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B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
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Updated: Jun 18, 2025

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

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Optimizing Robotic Mobile Fulfillment Systems for Order Picking Based on Deep Reinforcement Learning.

Zhenyi Zhu¹, Sai Wang^2,3, Tuantuan Wang³

¹School of Science, Wuhan University of Technology, Wuhan 430070, China.

Sensors (Basel, Switzerland)

|July 27, 2024

Summary

Deep Reinforcement Learning (DRL) enhances robotic mobile fulfillment systems (RMFSs) for large-scale orders. This advanced approach improves efficiency and stability in complex warehouse environments.

Keywords:

automatic warehousing system deep reinforcement learning order allocation and sequencing robot collaborative scheduling robotic mobile fulfillment systems shelf selection supply chain management

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Area of Science:

Robotics
Artificial Intelligence
Operations Research

Background:

Robotic Mobile Fulfillment Systems (RMFSs) face significant challenges in managing large-scale orders and complex operational environments.
Key decision-making problems include order allocation, shelf selection, and robot scheduling, impacting overall system efficiency and stability.

Purpose of the Study:

To integrate Deep Reinforcement Learning (DRL) into RMFSs to address challenges in efficient order processing and system stability.
To develop and evaluate DRL-based solutions for critical RMFS stages: order allocation/sorting, shelf selection, and robot scheduling.

Main Methods:

Mathematical models were established for each of the three key RMFS stages.
Deep Reinforcement Learning (DRL) was employed to solve complex decision-making problems, augmented by Genetic Algorithms and Ant Colony Optimization for large-scale order handling.
Simulation experiments were conducted to evaluate performance indicators like shelf access frequency and total processing time.

Main Results:

The proposed DRL-integrated algorithms demonstrated superior performance in handling large-scale orders compared to traditional methods.
The system achieved a high throughput, capable of completing approximately 110 tasks per hour.
Evaluated performance indicators showed significant improvements in efficiency and system stability.

Conclusions:

DRL integration offers a powerful solution for enhancing RMFS performance, particularly for large-scale order fulfillment.
The developed algorithms provide a superior alternative to traditional methods for complex decision-making in robotic fulfillment.
Future work should focus on integrated decision-making models and efficient heuristic algorithms for further optimization.