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Proposing a graph ranking method for manufacturing system selection in high-tech industries

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Abstract

All manufacturing centers are looking for the solutions to reduce costs and increase their competitive advantages. One of the practical solutions for cost reduction is to select a suitable manufacturing system in order to optimize usage of limited resources. In high-tech industries, the manufacturing system selection is extremely difficult because of the complex features and structures of their products. Generally, selecting the best manufacturing system of high-tech products is a multiple-criteria decision-making (MCDM) problem. Graph ranking method is one of the most used techniques among MCDM methods, which is originated from combinatorial mathematics. Simple computational procedure, ability to consider relationships between criteria, etc., are some perfect characteristics of this method for modeling and solving decision-making problems with complexity. Therefore, this study attempted to determine the most suitable manufacturing system in high-tech industries using graph ranking method. Moreover, because of vagueness and imprecision in human judgments, fuzzy set theory is utilized in the evaluation procedure. The suggested approach was used to select the most appropriate system for LCD manufacturing at Sanam Electronic Company. Finally, Obtained results indicated the efficiency of the proposed approach and selection of a cloud-based manufacturing system as the most suitable manufacturing system in high-tech industries.

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Notes

  1. Analytical hierarchy process.

  2. Preference ranking organization method for enrichment evaluations.

  3. Technique for order preference by similarity to ideal solution.

  4. Elimination ET choice translating reality.

References

  1. Iran chamber of commerce, industries, mines and agriculture (online). http://iccima.ir/

  2. Organisation for economic co-operation and development (OECD) (online). http://www.oecd.org/

  3. Ghobadian A, Stainer A, Kiss T (1993) A computerized vendor rating system. In: Proceedings of the first international symposium on logistics, pp 321–328

  4. Yücel A, Güneri AF (2011) A weighted additive fuzzy programming approach for multi-criteria supplier selection. Expert Syst Appl 38(5):6281–6286

    Article  Google Scholar 

  5. Chu W, Li Y, Liu C, Mou W, Tang L (2013) A manufacturing resource allocation method with knowledge-based fuzzy comprehensive evaluation for aircraft structural parts. Int J Prod Res 52(11):3239–3258

    Article  Google Scholar 

  6. Yin S (2014) Study on resources allocation between airworthiness authority and aviation industry. Proc Eng 80:668–676

    Article  Google Scholar 

  7. Rao RV (2006) A material selection model using graph theory and matrix approach. Mater Sci Eng A 431(1–2):248–255

    Article  Google Scholar 

  8. Darvish M, Yasaei M, Saeedi A (2009) Application of the graph theory and matrix methods to contractor ranking. Int J Proj Manag 27(6):610–619

    Article  Google Scholar 

  9. Chryssolouris G (2006) The design of manufacturing systems. In: Manufacturing systems: theory and practice, 2nd edn. Springer, New York, pp 329–463

    Google Scholar 

  10. Suh NP (2001) Axiomatic design: advances and applications. Oxford University Press, New York

    Google Scholar 

  11. Wang L, Bi ZM (2013) Challenges for better sustainable manufacturing. In: Azevedo A (ed) Advances in sustainable and competitive manufacturing systems, 1st edn. Springer International Publishing, Switzerland, pp 1209–1221

  12. Wu D, Rosen DW, Wang L, Schaefer D (2014) Cloud-based manufacturing: old wine in new bottles? Proc CIRP 17:94–99

    Article  Google Scholar 

  13. Hounshell D (1985) From the American system to mass production, 1800–1932: the development of manufacturing technology in the United States, vol 4. JHU Press, New York

    Google Scholar 

  14. Da Silveira G, Borenstein D, Fogliatto FS (2001) Mass customization: literature review and research directions. Int J Prod Econ 72(1):1–13

    Article  Google Scholar 

  15. Batchelor R (1994) Henry Ford, mass production, modernism, and design, vol 1. Manchester University Press, Manchester

    Google Scholar 

  16. Monden Y (1995) Toyota production system. J Oper Res Soc 46(5):669–670

    Google Scholar 

  17. Ohno T (1988) Toyota production system: beyond large-scale production. Productivity Press, New York

  18. Doolen TL, Hacker ME (2005) A review of lean assessment in organizations: an exploratory study of lean practices by electronics manufacturers. J Manuf Syst 24(1):55–67

    Article  Google Scholar 

  19. Vinodh S, Kumar Chintha S (2011) Application of fuzzy QFD for enabling leanness in a manufacturing organisation. Int J Prod Res 49(6):1627–1644

    Article  Google Scholar 

  20. Houborg C (2010) Implementing a successful lean program: where do you begin? Pharm Technol Eur 22(9):52–57

  21. Johnstone R, Kurtzhaltz JE (18 Sep 1984) Flexible manufacturing system. Google Patents

  22. Kostal P, Velisek K (2010) Flexible manufacturing system. World Acad Sci Eng Technol, ISSN, pp 825–829

  23. Talebanpour R, Javadi M (2015) Decision-making for flexible manufacturing systems using DEMATEL and SAW. Decis Sci Lett 4(3):363–372

    Article  Google Scholar 

  24. Koren Y, Ulsoy AG, Heisel U, Jovane F, Moriwaki T, Pritschow G, Ulsoy G, Van Brussel H (1999) Reconfigurable manufacturing systems. CIRP Ann Technol 48(2):527–540

    Article  Google Scholar 

  25. ElMaraghy HA (2005) Flexible and reconfigurable manufacturing systems paradigms. Int J Flex Manuf Syst 17(4):261–276

    Article  MATH  Google Scholar 

  26. Majija N, Mpofu K, Modungwa D (2013) Conceptual development of modular machine tools for reconfigurable manufacturing systems. In: Azevedo A (ed) Advances in sustainable and competitive manufacturing systems, 1st edn. Springer International Publishing, Switzerland, pp 467–477

  27. Goyal KK, Jain PK (2015) Design of reconfigurable flow lines using MOPSO and maximum deviation theory. Int J Adv Manuf Technol 84(5–8):1587–1600

  28. Koren Y, Shpitalni M (2010) Design of reconfigurable manufacturing systems. J Manuf Syst 29(4):130–141

    Article  Google Scholar 

  29. Talhi A, Huet JC, Fortineau V, Lamouri S (2015) Towards a cloud manufacturing systems modeling methodology. IFAC-PapersOnLine 48(3):288–293

    Article  Google Scholar 

  30. Schaefer D, Thames JL, Wellman RD, Wu D, Yim S, Rosen D (2012) Distributed collaborative design and manufacture in the cloud—motivation, infrastructure, and education. In: 2012 American society for engineering education annual conference, San Antonio, Paper# AC2012-3017

  31. Xu X (2012) From cloud computing to cloud manufacturing. Robot Comput Integr Manuf 28(1):75–86

    Article  Google Scholar 

  32. Wu D, Thames JL, Rosen DW, Schaefer D (2013) Enhancing the product realization process with cloud-based design and manufacturing systems. J Comput Inf Sci Eng 13(4):41004

    Article  Google Scholar 

  33. Baykasoglu A (2012) A review and analysis of ‘graph theoretical-matrix permanent’ approach to decision making with example applications. Artif Intell Rev 42(4):573–605

    Article  Google Scholar 

  34. Rao RV (2006) A decision-making framework model for evaluating flexible manufacturing systems using digraph and matrix methods. Int J Adv Manuf Technol 30(11–12):1101–1110

    Article  Google Scholar 

  35. Prabhakaran RTD, Babu BJ, Agrawal VP (2008) Quality evaluation of resin transfer molded products. J Reinf Plast Compos 27(6):559–581

  36. Raj T, Attri R (2010) Quantifying barriers to implementing total quality management (TQM). Eur J Ind Eng 4(3):308–335

    Article  Google Scholar 

  37. Wagner SM, Neshat N (2010) Assessing the vulnerability of supply chains using graph theory. Int J Prod Econ 126(1):121–129

    Article  Google Scholar 

  38. Upadhyay N, Agrawal VP (2007) Structural modeling and analysis of intelligent mobile learning environment a graph theoretic system approach. J Appl Quant Methods 2(2):226–248

    Google Scholar 

  39. Mohan M, Gandhi OP, Agrawal VP (2008) Real-time reliability index of a steam power plant: a systems approach. Proc Inst Mech Eng Part A J Power Energy 222(4):355–369

    Article  Google Scholar 

  40. Minc H (1978) Permanents. Addison-Wesley, Reading

    MATH  Google Scholar 

  41. Saaty TL (1988) What is the analytic hierarchy process?. Springer, Berlin Heidelberg

    Book  MATH  Google Scholar 

  42. Brans JP, Mareschal B, Vincke PH (1984) PROMETHEE: a new family of outranking methods in MCDM. In: International federation of operational research studies (IFORS 84), JP Brans, Amsterdam

  43. Tzeng G-H (2010) Multiple attribute decision making: methods and applications. CRC Press, Boca Raton

    MATH  Google Scholar 

  44. Roy B (1991) The outranking approach and the foundations of ELECTRE methods. Theory Decis 31(1):49–73

    Article  MathSciNet  Google Scholar 

  45. Lee AHI, Kang H-Y, Hsu C-F, Hung H-C (2009) A green supplier selection model for high-tech industry. Expert Syst Appl 36(4):7917–7927

    Article  Google Scholar 

  46. Sanam Electronic Company (online). www.sanamel.com

  47. Lee AHI, Kang HY, Lin CY, Wu HW (2013) Selecting candidate suppliers using a multiple criteria decision making model. Adv Mater Res 694–697:3472–3475

    Article  Google Scholar 

  48. Lee AHI, Kang HY, Chang CT (2009) Fuzzy multiple goal programming applied to TFT-LCD supplier selection by downstream manufacturers. Expert Syst Appl 36(3):6318–6325

    Article  Google Scholar 

  49. Wu B (2012) Manufacturing systems design and analysis. Springer, Netherlands

    Google Scholar 

  50. Ding G, Zeng L (2015) On the effect of measurement errors in regression-adjusted monitoring of multistage manufacturing processes. J Manuf Syst 36:263–273

    Article  Google Scholar 

  51. Borangiu T, Thomas A, Trentesaux D (2012) Service orientation in holonic and multi-agent manufacturing. Springer, Berlin Heidelberg

    Book  Google Scholar 

  52. Schaefer D (2014) Cloud-based design and manufacturing (CBDM). Springer, Switzerland

    Book  Google Scholar 

  53. Aghezzaf E-H, Khatab A, Le Tam P (2016) Optimizing production and imperfect preventive maintenance planning’s integration in failure-prone manufacturing systems. Reliab Eng Syst Saf 145:190–198

    Article  Google Scholar 

  54. Assid M, Gharbi A, Hajji A (2015) Production planning and opportunistic preventive maintenance for unreliable one-machine two-products manufacturing systems. IFAC-PapersOnLine 48(3):478–483

    Article  Google Scholar 

  55. Wang L (2013) Machine availability monitoring and machining process planning towards cloud manufacturing. CIRP J Manuf Sci Technol 6(4):263–273

    Article  Google Scholar 

  56. Ren L, Zhang L, Tao F, Zhao C, Chai X, Zhao X (2015) Cloud manufacturing: from concept to practice. Enterp Inf Syst 9(2):186–209

    Article  Google Scholar 

  57. Yurdakul M (2002) Measuring a manufacturing system’s performance using Saaty’s system with feedback approach. Integr Manuf Syst 13(1):25–34

    Article  Google Scholar 

  58. Cloutier R (2015) Becoming a global chief security executive officer: a how to guide for next generation security leaders. Elsevier Science, Amsterdam

    Google Scholar 

  59. LeRoy RS (2015) Hazard or risk analysis, overcoming the human factor. In: 2015 IEEE IAS Electrical Safety Workshop, Lakeland, Florida, pp 1–6

  60. Bhanot N, Rao PV, Deshmukh SG (2014) Sustainable manufacturing: an interaction analysis for machining parameters using graph theory. In: Proc SOM, pp 57–63

  61. Rohmert W (1985) Ergonomics and manufacturing industry. Ergonomics 28(8):1115–1134

    Article  Google Scholar 

  62. Wells LJ, Camelio JA, Williams CB, White J (2014) Cyber-physical security challenges in manufacturing systems. Manuf Lett 2(2):74–77

    Article  Google Scholar 

  63. McLean RRP, Padayachee J, Bright G (2015) A thin, hardware-supported middleware management system for reconfigurable manufacturing systems. S Afr J Ind Eng 26(1):207–223

    Google Scholar 

  64. Savino MM, Batbaatar E (2015) Investigating the resources for integrated management systems within resource-based and contingency perspective in manufacturing firms. J Clean Prod 104:392–402

    Article  Google Scholar 

  65. Grüner S, Weber P, Wagner C, Epple U (2015) Equipment interconnection models in discrete manufacturing. Math Model 8(1):928–929

    Google Scholar 

  66. Gahm C, Denz F, Dirr M, Tuma A (2016) Energy-efficient scheduling in manufacturing companies: a review and research framework. Eur J Oper Res 248(3):744–757

    Article  MathSciNet  MATH  Google Scholar 

  67. Urban TL, Chiang W-C (2016) Designing energy-efficient serial production lines: the unpaced synchronous line-balancing problem. Eur J Oper Res 248(3):789–801

    Article  MathSciNet  MATH  Google Scholar 

  68. Bratti M, Leombruni R (2014) Local human capital externalities and wages at the firm level: evidence from Italian manufacturing. Econ Educ Rev 41:161–175

    Article  Google Scholar 

  69. Winters JV (2014) STEM graduates, human capital externalities, and wages in the US. Reg Sci Urban Econ 48:190–198

    Article  Google Scholar 

  70. Zhang Y, Wang G, Li N, Wang X (2015) Online estimation and compensation method of installation error for rotating-modulated north seeker. Opt Int J Light Electron Opt 126(19):1773–1777

    Article  Google Scholar 

  71. Sagawa JK, Nagano MS (2015) Modeling the dynamics of a multi-product manufacturing system: a real case application. Eur J Oper Res 244(2):624–636

    Article  MathSciNet  MATH  Google Scholar 

  72. Wang H, Zhu X, Wang H, Hu SJ, Lin Z, Chen G (2011) Multi-objective optimization of product variety and manufacturing complexity in mixed-model assembly systems. J Manuf Syst 30(1):16–27

    Article  Google Scholar 

  73. Dombrowski U, Mielke T, Schulze S (2010) Structural analysis of approaches for worker participation. In: 2010 IEEE international conference on industrial engineering and engineering management (IEEM), pp 512–516

  74. Seppälä P, Klemola S (2004) How do employees perceive their organization and job when companies adopt principles of lean production? Hum Factors Ergon Manuf Serv Ind 14(2):157–180

    Article  Google Scholar 

  75. von Tunzelmann GN (1995) Time-saving technical change: the cotton industry in the english industrial revolution. Explor Econ Hist 32(1):1–27

    Article  Google Scholar 

  76. Li W, Dai H, Zhang D (2015) The relationship between maximum completion time and total completion time in flowshop production. Proc Manuf 1:146–156

    Google Scholar 

  77. Ebrahim Z, Rasib A, Hamzah A, Muhamad MR (2015) Understanding time loss in manufacturing operations. Appl Mech Mater 761:619–623

    Article  Google Scholar 

  78. Schonberger R (1982) Japanese manufacturing techniques: nine hidden lessons in simplicity. Simon and Schuster, New York

    Google Scholar 

  79. Covanich W, McFarlane D (2009) Assessing ease of reconfiguration of conventional and Holonic manufacturing systems: approach and case study. Eng Appl Artif Intell 22(7):1015–1024

    Article  Google Scholar 

  80. Covanich W, McFarlane D, Farid AM (2008) Guidelines for evaluating the ease of reconfiguration of manufacturing systems. In: 6th IEEE international conference on industrial informatics, 2008, INDIN 2008, pp 1214–1219

  81. Valilai OF, Houshmand M (2013) A collaborative and integrated platform to support distributed manufacturing system using a service-oriented approach based on cloud computing paradigm. Robot Comput Integr Manuf 29(1):110–127

    Article  Google Scholar 

  82. Junior OC, Favaretto F, Young RIM (2005) Information sharing using features technology to support multiple viewpoint design for manufacture. Prod Prod 8(2):75–86

    Google Scholar 

  83. Garg RKK, Agrawal VPP, Gupta VKK (2006) Selection of power plants by evaluation and comparison using graph theoretical methodology. Int J Electr Power Energy Syst 28(6):429–435

    Article  Google Scholar 

  84. Nourani Y, Andresen B (1999) Exploration of NP-hard enumeration problems by simulated annealing—the spectrum values of permanents. Theor Comput Sci 215(1–2):51–68

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Industrial Engineering School, University of Tehran, Tehran, Iran

    Alireza Hakimi-Asl, Mohsen Sadegh Amalnick & Mehdi Hakimi-Asl

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  1. Alireza Hakimi-Asl
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  2. Mohsen Sadegh Amalnick
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  3. Mehdi Hakimi-Asl
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Correspondence to Mohsen Sadegh Amalnick.

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Hakimi-Asl, A., Amalnick, M.S. & Hakimi-Asl, M. Proposing a graph ranking method for manufacturing system selection in high-tech industries. Neural Comput & Applic 29, 133–142 (2018). https://doi.org/10.1007/s00521-016-2420-7

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