Application of reinforcement learning methods to allocate logistics resources to production halls in an automotive industry

Abstract Efficiently managing internal logistics in the contemporary automobile industry is paramount. This paper delves into the simulation of an internal logistics (IL) system within an automotive factory, employing reinforcement learning. By capturing the unique IL characteristics of the factory, this paper formulates a comprehensive simulation model characterized by its incorporation of sparse reward mechanisms. This paper uses two distinct algorithms. The first algorithm is the multi-agent deep deterministic policy gradient, enhanced by integrating the Baseline to accommodate discrete actions. The second algorithm, shared experience deep Q-network, leverages the prioritized replay strategy to amplify its effectiveness in managing sparse rewards. This paper conducts rigorous numerical experiments to validate both the model's accuracy and the algorithms' efficacy.