An new approach for the automatic analysis and control of material handling systems: integrated layout flow analysis (ILFA)

Abstract

In the majority of small and medium sized enterprises (SEMs), the direct costs of material handling cannot be clearly measured. There are several reasons for this, including the large number of product types, complexity of their production cycle, and continuous change in markets. Therefore, production managers require flexible tools to create a suitable material handling system model which explicitly and rapidly calculates the indices required as these are traditionally neglected or laboriously approximated, (i.e., time and cost in material flow inside the factory, storage area requirements, and MH utilization percentage). This paper proposes an integrated approach to analyzing and controlling material handling operations in an industrial manufacturing plant from a “full quantitative” point of view. The model presented unites quite different fields of research into a unique methodology. This material handling model rapidly and automatically provides production managers with extensive and significant information. As a result, integrated layout flow analysis interrelates systematic layout planning with operational research algorithms and visual interactive simulation, using a complete software platform to implement them. This integrated layout flow analysis approach focuses on determining the space requirement for manufacturing department buffers, the transportation system requirements, the performance indices, and the time and cost of material flows spent in the layout and in MH traffic jams.

This is a preview of subscription content, access via your institution.

References

  1. 1.

    Tompkins JA, White JA, Bozer YA, Frazelle EH, Tanchoco JMA, Trevino J (2003) Facilities Planning, 3rd edn, Wiley, New York

    Google Scholar 

  2. 2.

    De Almeida D, Kellert P (2000) “An analytical queuing network model for flexible manufacturing systems with discrete handling device and transfer blockings”. Int J Flex Manuf Syst 12:25–27, DOI 10.1023/A:1008171031506

    Article  Google Scholar 

  3. 3.

    Pereira J, Paulre B (2001) Flexibility in manufacturing systems: A relational and a dynamic approach. Europ J Op Res 130(1):70–82, DOI 10.1016/S0377-2217(00)00020-5

    MATH  Article  MathSciNet  Google Scholar 

  4. 4.

    Koopmans TC, Beckman M (1957) Assignment problems and the location of economic activities. Econometrica 25:53–76, DOI 10.2307/1907742

    MATH  Article  MathSciNet  Google Scholar 

  5. 5.

    Kusiak A, Heragu SS (1987) “The facility layout problem”. Europ J Op Res 29:229–251, DOI 10.1016/0377-2217(87)90238-4

    MATH  Article  MathSciNet  Google Scholar 

  6. 6.

    Heragu SS, Kusiak A (1991) Efficient models for the facility layout problem. Europ J Op Res 53(1):1–13, DOI 10.1016/0377-2217(91)90088-D

    MATH  Article  Google Scholar 

  7. 7.

    Heragu SS, Kusiak A (1990) Machine layout: an optimization and knowledge-based approach. Int J Prod Res 28(4):615–635, DOI 10.1080/00207549008942744

    Article  Google Scholar 

  8. 8.

    Drira A, Pierreval H, Hajri-Gabouj S (2007) Facility layout problems: A survey. Annu Rev Control 31(2):255–267

    Google Scholar 

  9. 9.

    Moore JM, Lee RC (1967) “CORELAP, computerized relationship layout planning”. J Ind Eng 18:3

    Google Scholar 

  10. 10.

    Seehof M, Evans WO (1967) Automated layout design program. J Ind Eng 19:690–695

    Google Scholar 

  11. 11.

    Armour GC, Buffa ES (1963) A heuristic algorithm and simulation approach to relative allocation of facilities. Manag Sci 9:294–309

    Article  Google Scholar 

  12. 12.

    Yang T, Peters BA, Tu M (2005) Layout design for flexible manufacturing systems considering single-loop directional flow patterns. Europ J Op Res 164:440–455, DOI 10.1016/j.ejor.2003.04.004

    MATH  Article  Google Scholar 

  13. 13.

    Castillo I, Peters BA (2004) Integrating design and production planning considerations in multi-bay manufacturing facility layout. Europ J Op Res 157:671–687, DOI 10.1016/S0377-2217(03)00296-0

    MATH  Article  MathSciNet  Google Scholar 

  14. 14.

    Braglia M, Zanoni S, Zavanella L (2003) Layout design in dynamic environments: Strategies and quantitative indices. Int J Prod Res 41:995–1016, DOI 10.1080/00207540210162983

    MATH  Article  Google Scholar 

  15. 15.

    Ferrari E, Pareschi A, Persona A, Regattieri A (2003) Plant layout computerized design: logistic and re-layout software (LRP). Int J Adv Manuf Tech 12:917-922

    Google Scholar 

  16. 16.

    Holland HJ (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  17. 17.

    Hu L, Yasuda K (2006) Minimising material handing cost in cell formation with alternative processing routes by grouping genetic algorithm. Int J Prod Res 44(11):2133 - 2167, DOI 10.1080/00207540500336108

    MATH  Article  Google Scholar 

  18. 18.

    Vin E, De Lit P, Delchambre A (2005) A multiple-objective grouping genetic algorithm for the cell formation problem with alternative routings. J Intell Manuf 16(2):189–205, DOI 10.1007/s10845-004-5888-4

    Article  Google Scholar 

  19. 19.

    Brown EC, Sumichrast RT (2001) CF-GGA: A grouping genetic algorithm for the cell formation problem. Int J Prod Res 39(16):3651–3669

    MATH  Article  Google Scholar 

  20. 20.

    James TL, Brown C, Keeling KB (2007) A hybrid grouping genetic algorithm for the cell formation problem. Comp Op Res 34(7):2059–2079

    MATH  Article  Google Scholar 

  21. 21.

    Kim KS, Chung BD, Jae M (2003) A design for a tandem AGVS with multi-load AGVs. Int J Adv Manuf Technol 22:744–752, DOI 10.1007/s00170-003-1614-1

    Article  Google Scholar 

  22. 22.

    Kim KS, Jae M (2003) An object-oriented simulation and extension for tandem AGV systems. Int J Adv Manuf Technol 22:441–455, DOI 10.1007/s00170-003-1542-0

    Article  Google Scholar 

  23. 23.

    Huang C, Nof SY (1998) Development of an integrated models for material flow design and control-a tool perspective”. Robot Comp Int Manuf 14:441–454, DOI 10.1016/S0736-5845(98)00019-2

    Article  Google Scholar 

  24. 24.

    Suri R (1995) Quantitative techniques for robotic system analysis. Handbook of Industrial Robotics, Wiley, New York, pp 605–638

    Google Scholar 

  25. 25.

    Muther R (1961) Systematic Layout Planning. Von Nostrand, New York

    Google Scholar 

  26. 26.

    Dantzig GB (1949) “oftware of interdependent activities: II mathematical model. Econometrica. 17:200–211, DOI 10.2307/1905523

    Article  MathSciNet  Google Scholar 

  27. 27.

    Murty KG (1976) Linear and combinatorial programming. Wiley, New York

    MATH  Google Scholar 

  28. 28.

    Dijkstra EW (1959) A note on two problem in connexion with graphs. Numer Math 1:269–271, DOI 10.1007/BF01386390

    MATH  Article  MathSciNet  Google Scholar 

  29. 29.

    Hollier RH (1987) Automated guided vehicle system. Springer, Berlin

    Google Scholar 

  30. 30.

    Ferrari E, Gamberi M, Manzini R, Pareschi A, Persona A, Regattieri A (2002) Effectiveness of dynamic simulation supporting and optimising design and management of warehouse facilities, Proc Business and Industry Symposium. ASTC, San Diego, pp 76–81

    Google Scholar 

  31. 31.

    Hillier FS, Liberman GJ (1980) Introduction to operational research. Holden-Day, San Francisco

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Mauro Gamberi.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Gamberi, M., Manzini, R. & Regattieri, A. An new approach for the automatic analysis and control of material handling systems: integrated layout flow analysis (ILFA). Int J Adv Manuf Technol 41, 156 (2009). https://doi.org/10.1007/s00170-008-1466-9

Download citation

Keywords

  • Material handling
  • Plant layout analysis
  • Automated guided vehicle