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Integrated tasks assignment and routing for the estimation of the optimal number of AGVS

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A fundamental problem in the management of an automated guided vehicle system (AGVS) is the determination of the load to be transported and the vehicle to transport it. The time for the loading and unloading of pallets must be specified as soon as possible. Typical objectives are minimization of travel times and costs by the reduction of the number of vehicles required to fulfill a given transportation order. This article presents a methodology for the estimation the minimum number of AGVs (considering all the available ones at the shop floor level) required to execute a given transportation order within a specific time-window. A comparison is made between the algorithms Shortest Job First and meta-heuristic Tabu Search (applied to an initial solution) for a task assignment. An enhanced Dijkstra algorithm is used for the conflict-free routing task. The number of vehicles is estimated so as to provide an efficient distribution of tasks and reduce the operational costs of the materials handling system. Simulation results of two typical industrial warehouse shop floor scenarios are provided. Although the study focuses on pre-planning of order fulfillment of materials handling, the proposed methodology can also be utilized as an important tool for investment analysis of the warehouse layout design and for estimating the ideal number of AGVs.

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  1. 1.

    Vivaldini KCT, Galdamens JPM, Pasqual TB, Sobral RM, Araujo RC, Becker M, Caurin GAP (2010) Automatic routing system for intelligent warehouses. In: IEEE International Conference on Robotics and Automation, vol 1, pp 1–6

  2. 2.

    Yifei T, Junruo C, Meihong L, Xianxi L, Yali F (2010) An estimate and simulation approach to determining the automated guided vehicle fleet size in FMS. In: 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT Chengdu, China 2010, vol 9, pp 432–435

  3. 3.

    Vis IFA (2006) Survey of research in the design and control of automated guided vehicle systems. Eur J Oper Res Amsterdam 170(3):677–709

  4. 4.

    Arifin R, Egbelu P (2000) Determination of vehicle requirements in automated guided vehicle systems: a statistical approach. Prod Plan Control 11(3):258–270

  5. 5.

    Fauadi M HFM, Yahaya SH, Murata T (2013) Intelligent combinatorial auctions of decentralized task assignment for AGV with multiple loading capacity. IEEJ Trans Electr Electron Eng 8:371–379

  6. 6.

    Van Der Meer JR (2000) Operational control of internal transport. ERIM Ph.D. Series Research in Management 1, Erasmus University Rotterdam

  7. 7.

    Yoo J, Sim E, Cao C, Park J (2005) An algorithm for deadlock avoidance in an AGV system. Int J Adv Manuf Technol 26(5-6):659–668

  8. 8.

    Bodin LD, Golden BL (1981) Classification vehicle routing and scheduling. Networks, New York 11 (2):97–108

  9. 9.

    Kolen AWJ, et al. (1987) Vehicle routing with time windows. Oper Res 35(2):266–273

  10. 10.

    Broadbent AJ, et al. (1987) Free-ranging AGV and scheduling system. In: Automated guided vehicle systems, pp 301–309

  11. 11.

    Chiba R, Ota J, Arai T (2002) Integrated design with classification of transporter routing for AGV systems. In: IEEE/RSJ International Conference Intelligent Robots and Systems, 2002, Switzerland. Proceedings Lausanne: EPFL, vol 2 , pp 1820–1825

  12. 12.

    Duinkerken MB, Ottjes JA (2000) A simulation model for automated container terminals.. In: Business Industry Simulation Symposium, 2000, Washington, D.C., 2000. Proceedings [S.l.:s.n.], p 134149

  13. 13.

    Jula H, et al. (2000) Container terminals using automated shuttles driven by linear motors. In: IFAC Symposius Control in Transportation System, 9., 2000, Braunschweig. Proceedings Oxford: Pergamon, pp 1–6

  14. 14.

    Kim KH, Bae JW (2004) A look-ahead dispatching method for automated guided vehicles in automated port container terminals. Transfus Sci Baltimore 38(2):224–234

  15. 15.

    Lee C, Ventura JA (2001) Optimal dwell point location of automated guided vehicle to minimize mean response time in a loop layout. Int J Protein Res Londom 39(17):4013–4031

  16. 16.

    Liu CI, et al. (2000) Comparing different technologies for containers movement in marine container terminals. In: IEEE International Conference Intelligent Transportation Systems, 2000. Proceedings New York: IEEE, pp 488–493

  17. 17.

    Mhring RH, et al. (2004) Conflict-free real-time AGV routing. In: Hein F, Dic H, Peter K (eds) Operations research proceedings 2004. Springer Berlin Heidellberg, Berlin, pp 18–24

  18. 18.

    Sai-Nan L (2008) Optimization problem for AGV in automated warehouse system. In: IEEE International Conference on Service Operations and Logistics, and Informatics, New York. Proceedings New York: IEEE, vol 2, pp 1640–1642

  19. 19.

    Liu CI, Jula H, Ioannou PA (2002) Design, simulation, and evaluation of automated container terminals. IEEE Trans Intell Transp Syst 3(1):12–26

  20. 20.

    Egbelu PJ (1987) The use of non-simulation approaches in estimating vehicle requirements in an automated guided vehicle based transport system. Material Flow 4:17–32

  21. 21.

    Kasilingam RG (1991) Mathematical modeling of the AGVS capacity requirements planning problem. Engrneering Costs and Production Economics 2(1):171–175

  22. 22.

    Mahadevan B, Narendrau TT (1993) Estimation of number of AGVS for an FMS: an analytical model. Int J Protein Res 31(7):1655–1670

  23. 23.

    Rajotia S, Shanker K, Batra JL (1998) Determination of optimal AGV fleet size for an FMS. Int J Protein Res 36(5):1177–1198

  24. 24.

    Vis IFA, De Koster R, Roodbergen KJ, Peeters LWP (2001) Determination of the number of automated guided vehicles required at a semi-automated container terminal. J Oper Res Soc 52(4): 409–417

  25. 25.

    Koo P, Jang J, Huh J (2005) Estimation of part waiting time and fleet sizing in AGV systems. Int J Flex Manuf Syst 16:211–228

  26. 26.

    Ji M, Xia J (2010) Analysis of vehicle requirements in a general automated guided vehicle system based transportation system. Computers & Industrial Engineering 59:544–551

  27. 27.

    Mantel RJ, Landeweerd HRA (1995) Design and operational control of an AGV system. International Journal Production Economics, pp 257–266

  28. 28.

    Vosniakos G-C, Davies BJ (1988) Simulation study of an AGV system in an FMS environment. Int J Adv Manuf Technol 3(4): 33–46

  29. 29.

    Bowersox DJ, Closs DJ, Cooper MB (2007) Supply chain logistics management 2nd ed. McGraw-Hill, New York, pp 108–109,176,241

  30. 30.

    Le-anh T (2005) Intelligent control of vehicle-based internal transport systems, Ph.D Dissertation, ERIM Ph.D series research in management 51, Erasmus University Rotterdam

  31. 31.

    Morandin O, Carida Vinicius F, Kato Edilson RR, Tuma Carlos, CM Adaptive genetic fuzzy, predictive and multiobjective approach for AGVs dispatching. In: IECON 2011 37th Annual Conference of IEEE Industrial Electronics, 2011, Melbourne. IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society, vol 1, pp 2317–2322

  32. 32.

    Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13:533–549

  33. 33.

    Dijkstra EW (1959) A note on two problems in connection with graphs. Numerische Mathematik 1:269–271

  34. 34.

    Desrosiers J, Soumis F, Descrochers M, Sauv M (1986) Vehicle routing and scheduling with time windows, Netflow at Pisa. Springer Berlin Heidelberg, pp 249–251

  35. 35.

    Silberschatz A, Galvin PB, Gagne G (2005) Operating systems concepts (7th ed). Wiley, p 161. ISBN 0-471-69466-5

  36. 36.

    Tanenbaum Adrew S (2007) Modern operating systems, 3rd ed. Prentice Hall Press, Upper Saddle River, NJ, USA

  37. 37.

    Ishwari SR, Deepa G (2012) A priority based round robin CPU scheduling algorithm for real time systems. International Journal of Innovations in Engineering and Technology (IJIET) 1(3):1–11

  38. 38.

    Vivaldini KCT, Rocha LF, Becker M, Moreira AP (2015) Comprehensive Review of the Dispatching, Scheduling and Routing of AGVs.. In: CONTROLO, 2014, Proceedings of the 11th Portuguese Conference on Automatic Control. Lecture Notes in Electrical Engineering, vol 321. Springer International Publishing, pp 505–514

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Correspondence to Kelen Vivaldini.

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Vivaldini, K., Rocha, L.F., Martarelli, N.J. et al. Integrated tasks assignment and routing for the estimation of the optimal number of AGVS. Int J Adv Manuf Technol 82, 719–736 (2016).

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  • Automated guided vehicles
  • Task scheduling
  • Routing
  • Collision avoidance