Skip to main content

Advertisement

SpringerLink
Log in

Approaches of fuzzy systems applied to an AGV dispatching system in a FMS

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Excellence in manufacturing systems has been recognized as one of the main factors behind the success of industrial companies or production companies. New technology for manufacturing processes plays a significant role in this process. Achieving the potential of technological innovations in production, however, requires a wide range of management, as well as engineering issues related to the system. The handling capacity of advanced materials is essential because without this ability of providing the material needed for the proper workstation at the right time and in the right amount, the whole plant will become “bogged down.” This makes it less efficient and thus produces less profit, and/or it operates at higher costs. This paper proposes two approaches for the dispatching of AGV (automated guided vehicle) using systems fuzzy. The first use hierarchical fuzzy rule-based model building in which the main feature is to make the base of fuzzy rules is smaller and simpler but with high coverage and interpretability. The second use adaptive genetic fuzzy system with simple prediction in which the main feature is to increase the sensitivity of the system about the variables. Both approaches using multiple attributes and having the objective decrease the makespan in a FMS (flexible manufacturing system).

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

Similar content being viewed by others

References

  1. Joshi SB, Smith JS (1994) Computer control of flexible manufacturing systems: research and development. Chapman & Hall, p 427

  2. Evers JJM, Koppers SAJ (1996) Automated guided vehicle traffic control at a container terminal. Transp Res A Policy Pract 30(1):21–34

    Article  Google Scholar 

  3. Qiu L, Hsu W, Huang S, Wang H (2002) Scheduling and routing algorithms for AGVs: a survey. Int J Prod Res 40(3)

  4. Ye R, Hsu WJ, Vee VY (2000) Distributed routing and simulation of automated guided vehicles. In: IEEE Region 10 Annual International Conference, Malaysia

  5. Seifert RW, Kay MG (1995) Evaluation of AGV routing strategies using hierarchical simulation. In: Winter Simulation Conference, Arlington, USA

  6. Le-Anh T, De Koster MBM (2006) A review of design and control of automated guided vehicle systems. Eur J Oper Res 171(1):1–23

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  8. Egbelu PJ (1987) Pull versus push strategy for automated guided vehicle load movement in a batch manufacturing system. J Manuf Syst 6(3):209–221

    Article  Google Scholar 

  9. Bartholdi JJ III, Platzman LK (1989) Decentralized control of automated guided vehicles on a simple loop. IIE Trans (Inst Ind Eng) 21(1):76–81

    Google Scholar 

  10. Han MH, Mcginnis LF (1989) Control material handling transporter in automated manufacturing. IIE Trans (Inst Ind Eng) 21(2):184–190

    Google Scholar 

  11. Taghaboni F (1989) Scheduling and control of manufacturing systems with critical material handling. Ph. D. Thesis – Purdue University, West Lafayette, Indiana

  12. Kim SH, Hwang H (199) Adaptive dispatching algorithm for automated guided vehicles based on an evolutionary process. Int J Prod Econ 60

  13. Tan KK, Tang KZ (2000) Simulation of an evolutionary tuned fuzzy dispatching system for automated guided vehicles. In: Winter Simulation Conference, Orlando, USA

  14. Jeong BH, Randhawa SU (2001) A multi-attribute dispatching rule for automated guided vehicle systems. Int J Prod Res 39(13):2817–2832

    Article  MATH  Google Scholar 

  15. Benincasa AX, Morandin O Jr., Kato ERR (2003) Reactive fuzzy dispatching rule for automated guided vehicles. Syst Man Cybern 5

  16. Umashankar N, Karthik VN (2006) Multi-criteria intelligent dispatching control of automated guided vehicles in FMS. In: Ieee Conference on Cybernetics and Intelligent Systems, Thailand

  17. Naso D, Turchiano B (2005) Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence. IEEE Trans Syst Man Cybern Part B Cybern 35(2)

  18. Hidehiko Y (2008) Addition and deletion of agent memories to realize autonomous decentralized manufacturing systems. In: Lecture notes in computer science, Wroclaw, Poland

  19. Smolic-Rocak N, Bogdan S, Kovacic Z, Petrovic T (2010) Time windows based dynamic routing in multi-AGV systems. IEEE Trans Autom Sci Eng 7(1)

  20. Chiba R, Arai T, Ota J (2010) Integrated design for automated guided vehicle systems using cooperative co-evolution. Adv Robot 24(1–2):25–45

    Article  Google Scholar 

  21. Pedrycz W (1996) Fuzzy modelling: paradigms and practice. Kluwer, Dordrecht

    Book  MATH  Google Scholar 

  22. Dumitrescu D, Lazzerini B, Jair L (2000) Fuzzy sets and their application to clustering and training. Int. Series on Computational Intelligence, CBC Press

  23. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans Syst Man Cybern 116–132

  24. Buckley JJ, Hayashi Y (1993) Numerical relationships between neural networks, continuous functions, and fuzzy systems. Fuzzy Sets Syst 60:1–8

    Article  MathSciNet  Google Scholar 

  25. Driankov D, Hellendoorn H, Reinfrank M (1993) An introduction to fuzzy control. Springer, Berlin

    Book  MATH  Google Scholar 

  26. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  27. Wang P et al (1995) Constructing fuzzy systems. Proc. VI IFSA Congr. San Paulo

  28. Wang LX, Mendel JM (1992) Fuzzy basis functions, universal approximation and orthogonal least-squares learning. IEEE Trans Neural Netw 3:807–814

    Article  Google Scholar 

  29. Wang LX (1994) Adaptive fuzzy systems and control design and stability analysis. PTR Prentice-Hall, Englewood Cliffs

    Google Scholar 

  30. Zeng X-J, Singh MG (1994) Approximate theory of fuzzy systems—SISO case. IEEE Trans Fuzzy Syst 2:162–176

    Article  Google Scholar 

  31. Wang L (2003) The WM method completed: a flexible fuzzy system approach to data mining. IEEE Int Conf Fuzzy Syst 11:768–782

    Article  Google Scholar 

  32. Chen Y, Yang B, Abraham A, Peng L (2007) Automatic design of hierarchical Takagi–Sugeno type fuzzy systems using evolutionary algorithms. IEEE Trans Fuzzy Syst 15(3):385–397

    Article  MATH  Google Scholar 

  33. Gedeon TD, Wong KW, Tikk D (2001) Constructing hierarquical fuzzy rules bases for classification. The 10th IEEE International Fuzzy Systems Conference, vol. 3

  34. Sugeno M, Yasukawa T (1993) A fuzzy-logic-based approach to qualitative modeling. IEEE Trans Fuzzy Syst 1(1)

  35. Vieira SM, Mendonga LF, Sousa JMC (2005) Modified regularity criterion in dynamic fuzzy modeling applied to industrial processes. Fuzzy systems, 2005. FUZZ ′05. The 14th IEEE International Conference on, vol., no. p 483–488

  36. Wang LX, Mendel JM (1992) Generationg fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22(6):1414–1427

    Article  MathSciNet  Google Scholar 

  37. Akbarzadeh-T MR, Kumbla K, Tunstel E, Jamshidi M (2000) Soft computing for autonomous robotic systems. Comput Electr Eng 26(1):5–32

    Article  Google Scholar 

  38. Martins PG, Laugeni FP (2005) Production management. Saraiva, São Paulo

    Google Scholar 

  39. Berman S, Schechtman E, Edan Y (2009) Evaluation of automatic guided vehicle systems. Robot Comput Integr Manuf 25(3):522–528

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Federal University of São Carlos, Rodovia Washington Luis, Km 235, 13565-905, São Carlos, SP, Brazil

    V. F. Caridá, O. Morandin Jr. & C. C. M. Tuma

Authors
  1. V. F. Caridá
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. O. Morandin Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. C. M. Tuma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. F. Caridá.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Caridá, V.F., Morandin, O. & Tuma, C.C.M. Approaches of fuzzy systems applied to an AGV dispatching system in a FMS. Int J Adv Manuf Technol 79, 615–625 (2015). https://doi.org/10.1007/s00170-015-6833-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-015-6833-8

Keywords

Search

Navigation