Scheduling mobile robots in dynamic production environments considering battery management constraints

Purpose The dynamic nature of production environments with uncertainty about future tasks places a high demand on the flexibility for assigning tasks to a fleet of Automated Guided Vehicles. The goal of our research is to reduce the number of required vehicles compared to the traditional approach while achieving the same throughput for economic and sustainability reasons by enabling a more intelligent battery management. Design/methodology/approach Our proposed approach considers charging as an option during robot task assignment. We extend a previously presented sequential single-item auction algorithm for online scheduling using utility functions to compare the options for performing tasks or charging. This enables a more intelligent decision, depending on the current system state, focusing on improved availability in phases with high demand and efficiency. A material flow simulation highlights potential issues when disregarding battery management and validates the proposed methods. Findings Despite the advancements in modern battery technologies, battery management remains an important aspect for continuous operation without breaks for charging in between shifts. Additionally, frequently driving to charging stations for opportunity charging can generate significant additional movement and power consumption. It is demonstrated that our approach improves system performance while reducing the total number of charging operations, which allows a reduction in required vehicle count. Originality/value Our approach represents a deeper integration of battery management aspects into the robot-task assignment problem. The proposed online scheduler is very flexible, as any number of system states can be considered in the form of additional utilities, enabling potential for further optimization.