Energy Optimization of Large-Scale AGV Systems

We propose an efficient optimization method, which addresses several performance criteria, such as makespan, maximum lateness, and the sum of tardiness for an automated guided vehicle (AGV) system, together with its energy consumption. We show that the most important factors in energy consumption of AGVs are their cruise velocities and traveled distances. We also demonstrate that optimizing the productivity-related performance criteria also reduces energy consumption through less traveled distance. It also allows for the reduction of the cruise velocity, which leads to more energy savings. Our experiments demonstrate that the optimization method outperforms the existing traffic controller with respect to the performance criteria and reduces energy consumption. The proposed method can reduce the energy consumption by around 38%, while the values of makespan, lateness, and tardiness remain better than those obtained from the existing traffic controller. An important advantage of this article is that the evaluations are based on collected data from a real large-scale manufacturing plant. Note to Practitioners-It is commonly believed that reduction of speed, for example, due to safety reasons in critical areas, in automated guided vehicle (AGV) systems leads to lower system efficiency. However, it has been shown that speed management is an effective strategy to reduce the energy consumption of mobile robots and robot stations. If one seeks to utilize the existing slacks in the schedule of the AGVs, it should be possible to reduce energy consumption without affecting system efficiency. It can, furthermore, be combined with better scheduling to even improve performance measures, such as makespan, while reducing energy. In this article, we propose an optimization method that seeks to minimize the number of performance measures, such as makespan, maximum lateness, and sum of tardiness for a real AGV system designed by AGVE, which operates at Volvo Cars, Gothenburg, Sweden. We also show that the optimization method allows for reduction of cruise speed, while the mentioned performance measures are still better than the one obtained from the original traffic controller. We will also show the importance of taking into account the temperature of the drive system of the AGVs when performing energy measurements.