Fleet Sizing of Dynamically Routed In-plant Milk-run Vehicles Based on a Genetic Algorithm

Abstract

In-plant milk-run (MR) systems enable efficient supply of assembly lines in small lot sizes. One major chal-lenge for MR systems are demand fluctuations and short-term changes within schedules. Dynamic control strategies aim at increasing flexibility and efficiency of MR systems in volatile environments. This paper presents an application-oriented approach for determining the fleet size of an MR system with dynamically controlled routes based on a genetic algorithm. The approach is evaluated and discussed using a case study from a commercial vehicle manufacturer. The results show that the approach ena-bles effective analytical dimensioning of MR systems with dynamic routes. In addition, the case study indicates that the implementation of dynamic routes can lead to a reduction in fleet size.