Skip to main content
SpringerLink
Log in

Multi-commodity flow dynamic resource assignment and matrix-based job dispatching for multi-relay transfer in complex material handling systems (MHS)

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

Abstract

Mass management and production of customized products requires material handling systems (MHS) which are flexible and responsive enough to accommodate dynamic and real-time changes in material handling tasks. Towards this goal, we develop a novel control framework to improve the flexibility and responsiveness of material handling systems. Flexibility is achieved by using multi-commodity flow network optimization to find the most optimized job sequence in terms of minimum transfer steps. Responsiveness is achieved by the use of a matrix-based discrete event (DE) supervisory controller to dispatch equipment control commands in real-time based on real-time sensor information, according to the optimized sequence. By modeling the MHS network as multi-commodity flow network to define job routes, and using the matrix-based DE controller to implement the job routes in real-time, the users achieve a seamlessly integrated solution to control the execution of transfer jobs that covers the supervisory planning stage through the real-time actual dispatching decisions. The proposed control framework is evaluated on an industrial case study of airfreight terminal material handling and simulation results show its effectiveness.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1993). Network flows: Theory, algorithms, and applications. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Andriansyah, R., de Koning, W., Jordan, R., Etman, L., & Rooda, J. (2011). A process algebra based simulation model of a miniload-workstation order picking system. Computers in Industry, 62(3), 292–300.

    Article  Google Scholar 

  • Babiceanu, R. F., & Chen, F. F. (2005). Distributed and centralised material handling scheduling: Comparison and results of a simulation study. Robotics and Computer-Integrated Manufacturing, 23(2), 441–448.

    Google Scholar 

  • Basile, F., Chiacchio, P., & Coppola, J. (2011). A discrete event model for the control and analysis of complex automated warehouse systems. In: Proceedings of IEEE international conference on automation science and engineering. Italy: Trieste.

  • Bessenouci, H., Sari, Z., & Ghomri, L. (2010). Metaheuristic based control of a flow rack automated storage retrieval system. Journal of Intelligent Manufacturing. doi:10.1007/s10845-010-0432-1.

  • Bogdan, S., Lewis, F., Kovacic, Z., & Mireles, J. (2006). Manufacturing systems control design: A matrix based approach. London: Springer.

    Google Scholar 

  • Bright, G., & Walker, A. (2007). Standardised framework for flexible materials handling management based on operating system primatives. In: Proceedings of the 2007 Australasian conference on Robotics & Automation. Australia: Brisbane.

  • Choe, R., Cho, H., Park, T., & Ryu, K. (2011). Queue-based local scheduling and global coordination for real-time operation control in a container terminal. Journal of Intelligent Manufacturing. doi:10.1007/s10845-011-0564-y.

  • Confessore, G., Fabiano, M., & Liotta, G. (2011). A network flow based heuristic approach for optimising AGV movements. Journal of Intelligent Manufacturing. doi:10.1007/s10845-011-0612-7.

  • Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001). In introduction to algorithms (2nd ed., pp. 788–789). MIT Press and McGraw-Hill.

  • Daoud, S., Chehade, H., Yalaoui, F., & Amodeo, L. (2012). Efficient metaheuristics for pick and place robotic systems optimization. Journal of Intelligent Manufacturing. doi:10.1007/s10845-012-0668-z.

  • De Koster, R., Le-Duc, T., & Roodbergen, K. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182(2), 481–501.

    Article  Google Scholar 

  • Dotoli, M., & Fanti, M. (2005). A coloured Petri net model for automated storage and retrieval systems serviced by rail-guided vehicles: A control perspective. International Journal of Computer Integrated Manufacturing, 18(2–3), 122–136.

    Article  Google Scholar 

  • Even, S., Itai, A., & Shamir, A. (1967). On the complexity of timetable and multicommodity flow problems. SIAM Journal on Computing, 5(4), 691–703.

    Article  Google Scholar 

  • Giordano, V., Zhang, J., Naso, D., & Lewis, F. (2008). Integrated supervisory and operational control of a warehouse with a matrix-based approach. IEEE Transactions on Automation Science and Engineering, 5(1), 53–70.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Gurel, A., Bogdan, S., & Lewis, F. (2000). Matrix approach to deadlock-free dispatching in multi-class finite buffer flowlines. IEEE Transactions on Automatic Control, 45(11), 2086–2090.

    Article  Google Scholar 

  • Harris, B., Cook, D., & Lewis, F. (2002). A matrix formulation for integrating assembly trees and manufacturing resource planning (MRP) with capacity constraints. Journal of Intelligent Manufacturing, 13(4), 239–252.

    Article  Google Scholar 

  • Harris, B., Lewis, F., & Cook, D. (2000). Automatically generating plans for manufacturing. Journal of Intelligent Systems, 10(3), 279–319.

    Article  Google Scholar 

  • Harris, B., Lewis, F., & Cook, D. (1998). Machine planning for manufacturing: Dynamic resource allocation and on-line supervisory control. Journal of Intelligent Manufacturing, 9, 413–430.

    Article  Google Scholar 

  • Heineman, G., Pollice, G., & Stanley, S. (2009). Algorithms in a Nutshell. California, U.S.A: O’Reilly Media.

    Google Scholar 

  • Jung, S., & Kim, K. (2006). Load scheduling for multiple quay cranes in port container terminals. Journal of Intelligent Manufacturing, 17(4), 479–492.

    Article  Google Scholar 

  • Kusiak, A. (1992). Intelligent scheduling of automated machining system. In: Intelligent design and manufacturing (pp. 421–430). New York, U.S.A: Wiley.

  • Le-Anh, T., de Koster, R., & Yu, Y. (2010). Performance evaluation of dynamic scheduling approaches in vehicle-based internal transport systems. International Journal of Production Research, 48(24), 7219–7242.

    Article  Google Scholar 

  • Lin, L., Shinn, S., Gen, M., & Hwang, M. (2006). Network model and effective evolutionary approach for AGV dispatching in manufacturing system. Journal of Intelligent Manufacturing, 17(4), 465–477.

    Article  Google Scholar 

  • Luo, M., Zhang, J., Wong, M., Zhuang, L., Ng, K., & Aung, S. (2005). A heuristic approach to auto-recovery in the supervisory control of heterogeneous automatic equipment for material handling. In: Proceedings of 31st annual conference of IEEE industrial electronics society (IECON 2005), (pp. 2325–2330).

  • McAree, P., Bodin, L., & Ball, M. (2002). Models for the design and analysis of a large package sort facility. Networks, 39(2), 107–120.

    Google Scholar 

  • Medina, L., Bilsel, R., Wysk, R., Prabhu, V., & Ravindran, A. (2009). Simulation for predictive control of a distribution center. In: M. Rossetti, R. Hill, B. Johansson, A. Dunkin, & R. Ingalls (Eds.), Proceedings of the 2009 winter simulation conference.

  • Mireles, J., & Lewis, F. L. (2001). Intelligent material handling: Development and implementation of a matrix-based discrete-event controller. IEEE Transactions on Industrial Electronics, 48(6), 1087–1097.

    Article  Google Scholar 

  • Nishi, T., Ando, M., & Konishi, M. (2006). Experimental studies on a local rescheduling procedure for dynamic routing of autonomous decentralized AGV systems. Robotics and Computer-Integrated Manufacturing, 22(2), 154–165.

    Article  Google Scholar 

  • Ouelhadj, D., & Petrovic, S. (2009). A survey of dynamic scheduling in manufacturing systems. Journal of Scheduling, 12(4), 417–431.

    Article  Google Scholar 

  • Roodbergen, K., & Vis, I. (2009). A survey of literature on automated storage and retrieval systems. European Journal of Operational Research, 194, 343–362.

    Article  Google Scholar 

  • Rubrico, J., Higashi, T., Tamura, H., & Ota, J. (2011). Online rescheduling of multiple picking agents for warehouse management. Robotics and Computer-Integrated Manufacturing, 27(1), 62–71.

    Article  Google Scholar 

  • Steward, D. (1981). The design structure system: A method for managing the design of complex systems. IEEE Transactions on Engineering Management, 28(3), 71–74.

    Article  Google Scholar 

  • Tacconi, D., & Lewis, F. (1997). A new matrix model for discrete event systems: Application to simulation. IEEE Control System Magazine, 17(5), 62–71.

    Article  Google Scholar 

  • van der Meer, J. (2000). Operational Control of Internal Transport. ERIM Ph.D. dissertation. TRAIL Res. School, Amsterdam, The Netherlands, Research in management series, also available online as TRAIL Thesis T2000/5.

  • Wang, C.-N., & Chen, L.-C. (2011). The heuristic preemptive dispatching method of material transportation system in 300 mm semiconductor fabrication. Journal of Intelligent Manufacturing. doi:10.1007/s10845-011-0531-7.

  • Wong, M., Tan, C., Zhang, J., Zhuang, L., Zhao, Y., & Luo, M. (2008). On-line reconfiguration to enhance the routing flexibility of complex automated material handling operations. Robotics and Computer-Integrated Manufacturing, 23, 294–304.

    Google Scholar 

  • Yang, J.-W., Cheng, H.-C., Chiang, T.-C., & Fu, L.-C. (2008). Multiobjective lot scheduling and dynamic OHT routing in a 300-mm wafer fab. IEEE International Conference on Systems, Man and Cybernetics (SMC), (pp. 1608–1613). Singapore.

Download references

Acknowledgments

This work was supported by the National Science Foundation ECS-1128050, the Army Research Office W91NF-05-1-0314 and the Air Force Office of Scientific Research FA9550-09-1-0278.

Author information

Authors and Affiliations

  1. Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore, 638075, Singapore

    Yen Yen Joe & Oon Peen Gan

  2. Automation & Robotics Research Institute, The University of Texas at Arlington, 7300 Jack Newell Blvd. S., Fort Worth, TX, 76118, USA

    Frank L. Lewis

Authors
  1. Yen Yen Joe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oon Peen Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Frank L. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yen Yen Joe.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Joe, Y.Y., Gan, O.P. & Lewis, F.L. Multi-commodity flow dynamic resource assignment and matrix-based job dispatching for multi-relay transfer in complex material handling systems (MHS). J Intell Manuf 25, 681–697 (2014). https://doi.org/10.1007/s10845-012-0713-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-012-0713-y

Keywords

Search

Navigation