Scheduling tests in automotive R&D projects using a genetic algorithm

Abstract

For each car model an automotive manufacturer has to perform hundreds of tests on prototype vehicles before mass production can be started. In this chapter we present heuristic methods for scheduling the individual tests in automotive R & D projects such that the number of required experimental vehicles and hence the testing costs are minimized. The problem at hand can be interpreted as a multi-mode resource-constrained project scheduling problem with minimum and maximum time lags and cumulative resources. We present forward and backward variants of a priority-rule based method as well as a genetic algorithm based on an activity list representation. The presented methods are examined in a comprehensive computational study.