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Mathematical Problems in Engineering
Volume 2015, Article ID 597956, 10 pages
Research Article

Genetic Scheduling and Reinforcement Learning in Multirobot Systems for Intelligent Warehouses

1Department of Control and Systems Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China
2Research Center for Novel Technology of Intelligent Equipments, Nanjing University, Nanjing 210093, China

Received 8 August 2015; Accepted 15 December 2015

Academic Editor: Luciano Mescia

Copyright © 2015 Jiajia Dou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A new hybrid solution is presented to improve the efficiency of intelligent warehouses with multirobot systems, where the genetic algorithm (GA) based task scheduling is combined with reinforcement learning (RL) based path planning for mobile robots. Reinforcement learning is an effective approach to search for a collision-free path in unknown dynamic environments. Genetic algorithm is a simple but splendid evolutionary search method that provides very good solutions for task allocation. In order to achieve higher efficiency of the intelligent warehouse system, we design a new solution by combining these two techniques and provide an effective and alternative way compared with other state-of-the-art methods. Simulation results demonstrate the effectiveness of the proposed approach regarding the optimization of travel time and overall efficiency of the intelligent warehouse system.