Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management

Cestero, Julen; Quartulli, Marco; Metelli, Alberto Maria; Restelli, Marcello

doi:10.1109/ijcnn55064.2022.9891985

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Extensive simulation experiments demonstrated the effectiveness and efficiency of the proposed approach compared with state-of-the-art methods. Similarly, Cestero et al [41] presented Storehouse, a customizable environment for warehouse simulations designed to optimize warehouse management using RL techniques. The environment was validated against state-of-the-art RL algorithms and compared with human and random policies.…”

Section: Advancements In Rl With Simulation For Warehouse Operationsmentioning

confidence: 99%

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A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

Leon,

Li,

Martin

et al. 2023

Algorithms

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The use of simulation and reinforcement learning can be viewed as a flexible approach to aid managerial decision-making, particularly in the face of growing complexity in manufacturing and logistic systems. Efficient supply chains heavily rely on steamlined warehouse operations, and therefore, having a well-informed storage location assignment policy is crucial for their improvement. The traditional methods found in the literature for tackling the storage location assignment problem have certain drawbacks, including the omission of stochastic process variability or the neglect of interaction between various warehouse workers. In this context, we explore the possibilities of combining simulation with reinforcement learning to develop effective mechanisms that allow for the quick acquisition of information about a complex environment, the processing of that information, and then the decision-making about the best storage location assignment. In order to test these concepts, we will make use of the FlexSim commercial simulator.

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Section: Advancements In Rl With Simulation For Warehouse Operationsmentioning

confidence: 99%

“…Ren and Huang [35] Balachandran et al [36] Gazebo Arslan and Ekren [37] Lewis et al [38] NVIDIA Isaac Sim Ho et al [39] Zhou et al [40] Cestero et al [41] Choi et al [42] Elkunchwar et al [43] Wang et al [44] Y. Ekren and Arslan [45] Arena…”

Section: Advancements In Rl With Simulation For Warehouse Operationsmentioning

confidence: 99%

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

Leon,

Li,

Martin

et al. 2023

Algorithms

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Solving the Storage Location Assignment Problem Using Reinforcement Learning

Troch,

Mannens,

Mercelis

2023

Proceedings of the 2023 8th International Conference on Mathematics and Artificial Intelligence

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UAV-Based Warehouse Management Using Multi-Agent RL

Esmaili,

Roayaei

2024

Applications of Machine Learning in UAV Networks

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This chapter aims to investigate the applications of reinforcement learning (RL) and Multi-Agent RL (MARL) in UAV networks in warehouse management. Different applications of UAVs in warehousing and different applications of RL in UAV networks are reviewed. Currently, most research in this area relies on single-agent RL approaches. Transitioning from single-agent RL to MARL offers the opportunity can potentially achieve higher levels of optimization, scalability, and adaptability in warehouse management using UAV networks. However, the application of multi-agent approaches in UAV-based warehouse management is still in its early stages, and may introduce new challenges. Thus, this chapter specifically focuses on the challenges and solutions associated with adopting MARL in the context of UAV-based warehouse management tasks. This includes addressing challenges such as non-stationary, partial observability, credit assignment, scalability, and task allocation. The authors highlight challenges and present some of the widely-used approaches to address these challenges.

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Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management

Cited by 3 publications

References 16 publications

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

Solving the Storage Location Assignment Problem Using Reinforcement Learning

UAV-Based Warehouse Management Using Multi-Agent RL

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