Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management

The essentials

• Warehouse management systems have been evolving with new data intelligence techniques
• Current optimizations often require manual interaction or are specific to certain cases
• Reinforcement learning (RL) techniques can provide automation and adaptability to current optimization policies
• The authors investigate RL approaches for warehouse storage management and present Storehouse, a customizable environment that generalizes the definition of warehouse simulations for RL
• Storehouse is implemented using the gym interface and includes a customization method that allows developers to try out different versions of the environment with different grid sizes, material types, and timers
• The authors formulate warehouse storage management as a Markov Decision Process (MDP) which can be customized with different warehouse topologies and generation processes
• They provide an experimental evaluation of several RL algorithms including DQN, PPO, A2C against handcrafted human policies showing competitive performance.
• The code for the environment is available on Github.
• Current methodologies for optimizing warehouse management systems can be grouped into two trends: classic trend and modern trend.
• Classic trend leverages advancements in sensors and automation to enhance existing ad hoc methods using real data instead of human experience.
• Several optimization methods have been proposed such as wave-picking warehouse management, scheduling functions aimed at interconnecting several agents in an industrial management process involving multiple warehouses, fleet vehicles, manufacturers, and consumers.
• The second trend aims to propose methods breaking with existing ones by addressing limitations such as item placement or picking constraints. However, these methodologies have limitations as they are aimed at solving specific problems of certain warehouses without providing a generalized solution applicable across all warehouses.
• In contrast, Storehouse provides a customizable environment that allows developers to train RL agents to manage the storage of any warehouse using FIFO methodology while also being adaptable to different scenarios through its configuration file.
• Overall, this paper presents an innovative approach towards optimizing warehouse storage management through reinforcement learning techniques while providing a customizable environment that can be adapted to different warehouse scenarios.

Key points written by AI: they may contain errors, check them against the paper.