Mulation Analysis of a Reinforcement-Learning-Based Warehouse Dispatching Method Considering due Date and Travel Distance

Abstract

As the adoption of autonomous mobile robots in warehouses and other industrial environments continues to increase, there is a need for methods that can effectively dispatch robots to meet system demand. Real-time dispatching of autonomous mobile robots can be very complex, but simple rule-based methods are typically used for this task. In this paper, a reinforcement-learning-based dispatching method for intralogistics (RLDI) is proposed. RLDI is warehouse layout independent and takes into consideration task due dates and the travel distance. The algorithm is trained and tested in a simulation environment that represents a small warehouse. Monte Carlo simulation analysis is used to explore the capabilities and limitations of the established RLDI. The performance of the method is compared to the shortest distance dispatching rule in single and multi-agent environments under various levels of due date tightness. Experimental results demonstrate the potential for using reinforcement learning methods for warehouse dispatching.