Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

R:

Set of mobile robots on the IWs

Nc :

Grid area at two dimensions

Oobs :

Barrier raster set

g:

Arbitrary raster set

Te(xe, ye):

Set of target point

Ss(xe, ye):

Starting point for mobile robot

D:

Set of order need to process over a certain period

T:

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

C:

Represented transportation of robot ri

Tti1.Ctijti(j−1) :

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

W(ri):

Walking costs for each robot

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

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

F1 :

Longest time taken for a robot to complete its task

F2 :

Total completion time for the order

Vi :

Velocity vector (PSO)

Xi :

Position vector (PSO)

Pg :

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

C1 :

Self-confidence factor

C2 :

Swarm confidence factor

f:

Fitness function

xsize:

Population size

maxgen:

Number of iterations

w:

Inertia weight

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

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

pc:

Crossover probability

pm:

Mutation probability

References

  • Alavidoost, M., Tarimoradi, M., & Zarandi, M. F. (2015). Bi-objective mixed-integer nonlinear programming for multi-commodity tri-echelon supply chain networks. Journal of Intelligent Manufacturing,29, 1–18.

    Google Scholar 

  • Bai, Q. (2010). Analysis of particle swarm optimization algorithm. Computer and Information Science,3(1), 180.

    Article  Google Scholar 

  • Baker, P., & Canessa, M. (2009). Warehouse design: A structured approach. European Journal of Operational Research,193(2), 425–436.

    Article  Google Scholar 

  • Barenji, A. V., Barenji, R. V., Roudi, D., & Hashemipour, M. (2017a). A dynamic multi-agent-based scheduling approach for SMEs. The International Journal of Advanced Manufacturing Technology,89(9–12), 3123–3137.

    Article  Google Scholar 

  • Barenji, R. V., Barenji, A. V., & Hashemipour, M. (2014). A multi-agent RFID-enabled distributed control system for a flexible manufacturing shop. The International Journal of Advanced Manufacturing Technology,71(9–12), 1773–1791.

    Article  Google Scholar 

  • Barenji, R. V., Ozkaya, B. Y., & Barenji, A. V. (2017b). Quantifying the advantage of a kitting system using Petri nets: A case study in Turkey, modeling, analysis, and insights. The International Journal of Advanced Manufacturing Technology,93(9–12), 3677–3691.

    Article  Google Scholar 

  • Chen, F., Wang, H., Xie, Y., & Qi, C. (2016). An ACO-based online routing method for multiple order pickers with congestion consideration in warehouse. Journal of Intelligent Manufacturing,27(2), 389–408.

    Article  Google Scholar 

  • Contreras-Cruz, M. A., Ayala-Ramirez, V., & Hernandez-Belmonte, U. H. (2015). Mobile robot path planning using artificial bee colony and evolutionary programming. Applied Soft Computing,30, 319–328.

    Article  Google Scholar 

  • Costa, A., Cappadonna, F. A., & Fichera, S. (2017). A hybrid genetic algorithm for minimizing makespan in a flow-shop sequence-dependent group scheduling problem. Journal of Intelligent Manufacturing,28(6), 1269–1283.

    Article  Google Scholar 

  • Dias, M. B., Zlot, R., Kalra, N., & Stentz, A. (2006). Market-based multirobot coordination: A survey and analysis. Proceedings of the IEEE,94(7), 1257–1270.

    Article  Google Scholar 

  • Dou, J., Chen, C., & Yang, P. (2015). Genetic scheduling and reinforcement learning in multirobot systems for intelligent warehouses. Mathematical Problems in Engineering, 2015, 1–10.

    Google Scholar 

  • Elango, M., Nachiappan, S., & Tiwari, M. K. (2011). Balancing task allocation in multi-robot systems using K-means clustering and auction based mechanisms. Expert Systems with Applications,38(6), 6486–6491.

    Article  Google Scholar 

  • Foumani, M., Moeini, A., Haythorpe, M., & Smith-Miles, K. (2018). A cross-entropy method for optimising robotic automated storage and retrieval systems. International Journal of Production Research,56, 1–23.

    Article  Google Scholar 

  • Gautam, A., Thakur, A., Dhanania, G., & Mohan, S. (2016). A distributed algorithm for balanced multi-robot task allocation. 2016 11th International conference on paper presented at the industrial and information systems (ICIIS).

  • Gu, J. (2005). The forward reserve warehouse sizing and dimensioning problem. Atlanta: Georgia Institute of Technology.

    Google Scholar 

  • Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research,177(1), 1–21.

    Article  Google Scholar 

  • Gu, J., Goetschalckx, M., & McGinnis, L. F. (2010). Research on warehouse design and performance evaluation: A comprehensive review. European Journal of Operational Research,203(3), 539–549.

    Article  Google Scholar 

  • Hariga, M. A., & Jackson, P. L. (1996). The warehouse scheduling problem: Formulation and algorithms. IIE Transactions,28(2), 115–127.

    Article  Google Scholar 

  • Hassan, M. M. D. (2002). A framework for the design of warehouse layout. Facilities,20(13/14), 432–440.

    Article  Google Scholar 

  • Hassan, R., Cohanim, B., De Weck, O., & Venter, G. (2005). A comparison of particle swarm optimization and the genetic algorithm. Paper presented at the 46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference.

  • Heragu, S. S., Du, L., Mantel, R. J., & Schuur, P. C. (2005). Mathematical model for warehouse design and product allocation. International Journal of Production Research,43(2), 327–338.

    Article  Google Scholar 

  • Huh, J., Chae, M.-J., Park, J., & Kim, K. (2017). A case-based reasoning approach to fast optimization of travel routes for large-scale AS/RSs. Journal of Intelligent Manufacturing, 28, 1–14.

    Article  Google Scholar 

  • Kennedy, J. (2011). Particle swarm optimization. In C. Sammut, & G. I. Webb (Eds.), Encyclopedia of machine learning (pp. 760–766). Berlin: Springer.

    Google Scholar 

  • Kulkarni, R. V., & Venayagamoorthy, G. K. (2011). Particle swarm optimization in wireless-sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews),41(2), 262–267.

    Article  Google Scholar 

  • Liu, M., Ma, J., Lin, L., Ge, M., Wang, Q., & Liu, C. (2017). Intelligent assembly system for mechanical products and key technology based on internet of things. Journal of Intelligent Manufacturing,28(2), 271–299.

    Article  Google Scholar 

  • Minner, S., & Transchel, S. (2010). Periodic review inventory-control for perishable products under service-level constraints. OR Spectrum,32(4), 979–996.

    Article  Google Scholar 

  • Mohemmed, A. W., Sahoo, N. C., & Geok, T. K. (2008). Solving shortest path problem using particle swarm optimization. Applied Soft Computing,8(4), 1643–1653.

    Article  Google Scholar 

  • Nastasi, G., Colla, V., Cateni, S., & Campigli, S. (2016). Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse. Journal of Intelligent Manufacturing, 27, 1–13.

    Article  Google Scholar 

  • Nedic, N., Stojanovic, V., & Djordjevic, V. (2015). Optimal control of hydraulically driven parallel robot platform based on firefly algorithm. Nonlinear Dynamics,82(3), 1457–1473.

    Article  Google Scholar 

  • Onwubolu, G. C., & Mutingi, M. (2003). A genetic algorithm approach for the cutting stock problem. Journal of Intelligent Manufacturing,14(2), 209–218.

    Article  Google Scholar 

  • Poli, R. (2008). Analysis of the publications on the applications of particle swarm optimisation. Journal of Artificial Evolution and Applications. https://doi.org/10.1155/2008/685175.

    Article  Google Scholar 

  • Ponnambalam, S., Ramkumar, V., & Jawahar, N. (2001). A multiobjective genetic algorithm for job shop scheduling. Production Planning & Control,12(8), 764–774.

    Article  Google Scholar 

  • Pršić, D., Nedić, N., & Stojanović, V. (2017). A nature inspired optimal control of pneumatic-driven parallel robot platform. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science,231(1), 59–71.

    Google Scholar 

  • Rouwenhorst, B., Reuter, B., Stockrahm, V., van Houtum, G.-J., Mantel, R., & Zijm, W. H. (2000). Warehouse design and control: Framework and literature review. European Journal of Operational Research,122(3), 515–533.

    Article  Google Scholar 

  • Stojanovic, V., Nedic, N., Prsic, D., Dubonjic, L., & Djordjevic, V. (2016). Application of cuckoo search algorithm to constrained control problem of a parallel robot platform. The International Journal of Advanced Manufacturing Technology,87(9–12), 2497–2507.

    Article  Google Scholar 

  • Venter, G., & Sobieszczanski-Sobieski, J. (2003). Particle swarm optimization. AIAA Journal,41(8), 1583–1589.

    Article  Google Scholar 

  • Yalcin, A., Koberstein, A., & Schocke, K.-O. (2018). An optimal and a heuristic algorithm for the single-item retrieval problem in puzzle-based storage systems with multiple escorts. International Journal of Production Research, 29, 1–23.

    Google Scholar 

  • Yan, B., Yan, C., Long, F., & Tan, X.-C. (2015). Multi-objective optimization of electronic product goods location assignment in stereoscopic warehouse based on adaptive genetic algorithm. Journal of Intelligent Manufacturing, 26, 1–13.

    Article  Google Scholar 

  • Ye, S., Ma, H., Xu, S., Yang, W., & Fei, M. (2017). An effective fireworks algorithm for warehouse-scheduling problem. Transactions of the Institute of Measurement and Control,39(1), 75–85.

    Article  Google Scholar 

  • Zhang, Y., Gong, D.-W., & Zhang, J.-H. (2013). Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing,103, 172–185.

    Article  Google Scholar 

  • Zhong, R. Y., Huang, G. Q., Dai, Q. Y., & Zhang, T. (2014). Mining SOTs and dispatching rules from RFID-enabled real-time shopfloor production data. Journal of Intelligent Manufacturing,25(4), 825–843.

    Article  Google Scholar 

  • Zhou, L., Shi, Y., Wang, J., & Yang, P. (2014). A balanced heuristic mechanism for multirobot task allocation of intelligent warehouses. Mathematical Problems in Engineering, 2014, 15–25.

    Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing Systems, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong, China

    Zhi Li, Ali Vatankhah Barenji & Jiazhi Jiang

  2. Department of Mechatronics Engineering, Kennesaw State University, Kennesaw, USA

    Ali Vatankhah Barenji

  3. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pok Fu Lam, Hong Kong

    Ray Y. Zhong

  4. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Gangyan Xu

Authors
  1. Zhi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Vatankhah Barenji
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiazhi Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ray Y. Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gangyan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ray Y. Zhong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Z., Barenji, A.V., Jiang, J. et al. A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand. J Intell Manuf 31, 469–480 (2020). https://doi.org/10.1007/s10845-018-1459-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-018-1459-y

Keywords

Search

Navigation