A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand

  • Zhi Li
  • Ali Vatankhah Barenji
  • Jiazhi Jiang
  • Ray Y. ZhongEmail author
  • Gangyan Xu


Given the evolutionary journey of E-commerce, there have been emerging challenges confronting warehouse logistics, including smaller shipping units, more varieties and batches, and shorter cycles. These challenges are difficult to cope when using conventional scheduling with the robotic approach. Currently, automated storage and retrieval system are becoming preferred for warehouse companies with the help of mobile robots. However, when many orders are received simultaneously, the existing scheduling approach might make unreasonable decisions, leading to delayed packaging of entire orders and reducing the performance of the warehouse. Therefore, this paper addresses this problem and proposes a novel scheduling mechanism for multi-robot and tasks allocation problems which may arise in an intelligent warehouse system. This mechanism proposes into the intelligent warehouse troubled with simultaneous multiple customer demands. The mathematical model for the system is developed by considering a multitask robot facing dynamic customer demand. The proposed model’s approach is based on the particle swarm optimization heuristic. The result for this approach then compared with the genetic algorithm (GA). The simulation results demonstrate that the proposed solution is far superior to that of the GA for multi-robot scheduling and tasks allocation problems in the intelligent warehouse.


Intelligent warehousing system Multi-robot Scheduling Synchronized 

List of Symbols


Set of mobile robots on the IWs


Grid area at two dimensions


Barrier raster set


Arbitrary raster set

Te(xe, ye)

Set of target point

Ss(xe, ye)

Starting point for mobile robot


Set of order need to process over a certain period


Set of tasks need to be carried by robots for specific order


Represented transportation of robot ri


Transportation time of the robot ri runs from its parking point to the shelf storage point


Walking costs for each robot

\( ITC\{ r_{i} ,T_{k} \} \)

Total cost for the robot \( r_{i} \) to complete the assigned task


Longest time taken for a robot to complete its task


Total completion time for the order


Velocity vector (PSO)


Position vector (PSO)


Best location which can be found by the current group and which can be a globally optimal solution

\( \xi \) and δ

Number between 0 and 1


Self-confidence factor


Swarm confidence factor


Fitness function


Population size


Number of iterations


Inertia weight

\( {\text{P}}^{\text{g}}_{\text{k}} \)

Position of the particle with best global fitness at current move k


Crossover probability


Mutation probability



This work was supported by the National Natural Science Foundation of China (51405089), the Science and Technology Planning Project of Guangdong Province (2015B010131008) and China Postdoctoral Science Foundation under (2018M633008).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing Systems, School of Electromechanical EngineeringGuangdong University of TechnologyGuangzhouChina
  2. 2.Department of Mechatronics EngineeringKennesaw State UniversityKennesawUSA
  3. 3.Department of Industrial and Manufacturing Systems EngineeringThe University of Hong KongPok Fu LamHong Kong
  4. 4.School of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingaporeSingapore

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