Vehicle scheduling in single-loop automated guided vehicle system via mixed-integer programming

Abstract A single-loop automated guided vehicle (AGV) system, in which multiple vehicles run on a single closed loop, is widely used in smart factories for material transport. The advantages of the system include its cost-effectiveness, ease of installation, and expansion capabilities. However, scheduling vehicles in a single-loop system is challenging because of potential interference among vehicles. For example, when a vehicle is picking up or dropping off loads at a station, the following vehicles must wait until the station becomes free. This paper presents a novel mixed-integer programming (MIP) approach to optimize vehicle scheduling while considering such interference. Building upon the core concept of modeling cycles in a looped path as consecutive linear paths, we develop a MIP formulation that incorporates both an objective function minimizing the total completion time for a set of transportation jobs and the constraints addressing interference among vehicles. We validate our approach through experiments using real-world data from a smart factory of a major pharmaceutical company. The results demonstrate that our proposed method reduces the completion time for a set of jobs by 10% compared to a baseline method based on a nearest neighbor heuristic. This research contributes to the productivity and efficiency of smart factories by providing an effective solution for vehicle scheduling in single-loop AGV systems.