An analytical approach to prototype vehicle test scheduling. (March 2017) - Shi, Yuhui; Reich, Daniel; Epelman, Marina; Klampfl, Erica; Cohn, Amy
Abstract: The test planning group within Ford׳s Product Development division develops schedules for building prototype vehicles and assigning them to departments in charge of different vehicle components, systems and aspects (e.g., powertrain, electrical, safety). These departments conduct tests at pre-production phases of each vehicle program (e.g., 2015 Ford Fusion, 2016 Ford Escape) to ensure the vehicles meet all requirements by the time they reach the production phase. Each prototype can cost in excess of $200 K because many of the parts and the prototypes themselves are hand-made and highly customized. Parts needed often require months of lead time, which constrains when vehicle builds can start. That, combined with inflexible deadlines for completing tests on those prototypes introduces significant time pressure, an unavoidable and challenging reality. One way to alleviate time pressure is to build more prototype vehicles; however, this would greatly increase the cost of each program. A more efficient way is to develop test plans with tight schedules that combine multiple tests on vehicles to fully utilize all available time. 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. We introduce analytical approaches for obtaining efficient schedules to replace the tedious manual scheduling process engineers undertake for each program. Our models and algorithms save test planners׳ and engineers׳ time, increases their ability to quickly react to program changes, and save resources by ensuring maximal vehicle utilization. Abstract : Highlights: Test scheduling is a key step in design and development of new car models. The goal is to minimize the cost of prototypes used (and crashed!) during testing. This scheduling problem has features of bin packing and parallel machine scheduling. We present fast heuristic methods for generating efficient schedules. New analytical approaches reduce the engineering burden of test scheduling at Ford.