Skip to main content

Advertisement

Log in

A case study on AGV’s alternatives selection problem

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Current trend for Factory Automation is to design a variety of products to meet the changing demands of the customers. Automated material handling is an integral part of any automated manufacturing system. Automated Guided Vehicle (AGVs) is emerging as intelligent Material Handling Equipments (MHE) that is capable of adjusting to the changing scenario of manufacturing systems. AGVs are battery-powered, automated vehicles with capabilities to tag along programmed motions and orientation. AGVs selection has always been a challenging task owing to several constraints of manufacturing systems. Different MCDM (Multiple Criteria Decision Making) approaches have been employed by researchers for selection of MHE problem. This work is an attempt to choose amongst the AGVs alternatives and analyze their performance using certain MCDM approaches such as AHP, DEMATEL, TOPSIS, Fuzzy AHP, Fuzzy DEMATEL and Fuzzy TOPSIS. In the present work, data has been taken from Maniya and Bhatt [27] and the problem has been evaluated using four integrated approaches for analysis of AGV’s selection problem. It was found that Fuzzy AHP-Fuzzy TOPSIS and Fuzzy DEMATEL—Fuzzy TOPSIS technique gave better results for the instant case as they were devoid of vagueness error caused due to uncertainty in the decision making of experts. Authors have applied four approaches out of which DEMATEL-TOPSIS and Fuzzy DEMATEL-Fuzzy TOPSIS approaches has yet not been applied in AGVs selection problem, thus justifying original work and their appropriateness for the problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

AGV:

Automated guided vehicle

AHP:

Analytical hierarchical process

DEMATEL:

Decision making trial and evaluation laboratory

FAHP:

Fuzzy analytical hierarchical process

FDEMATEL:

Fuzzy decision making trial and evaluation laboratory

FAHP:

Fuzzy analytical hierarchical process

FTOPSIS:

Fuzzy technique for order preference by similarity to ideal solution

MCDM:

Multiple criteria decision making

MHE:

Material handling equipment

TOPSIS:

Technique for order preference by similarity to ideal solution

References

  1. Agarwal D, Singholi AKS, Tayal A (2015). Comparison of integrated approach of AHP-topsis and fuzzy AHP-fuzzy topsis to evaluate facility layout problem in indian manufacturing enterprises: a case study. 15th global conference on flexible system management 15:1055—1069

  2. Altuntas S, Selim H, Dereli T (2014) A fuzzy DEMATEL-based solution approach for facility layout problem: a case study. Int J Adv Manuf Technol 73:749–771. https://doi.org/10.1007/s00170-014-5826-3

    Article  Google Scholar 

  3. Aydogan EK (2011) Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment. Expert Syst Appl 38:3992–3998. https://doi.org/10.1016/j.eswa.2010.09.060

    Article  Google Scholar 

  4. Baykasoğlu A, Kaplanoğlu V, Durmuşoğlu ZD, Şahin C (2013) Integrating fuzzy DEMATEL and fuzzy hierarchical TOPSIS methods for truck selection. Expert Syst Appl 40(3):899–907. https://doi.org/10.1007/s00521-014-1771-1

    Article  Google Scholar 

  5. Behzadian M, Otaghsara SK, Yazdani M, Ignatius J (2012) A state-of the-art survey of TOPSIS applications. Expert Syst Appl 39:13051–13069. https://doi.org/10.1016/j.eswa.2012.05.056

    Article  Google Scholar 

  6. Bharitkar UD, Kale SR, Agarwal AV, Lodha BR, Deshpande SP (2015) A structured approach to material handling system selection and specification for manufacturing. Proceedings of 27th IRF international conference 27:55–59

  7. Chakraborty S, Banik D (2006) Design of a material handling equipment selection model using analytic hierarchy process. Int J Adv Manuf Technol 28:1237–1245. https://doi.org/10.1007/s00170-004-2467-y

    Article  Google Scholar 

  8. Chamzini YA, Shariati S (2013) Selection of material handling equipment system for surface mines by using a combination of fuzzy MCDM Models. Int Res J Appl Basic Sci 5(12):1501–1511

    Google Scholar 

  9. Chan FTS, Ip RWL, Lau H (2001) Integration of expert system with analytic hierarchy process for the design of material handling equipment selection system. J Mater Process Technol 116:137–145. https://doi.org/10.1016/S0924-0136(01)01038-X

    Article  Google Scholar 

  10. Chang B, Chang CW, Wu CH (2011) Fuzzy DEMATEL method for developing supplier selection criteria. Expert Syst Appl 38:1850–1858. https://doi.org/10.1016/j.eswa.2010.07.114

    Article  Google Scholar 

  11. Cho CH, Chae SW, Kim KH (2017) Simplified evaluation criterion for concepts of engineering design based on cost, simplicity, and safety. J Mech Sci Technol 31(9):4319–4328. https://doi.org/10.1007/s12206-017-0830-9

    Article  Google Scholar 

  12. Dağdeviren M (2008) Decision making in equipment selection: an integrated approach with AHP and PROMETHEE. J Intell Manuf 19(4):397–406. https://doi.org/10.1007/s10845-008-0091-7

    Article  Google Scholar 

  13. Dongre A, Mohite NY (2015) Significance of selection of material handling system design in industry—a review. Int J Eng Res Gen Sci 3(2):2091–2730

    Google Scholar 

  14. Falatoonitoosi E, Ahmed S, Sorooshian S (2014) Expanded DEMATEL for determining cause and effect group in bidirectional relations. Sci World J. https://doi.org/10.1155/2014/103846

    Article  Google Scholar 

  15. Fonseca DJ, Uppal G, Greene TJ (2004) A knowledge-based system for conveyor equipment selection. Expert Syst Appl 26(4):615–623. https://doi.org/10.1016/j.eswa.2003.12.011

    Article  Google Scholar 

  16. Fontela E, Gabus A (eds) (1976) The DEMATEL observe. Battelle Institute, Geneva Research Center

  17. Gavade RK (2014) Multi-criteria decision making: an overview of different selection problems and methods. Int J Comput Sci Inf Technol 5(4):5643–5646

    Google Scholar 

  18. Hadi-Vencheha A, Mohamadghasemi A (2015) A new hybrid fuzzy multi-criteria decision making model for solving the material handling equipment selection problem. Int J Comput Integr Manuf 28(5):534–550. https://doi.org/10.1080/0951192X.2014.880948

    Article  Google Scholar 

  19. Hamid S, Mirhosseyni L, Webb P (2009) A hybrid fuzzy knowledge-based expert system and genetic algorithm for efficient selection and assignment of material handling equipment. Expert Syst Appl 36:11875–11887. https://doi.org/10.1016/j.eswa.2009.04.014

    Article  Google Scholar 

  20. Karande P, Chakraborty C (2013) Material handling equipment selection using weighted utility additive theory. J Ind Eng 2013:1–9. https://doi.org/10.1155/2013/268708

    Article  Google Scholar 

  21. Kodali R (1997) Knowledge-based system for selection of an AGV and a WorkCentre for transport of a part in on-line scheduling of FMS. Prod Plan Control 8(2):114–122. https://doi.org/10.1080/095372897235370

    Article  Google Scholar 

  22. Kulak O (2005) A decision support system for fuzzy multi-attribute selection of material handling equipments. Expert Syst Appl 29(2):310–319. https://doi.org/10.1016/j.eswa.2005.04.004

    Article  Google Scholar 

  23. Lashgari A, Chamzini AY, Fouladgar MM, Zavadskas EK, Shafiee S, Abbate N (2012) Equipment selection using fuzzy multi criteria decision making model: key study of Gole Gohar iron min. Eng Econ 23(2):125–136. https://doi.org/10.5755/j01.ee.23.2.1544

    Article  Google Scholar 

  24. Lertprapai S (2013) Review: multiple criteria decision making method with applications. Int Math Forum 8(7):347–355

    Article  MathSciNet  Google Scholar 

  25. Lo CC, Chen DY, Tsai CF, Chao KM (2010) Service selection based on fuzzy TOPSIS method. In proceedings of the 24th IEEE international conference on advanced information networking and applications workshops 24:367–372. https://doi.org/10.1109/waina.2010.117

  26. Maniya KD, Bhatt MG (2011) A multi-attribute selection of automated guided vehicle using the AHP/M-GRA technique. Int J Prod Res 49(20):6107–6124. https://doi.org/10.1080/00207543.2010.518988

    Article  Google Scholar 

  27. Onut S, Soner KS, Mert S (2009) Selecting the suitable material handling equipment in the presence of vagueness. Int J Adv Manuf Technol 44(7–8):818–828. https://doi.org/10.1007/s00170-008-1897-3

    Article  Google Scholar 

  28. Razmi J, Taghisadeh MR, Asadzadeh SM (2007) Evaluating of an AGV system in a CIM unit: a simulation approach. J Inf Technol 6(2):304–309

    Article  Google Scholar 

  29. Saaty TL (2004) Decision making—the analytic hierarchy and network processes (AHP/ANP). J Syst Sci Syst Eng 13(1):1–35. https://doi.org/10.1007/s11518-006-0151-5

    Article  Google Scholar 

  30. Sangaiah AK, Subramaniam PR, Zheng X (2015) A combined fuzzy DEMATEL and fuzzy TOPSIS approach for evaluating GSD project outcome factors. Neural Comput Appl 26(5):1025–1040. https://doi.org/10.1016/j.eswa.2012.05.046

    Article  Google Scholar 

  31. Saputro TE, Masudin I, Rouyendegh BD, Erdebilli B (2015) A literature review on MHE selection problem: levels, contexts, and approaches. Int J Prod Res 53(17):5139–5152. https://doi.org/10.1080/00207543.2015.1005254

    Article  Google Scholar 

  32. Sawant VB, Mohite SS (2009) Investigations on benefits generated by using fuzzy numbers in a TOPSIS model developed for automated guided vehicle selection problem. In: Sakai H, Chakraborty MK, Hassanien AE, Ślęzak D, Zhu W (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2009. Lecture Notes in Computer Science, vol 5908. Springer, Berlin, Heidelberg, pp 295–302

    Google Scholar 

  33. Sawant VB, Mohite SS (2013) A composite weight based multiple attribute decision support system for the selection of automated guided vehicle. Int J Comput Appl 70(19):8–16. https://doi.org/10.5120/12173-8222

    Article  Google Scholar 

  34. Sawant VB, Mohite SS, Patil R (2011) A decision-making framework using a preference selection index method for automated guided vehicle selection problem. International conference on technology systems and management (ICTSM) proceedings published by international journal of computer applications (IJCA)

  35. Sayarshad HR (2010) Using bees algorithm for material handling equipment planning in manufacturing systems. Int J Adv Manuf Technol 48:1009–1018. https://doi.org/10.1007/s00170-009-2363-6

    Article  Google Scholar 

  36. Sujono S, Lashkari RS (2007) A multi-objective model of operation allocation and material handling system selection in FMS design. Int J Prod Econ 105:116–133. https://doi.org/10.1016/j.ijpe.2005.07.007

    Article  Google Scholar 

  37. Tabucanon MT, Batanov DN, Verma DK (1994) Decision support system for multicriteria machine selection for flexible manufacturing systems. Comput Ind 25(2):131–143. https://doi.org/10.1016/0166-3615(94):90044-2

    Article  Google Scholar 

  38. Torlak G, Sevkli M, Sanal M, Zaim S (2011) Analyzing business competition by using fuzzy TOPSIS method: an example of Turkish domestic airline industry. Expert Syst Appl 38:3396–3406. https://doi.org/10.1016/j.eswa.2010.08.125

    Article  Google Scholar 

  39. Triantaphyllou E, Shu B, Sanchez SN, Ray T (1998) Multi-criteria decision making: an operations research approach. In: Webster JG (ed) Encyclopaedia of electrical and electronics engineering, 15th edn. John Wiley and Sons, New York, pp 175–186

    Google Scholar 

  40. Tripathy K, Tripathy DK (2016) Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysis. Eng Sci Technol Int J 19(2016):62–70

    Google Scholar 

  41. Tuzkaya G, Gulsan B, Kahraman C, Ozgen D (2010) An integrated fuzzy multi-criteria decision making methodology for material handling equipment selection problem and an application. Expert Syst Appl 37:2853–2863. https://doi.org/10.1016/j.eswa.2009.09.004

    Article  Google Scholar 

  42. Uygun O, Kacamak H, Kahraman UA (2014) An integrated DEMATEL and fuzzy ANP techniques for evaluation and selection of outsourcing provider for a telecommunication company. Comput Ind Eng 86:137–146. https://doi.org/10.1016/j.cie.2014.09.014

    Article  Google Scholar 

  43. Veeravalli B, Rajesh G, Viswanadham N (2002) Design and analysis of optimal material distribution policies in flexible manufacturing systems using a single AGV. Int J Prod Res 40(12):2937–2954. https://doi.org/10.1080/00207540210137648

    Article  MATH  Google Scholar 

  44. Velasquez M, Hester PT (2013) An analysis of multi-criteria decision making methods. Int J Oper Res 10(2):56–66

    MathSciNet  Google Scholar 

  45. Yaman R (2001) A knowledge-based approach for selection of material handling equipment and material handling system predesign. Turk J Eng Environ Sci 25(4):267–278

    Google Scholar 

  46. Yang T, Hung CC (2007) Multiple-attribute decision making methods for plant layout design problem. Robotics Comput Integr Manuf 23:126–137. https://doi.org/10.1016/j.rcim.2005.12.002

    Article  Google Scholar 

  47. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

    Article  MATH  Google Scholar 

  48. Zhe W, Yixiong F, Zhaoxi H, Jianrong T (2017) A TEL decision method of process parameters for smart energy efficient manufacturing. J Mech Sci Technol 31(8):3897–3905. https://doi.org/10.1007/s12206-017-0735-7

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Divya Agarwal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agarwal, D., Bharti, P.S. A case study on AGV’s alternatives selection problem. Int. j. inf. tecnol. 14, 1011–1023 (2022). https://doi.org/10.1007/s41870-018-0223-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-018-0223-z

Keywords

Navigation