ~30-50 vehicle types
• Manual Solution
• Not attempted
Results
• Aurora Solution
• 60-200 Vehicles
• Adhere to all constraints
• Represents 6% reduction from best estimate
• Pro...
Optimized Schedule
Deployment
• Deployed and in use by novice planners
• Previous solution no longer used
• Integrated with enterprise system...
Conclusion
• Complex, real-world, scheduling problem
• Added domain-specific heuristics to a
general intelligent schedulin...
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A Schedule Optimization Tool for Destructive and Non-Destructive Vehicle Tests

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Whenever an auto manufacturer refreshes an existing car or
truck model or builds a new one, the model will undergo
hundreds if not thousands of tests before the factory line and
tooling is finished and vehicle production begins. These
tests are generally carried out on expensive, custom-made
prototype vehicles because the new factory lines for the
model do not exist yet. The work presented in this paper
describes how an existing intelligent scheduling software
framework was modified to include domain-specific
heuristics used in the vehicle test planning process. The
result of this work is a scheduling tool that optimizes the
overall given test schedule in order to complete the work in
a given time window while minimizing the total number of
vehicles required for the test schedule. The tool was
validated on the largest testing schedule for an updated
vehicle to date. This model exceeded the capabilities of the
existing manual scheduling process but was successfully
handled by the tool. Additionally the tool was expanded to
better integrate it with existing processes and to make it
easier for new users to create and optimize testing
schedules.

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A Schedule Optimization Tool for Destructive and Non-Destructive Vehicle Tests

  1. 1. A Schedule Optimization Tool for Destructive and Non-Destructive Vehicle Tests Jeremy Ludwig, Annaka Kalton, and Robert Richards Stottler Henke Associates, Inc. Brian Bautsch, Craig Markusic, and Cyndi Jones Honda R&D Americas, Inc. ICAPS-SPARK, June 2016
  2. 2. Overview • Introduction • Scheduling Framework • Scheduling UI • Domain Customization • Methods • Results • Deployment • Conclusion
  3. 3. Introduction • Create a schedule for testing new and refreshed vehicle models • Only some tasks are destructive • Most tasks are non-destructive but may have other constraints • Test vehicles hand-built • Build order • Not all available at once • Variety of models • Frame, Market, Drivetrain, and Trim • Project end date defined externally • Limited personnel and facility resources
  4. 4. Constraints • Temporal • Calendar • Ordering • Resource • Build Pitch • Exclusive • Destructive
  5. 5. Aurora Scheduling Framework • Create a high-quality schedule • Based on a model of temporal, calendar, ordering, and resource constraints • Uses graph analysis techniques and heuristic-based scheduling • Customized for domain • Handle special kinds of tasks • Exclusive, Destructive • Minimize the number of vehicles required • Select the types of vehicles built • Select a build order for the vehicles
  6. 6. 6
  7. 7. 7
  8. 8. Scheduling UI
  9. 9. Initial Schedule by Resource
  10. 10. Scheduling Framework Components • Schedule Initialization • Preprocessor • Prioritizer • Scheduling Loop • Priortizier • Scheduler • Quality Criterion • Conflict Manager • Schedule Finalization • Postprocessor
  11. 11. Differences From Prototype • Testing on more complex models that require over 100 vehicles • Utilizing facility and personnel constraints when creating a schedule • Supporting the transition of the software into the hands of the actual planners
  12. 12. Domain Specific Customization • User Interface • Wizard • Model Verification during Import • Build Pitch • Manage Vehicles • Long Tasks • Optimization Dashboard • Scheduling Components
  13. 13. Wizard
  14. 14. Build Pitch
  15. 15. Manage Vehicles
  16. 16. Long Tasks
  17. 17. Optimization Dashboard
  18. 18. Scheduling Component Customization • Preprocessor • Override project end date • Prioritizer • Exclusive Task • Long Task • Destructive Task • Tight Window • End Based • Load Based • Subsequent Duration
  19. 19. Methods • Actual Model • ~340 tasks • ~4000 days of work • ~30-50 vehicle types • Manual Solution • Not attempted
  20. 20. Results • Aurora Solution • 60-200 Vehicles • Adhere to all constraints • Represents 6% reduction from best estimate • Prototype found 12% reduction in direct comparison • Schedule created in 2 minutes from model vs. days of labor • Spend time using ‘What-if’ capability further improve the schedule • Time • Build Pitch • Negotiation
  21. 21. Optimized Schedule
  22. 22. Deployment • Deployed and in use by novice planners • Previous solution no longer used • Integrated with enterprise system • Input data extracted from external data • Results exported to corporate format • Providing huge savings and other benefits with every new test suite
  23. 23. Conclusion • Complex, real-world, scheduling problem • Added domain-specific heuristics to a general intelligent scheduling framework • Added help for novice planners • Generated schedule for vehicle testing • Significant reduction in the number of prototype vehicles required • Still completed in the given timeframe • Extend to multiple projects in future work

Whenever an auto manufacturer refreshes an existing car or truck model or builds a new one, the model will undergo hundreds if not thousands of tests before the factory line and tooling is finished and vehicle production begins. These tests are generally carried out on expensive, custom-made prototype vehicles because the new factory lines for the model do not exist yet. The work presented in this paper describes how an existing intelligent scheduling software framework was modified to include domain-specific heuristics used in the vehicle test planning process. The result of this work is a scheduling tool that optimizes the overall given test schedule in order to complete the work in a given time window while minimizing the total number of vehicles required for the test schedule. The tool was validated on the largest testing schedule for an updated vehicle to date. This model exceeded the capabilities of the existing manual scheduling process but was successfully handled by the tool. Additionally the tool was expanded to better integrate it with existing processes and to make it easier for new users to create and optimize testing schedules.

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