The purpose of this paper is to develop an efficient method for solving a vehicle scheduling problem (VSP) in 2D industrial environments. An autonomous vehicle is requested to serve a set of work centers in the shop floor providing transport and delivery tasks while avoiding collisions with obstacles during its travel. The objective is to find a minimum in length, collision‐free vehicle routing schedule that serves timely as many as possible work centers in the shop floor.
First, the vehicle's environment is mapped into a 2D B‐Spline surface embedded in 3D Euclidean space using a robust geometric model. Then, a modified genetic algorithm is applied on the generated surface to search for an optimum legal route schedule that satisfies the requirements of the vehicle's mission.
Simulation experiments show that the method is robust enough and can determine in a reasonable computation time a solution to VSP under consideration.
There is a gap in the literature for methods that face VSP in shop‐floor environments. This paper contributes to filling this gap by implementing a practical method that can be easily programmed and included in a modern service delivery system.
