Robotic Agents through Scalable Multi-agent Reinforcement Learning for Optimization of Warehouse Logistics

Optimizing warehouse logistics is a daunting challenge, especially in today's ever-changing industrial environment. As warehouses become complex labyrinths with robots dedicated to various tasks, heuristic methods become indispensable. Based on experience and intuition, these methods offer shortcuts to solving complex challenges, enabling rapid decisions without resorting to exhaustive research. The recent research, based on pre-established rules and previous experience, has found that heuristic methods are difficult to adapt quickly to frequent changes in logistical fields. The heuristic methods are weak when faced with new situations requiring frequent change. These constraints allow us to migrate to reinforcement learning, which introduces a dynamic and continuous path in logistics environments, unlike static heuristic methods. In this environment, this paper aims to provide agents with intelligent and structured strategies to manage navigation efficiently in such dynamic logistic environments, and to meet the challenges of modern warehouses and their respective targets in real time. To achieve this, we have hybridized the BAT algorithm of Meta heuristics and reinforcement learning algorithms, which will yield remarkable results.

• Publication year

  2025

• Venue

  WSEAS transactions on systems and control

• Publication date

  2025-03-26

• Fields of study

  Not labeled

• Identifiers
  DOI 10.37394/23203.2025.20.5
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Learning based short term wind speed forecasting models for smart grid applications: An extensive review and case study

  2023cited by this paper
• Human-robot kinematics mapping method based on dynamic equivalent points

  2022cited by this paper
• Multi-agent system optimisation in factories of the future: cyber collaborative warehouse study

  2021cited by this paper
• Autonomous navigation of stratospheric balloons using reinforcement learning

  2020cited by this paper
• Deep Q learning-based mitigation of man in the middle attack over secure sockets layer websites

  2020cited by this paper
• Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications

  2018cited by this paper
• Sockets for Limb Prostheses: A Review of Existing Technologies and Open Challenges

  2018cited by this paper
• The STIC analysis: A decision support tool for technology related investments in logistics

  2017cited by this paper
• Bat algorithm: literature review and applications

  2013cited by this paper
• Design and control of warehouse order picking: A literature review

  2006cited by this paper
• A selection-mutation model for q-learning in multi-agent systems

  2003cited by this paper
• An Evaluation of Routing and Volume‐based Storage Policies in an Order Picking Operation

  1999cited by this paper

• No citing papers are available for this paper.