Advertisement

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
Article
  • 158 Downloads

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.

Keywords

Intelligent warehousing system Multi-robot Scheduling Synchronized 

List of Symbols

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

Notes

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).

References

  1. 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
  2. Bai, Q. (2010). Analysis of particle swarm optimization algorithm. Computer and Information Science, 3(1), 180.CrossRefGoogle Scholar
  3. Baker, P., & Canessa, M. (2009). Warehouse design: A structured approach. European Journal of Operational Research, 193(2), 425–436.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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
  12. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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).Google Scholar
  15. Gu, J. (2005). The forward reserve warehouse sizing and dimensioning problem. Atlanta: Georgia Institute of Technology.Google Scholar
  16. Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1–21.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. Hariga, M. A., & Jackson, P. L. (1996). The warehouse scheduling problem: Formulation and algorithms. IIE Transactions, 28(2), 115–127.CrossRefGoogle Scholar
  19. Hassan, M. M. D. (2002). A framework for the design of warehouse layout. Facilities, 20(13/14), 432–440.CrossRefGoogle Scholar
  20. 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.Google Scholar
  21. 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.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. Kennedy, J. (2011). Particle swarm optimization. In C. Sammut, & G. I. Webb (Eds.), Encyclopedia of machine learning (pp. 760–766). Berlin: Springer.Google Scholar
  24. 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.CrossRefGoogle Scholar
  25. 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.CrossRefGoogle Scholar
  26. Minner, S., & Transchel, S. (2010). Periodic review inventory-control for perishable products under service-level constraints. OR Spectrum, 32(4), 979–996.CrossRefGoogle Scholar
  27. 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.CrossRefGoogle Scholar
  28. 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.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. Onwubolu, G. C., & Mutingi, M. (2003). A genetic algorithm approach for the cutting stock problem. Journal of Intelligent Manufacturing, 14(2), 209–218.CrossRefGoogle Scholar
  31. 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.CrossRefGoogle Scholar
  32. Ponnambalam, S., Ramkumar, V., & Jawahar, N. (2001). A multiobjective genetic algorithm for job shop scheduling. Production Planning & Control, 12(8), 764–774.CrossRefGoogle Scholar
  33. 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
  34. 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.CrossRefGoogle Scholar
  35. 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.CrossRefGoogle Scholar
  36. Venter, G., & Sobieszczanski-Sobieski, J. (2003). Particle swarm optimization. AIAA Journal, 41(8), 1583–1589.CrossRefGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. 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.CrossRefGoogle Scholar
  39. 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.CrossRefGoogle Scholar
  40. 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.CrossRefGoogle Scholar
  41. 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.CrossRefGoogle Scholar
  42. 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

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

Personalised recommendations