Vehicle sequencing based on evolutionary computation

A critical component for the efficient manufacturing of vehicles is an assembly scheduling tool that allows for the economic utilization of time and resources. From a computational perspective, vehicle scheduling can be characterized as a combinatorial constraint satisfaction and optimization problem that is NP-complete and for which exhaustive search methods are impractical. To date, heuristic approaches, based on a rank ordering of the relative importance of different types of constraints, have been used for the development of assembly sequences. Although these heuristic approaches have been beneficially applied, they are not easily adapted to incorporate new and more complicated scheduling constraints. We describe a practical and extensible approach for the development of assembly sequences based on methods of evolutionary computation (EC). We demonstrate that the resulting procedure, which we refer to as EC-VS, discovers desirable vehicle assembly sequences for complex problems in an efficient and effective fashion. A production version has been developed and is now deployed at a Ford vehicle assembly plant