Pilot Prototype of Autonomous Pallets and Employing Little’s Law for Routing

  • Afshin Mehrsai
  • Hamid-Reza Karimi
  • Klaus-Dieter Thoben
  • Bernd Scholz-Reiter
Conference paper
Part of the Lecture Notes in Logistics book series (LNLO)


Application of autonomous control for shop-floor scheduling by considering real-time control of material flows is advantageous to those assembly lines with dynamic and uncertain circumstances. Among several potential processors with computing and communication capabilities—for representing autonomous material carriers—wireless sensor nodes seem as promising objects to be applied in practice. For realizing autonomy in making scheduling and routing-control decisions some methodologies need to be embedded in the nodes. Among several experimented methodologies, e.g., artificial intelligence, genetic algorithm, etc., in the context of a doctoral research, in this current special case of assembly scenario, the queuing theory and its simple equations seem quite suitable. For instance, employment of Little’s law for calculating and analysis of simple queuing structures is a favorable method for autonomous pallets in real shop-floors. Concerning the simplicity and inexpensive computing loads of such a rule, it suits the best to the low capacity wireless sensors in developing pilot prototypes of autonomous carriers. Little’s law can be used to estimate the current waiting times of alternative stations and try to find a non-decreasing order of operations to improve the performance record (e.g., makespan) of the entire assembly system. To develop a pilot prototype, some wireless sensors—representing pallets in practice—are connected to a simulated assembly scenario via the TCP/IP protocol to evaluate the feasibility of realizing autonomous pallets in the practice of shop-floor control. Nevertheless, wireless nodes are distributed objects, so the use of data sharing for transferring low data between each other and respectively low energy consumption is necessary.


Autonomous pallets Shop-floor real-time scheduling and control Prototype with simulation Queuing theory Wireless sensor nodes 


  1. Bilgen B, Günther HO (2010) Integrated production and distribution planning in the fast moving consumer goods industry: a block planning application. OR Spectr 32(4):927–955MathSciNetCrossRefzbMATHGoogle Scholar
  2. Cooper RB (1981) Introduction to queueing theory, 2nd edn. North Holland, New YorkzbMATHGoogle Scholar
  3. Dombacher C (2009) Stationary queueing models with aspects of customer impatience and retrial behaviour. Telecom 131. Accessed 01 Nov 2013
  4. Farahani S (2008) ZigBee wireless networks and transceivers. Elsevier, AmsterdamGoogle Scholar
  5. Gross D, Shortle JF, Thompson JM, Harris CM (2008) Fundamentals of queueing theory. Wiley, New YorkCrossRefzbMATHGoogle Scholar
  6. Mehrsai A, Scholz-Reiter B (2011) Towards learning pallets applied in pull control job-open shop problem. In: IEEE international symposium on assembly and manufacturing (ISAM), pp 1–6Google Scholar
  7. Mehrsai A, Karimi HR, Thoben K-D, Scholz-Reiter B (2013) Application of learning pallets for real-time scheduling by the use of radial basis function network. Neurocomputing 101:82–93CrossRefGoogle Scholar
  8. Ouazene Y, Chehade H, Yalaoui A, Yalaoui F (2013) Equivalent machine method for approximate evaluation of buffered unreliable production lines. In: IEEE workshop on computational intelligence in production and logistics systems (CIPLS), pp 33–39Google Scholar
  9. Polastre J, Szewczyk R, Culler D (2005) Telos: enabling ultra-low power wireless research. In: Fourth international symposium on information processing in sensor networks, pp 364–369Google Scholar
  10. Ravindran A (2008) Operations research and management science handbook. CRCGoogle Scholar
  11. Ruiz-Garcia L, Lunadei L, Barreiro P, Robla I (2009) A review of wireless sensor technologies and applications in agriculture and food industry: state of the art and current trends. Sensors 9(6):4728–4750CrossRefGoogle Scholar
  12. Son VQ (2011) Modeling and implementation of wireless sensor networks for logistics applications. Dissertation, University of BremenGoogle Scholar
  13. Son VQ, Wenning B, Görg C, Timm-Giel A (2010) WiSeCoMaSys: a tool for data collection and management of wireless sensor networks. In: International conference on advanced technologies for communications (ATC), pp 33–38Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Afshin Mehrsai
    • 1
  • Hamid-Reza Karimi
    • 2
  • Klaus-Dieter Thoben
    • 1
  • Bernd Scholz-Reiter
    • 1
  1. 1.Department of Planning and Control of Production SystemsUniversity of BremenBremenGermany
  2. 2.Department of Engineering, Faculty of Engineering and ScienceUniversity of AgderGrimstadNorway

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