Optimization Models and Algorithms for Prototype Vehicle Test Scheduling

Scheduling;Optimization;Integer programming;Decomposition method;Industrial and Operations Engineering;Engineering;Industrial & Operations Engineering

Shi, YuhuiReich, Daniel ;

University of Michigan

关键词: Scheduling;    Optimization;    Integer programming;    Decomposition method;    Industrial and Operations Engineering;    Engineering;    Industrial & Operations Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/137071/yuhuishi_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Automotive makers conduct a series of tests at pre-production phases of each new vehicle model development program. The main goal of those tests is to ensure that the vehicle models meet all design requirements by the time they reach the production phase. These tests target different vehicle components or functions, such as powertrain systems, electrical systems, safety aspects, etc. However, one big issue is that the cost of the resources, mainly prototype vehicles, invested in the testing process is exceedingly expensive. An individual prototype vehicle can cost over 5 times its counterpart’s price in the commercial market because many of the parts and the prototype vehicles themselves are highly customized and produced in small batches. Parts needed often require months of lead time, which constrains when vehicle builds can start. That, combined with inflexible time-window constraints for completing tests on those prototypes introduces significant time pressure, an unavoidable and challenging reality. What makes the problem even more difficult is that in addition to the prototype vehicle resources, there are other constrained supporting resources involved during the execution of those tests, such as testing facilities, instruments and equipment like cameras and sensors, human-power availability, etc.An efficient way to conquer the problem is to develop test plans with tight schedules that combine multiple tests on vehicles to fully utilize all available time while balancing the loads of other supporting resources. There are many challenges that need to be overcome in implementing this approach, including complex compatibility relationships between the tests and destructive nature of, e.g., crash tests.In this thesis, we show how to mathematically model these test scheduling problems as optimization problems. We develop corresponding solution approaches that enable quick generation of an efficient schedule to execute all tests while respecting all constraints. Our models and algorithms save test planners’ and engineers’ time, increase q their ability to quickly react to program changes, and save resources by ensuring maximal vehicle utilization.