Skip to main content
SpringerLink
Log in

A Rough Decision-Making Model for Biomaterial Selection

  • Chapter
  • First Online:
Biomaterials in Orthopaedics and Bone Regeneration

Part of the book series: Materials Horizons: From Nature to Nanomaterials ((MHFNN))

Abstract

Biomaterials are artificial or natural materials that substitute the impaired organic parts to fulfill the global medical requirements for improving resilience and quality of human life. Thus, it has become indispensable to select the most appropriate materials for various biomedical applications. In this chapter, a decision-making model integrating analytic hierarchy process (AHP) as well as combinative distance-based assessment (CODAS) techniques based on rough numbers has been developed for determining the performance scores of different biomaterials for a hip joint prosthesis application. Sensitivity analyses and comparative result analysis show an excellent performance of the integrated model against with respect to some well-established decision-making methods in terms of derived ranking patterns.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Mehrali M, Thakur A, Pennisi CP, Talebian S, Arpanaei A, Nikkhah M, Dolatshahi-Pirouz A (2017) Nanoreinforced hydrogels for tissue engineering: biomaterials that are compatible with load-bearing and electroactive tissues. Adv Mater 29(8):1–26

    Article  CAS  Google Scholar 

  2. Huebsch N, Mooney DJ (2009) Inspiration and application in the evolution of biomaterials. Nature 462:426–432

    Article  CAS  Google Scholar 

  3. Jahan A (2012) Material selection in biomedical applications: comparing the comprehensive VIKOR and goal programming models. Int J Mater Struct Integrity 6(2–4):230–240

    Article  Google Scholar 

  4. Walker J, Shadanbaz S, Woodfield TB, Staiger MP, Dias GJ (2014) Magnesium biomaterials for orthopedic application: a review from a biological perspective. J Biomed Mater Res B Appl Biomater 102(6):1316–1331

    Article  CAS  Google Scholar 

  5. Sridharan R, Cameron AR, Kelly DJ, Kearney CJ, O’Brien FJ (2015) Biomaterial based modulation of macrophage polarization: a review and suggested design principles. Mater Today 18(6):313–325

    Article  CAS  Google Scholar 

  6. Chiti MC, Dolmans MM, Donnez J, Amorim CA (2017) Fibrin in reproductive tissue engineering: a review on its application as a biomaterial for fertility preservation. Ann Biomed Eng 45(7):1650–1663

    Article  CAS  Google Scholar 

  7. Aherwar A, Singh AK, Patnaik A (2016) Current and future biocompatibility aspects of biomaterials for hip prosthesis. AIMS Bioeng 3(1):23–43

    Article  CAS  Google Scholar 

  8. Bahraminasab M, Sahari BB (2013) NiTi shape memory alloys, promising materials in orthopedic applications. In: Shape memory alloys-processing, characterization and applications, InTech, USA, pp 261–278

    Google Scholar 

  9. Aherwar A, Singh A, Patnaik A, Unune D (2018). Selection of molybdenum-filled hip implant material using grey relational analysis method. In: Handbook of research on emergent applications of optimization algorithms, IGI Global, USA, pp 675–692

    Google Scholar 

  10. Hafezalkotob A, Hafezalkotob A (2017) Interval MULTIMOORA method with target values of attributes based on interval distance and preference degree: biomaterials selection. J Ind Eng Int 13(2):181–198

    Article  Google Scholar 

  11. Farag MM (2013) Materials and process selection for engineering design. CRC Press, USA

    Book  Google Scholar 

  12. Mousavi-Nasab SH, Sotoudeh-Anvari A (2017) A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Mater Des 121:237–253

    Article  Google Scholar 

  13. Panchal D, Singh AK, Chatterjee P, Zavadskas EK, Ghorabaee MK (2019) A new fuzzy methodology-based structured framework for RAM and risk analysis. Appl Soft Comput 74:242–254

    Article  Google Scholar 

  14. Yazdani M, Chatterjee P, Zavadskas EK, Streimikiene D (2018) A novel integrated decision-making approach for evaluation and selection of renewable energy technologies. Clean Technol Environ Policy 20(2):403–420

    Article  Google Scholar 

  15. Tavana M, Yazdani M, Di Caprio D (2017) An application of an integrated ANP–QFD framework for sustainable supplier selection. Int J Logistics Res Appl 20(3):254–275

    Article  Google Scholar 

  16. Chatterjee P, Chakraborty S (2017) Development of a meta-model for determination of technological value of cotton fiber using design of experiments and TOPSIS method. J Nat Fibers 15(6)

    Google Scholar 

  17. Chatterjee P, Chakraborty S (2017) A developed meta-model for selection of cotton fabrics using design of experiments and TOPSIS method. J Inst Eng (India): Ser E 98(2):79–90

    Google Scholar 

  18. Chakraborty S, Chatterjee P, Prasad K (2018) An integrated DEMATEL-VIKOR method-based approach for cotton fibre selection and evaluation. J Inst Eng (India): Ser E 99(1):63–73

    Article  CAS  Google Scholar 

  19. Chatterjee P, Chakraborty S (2016) A comparative analysis of VIKOR method and its variants. Decis Sci Lett 5(4):469–486

    Article  Google Scholar 

  20. Ranjan R, Chatterjee P, Chakraborty S (2016) Performance evaluation of Indian railway zones using DEMATEL and VIKOR methods. Benchmarking: an Int J 23(1):78–95

    Article  Google Scholar 

  21. Chatterjee P, Athawale VM, Chakraborty S (2010) Selection of industrial robots using compromise ranking and outranking methods. Robot Comput Integr Manuf 26(5):483–489

    Article  Google Scholar 

  22. Kaklauskas A, Zavadskas EK, Raslanas S, Ginevicius R, Komka A, Malinauskas P (2006) Selection of low-e windows in retrofit of public buildings by applying multiple criteria method COPRAS: a Lithuanian case. Energy Build 38(5):454–462

    Article  Google Scholar 

  23. Yazdani M, Chatterjee P, Zavadskas EK, Zolfani SH (2017) Integrated QFD-MCDM framework for green supplier selection. J Clean Prod 142(4):3728–3740

    Article  Google Scholar 

  24. Maity SR, Chatterjee P, Chakraborty S (2012) Cutting tool material selection using grey complex proportional assessment method. Mater Des 36:372–378

    Article  CAS  Google Scholar 

  25. Chatterjee P, Chakraborty S (2012) Materials selection using COPRAS and COPRAS-G methods. Int J Mater Struct Integrity 6(2/3/4):111–133

    Article  Google Scholar 

  26. Chatterjee P, Athawale VM, Chakraborty S (2011) Materials selection using complex proportional assessment and evaluation of mixed data methods. Mater Des 32(2):851–860

    Article  Google Scholar 

  27. Stanujkić D, Đorđević B, Đorđević M (2013) Comparative analysis of some prominent MCDM methods: a case of ranking Serbian banks. Serb J Manage 8(2):213–241

    Article  Google Scholar 

  28. Ghorabaee MK, Zavadskas EK, Olfat L, Turskis Z (2015) Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica 26(3):435–451

    Article  Google Scholar 

  29. Chatterjee P, Banerjee A, Mondal S, Boral S, Chakraborty S (2018) Development of a hybrid meta-model for materials selection using design of experiments and EDAS method. Eng Trans 66(2):187–207

    Google Scholar 

  30. Ghorabaee MK, Zavadskas EK, Turskis Z, Antucheviciene J (2016) A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Econ Comput Econ Cybern Stud Res 50(3):25–44

    Google Scholar 

  31. Panchal D, Chatterjee P, Shukla RK, Choudhury T, Tamosaitiene J (2017) Integrated fuzzy AHP-CODAS framework for maintenance decision in urea fertilizer industry. Econ Comput Econ Cybern Stud Res 51(3):179–196

    Google Scholar 

  32. Bahraminasab M, Jahan A (2011) Material selection for femoral component of total knee replacement using comprehensive VIKOR. Mater Des 32(8):4471–4477

    Article  CAS  Google Scholar 

  33. Jahan A, Edwards KL (2013) Weighting of dependent and target-based criteria for optimal decision-making in materials selection process: biomedical applications. Mater Des 49:1000–1008

    Article  CAS  Google Scholar 

  34. Bahraminasab M, Sahari BB, Edwards KL, Farahmand F, Hong TS, Arumugam M, Jahan A (2014) Multi-objective design optimization of functionally graded material for the femoral component of a total knee replacement. Mater Des 53:159–173

    Article  CAS  Google Scholar 

  35. Petković D, Madić M, Radenković G, Manić M, Trajanović M (2015) Decision support system for selection of the most suitable biomedical material. In: Proceedings of 5th international conference on information society and technology, Serbia, pp 27–31

    Google Scholar 

  36. Hafezalkotob A, Hafezalkotob A (2015) Comprehensive MULTIMOORA method with target based attributes and integrated significant coefficients for materials selection in biomedical applications. Mater Des 87:949–959

    Article  CAS  Google Scholar 

  37. Chowdary Y, Sai Ram V, Nikhil EVS, Vamsi Krishna PNS, Nagaraju D (2016) Evaluation and prioritizing of biomaterials for the application of implantation in human body using fuzzy AHP AND TOPSIS. Int J Control Theory Appl 9(40):527–533

    Google Scholar 

  38. Kabir G, Lizu A (2016) Material selection for femoral component of total knee replacement integrating fuzzy AHP with PROMETHEE. J Intell Fuzzy Syst 30(6):3481–3493

    Article  Google Scholar 

  39. Abd K, Hussein A, Ghafil A (2016) An intelligent approach for material selection of sensitive components based on fuzzy TOPSIS and sensitivity analysis. In: Proceedings of academics world 30th international conference, Australia, pp 13–18

    Google Scholar 

  40. Ristić M, Manić M, Mišić D, Kosanović M, Mitković M (2017) Implant material selection using expert system. Facta Univ, Ser: Mech Eng 15(1):133–144

    Google Scholar 

  41. Pamucar D, Lj Gigovic, Bajic Z, Janosevic M (2017) Location selection for wind farms using GIS multi-criteria hybrid model: an approach based on fuzzy and rough numbers. Sustainability 9(8):1–24

    Article  Google Scholar 

  42. Song W, Ming X, Wu Z, Zhu B (2014) A rough TOPSIS approach for failure mode and effects analysis in uncertain environments. Qual Reliab Eng Int 30(4):473–486

    Article  Google Scholar 

  43. Pamucar D, Mihajlovic M, Obradovic R, Atanaskovic P (2017) Novel approach to group multi-criteria decision making based on interval rough numbers: hybrid DEMATEL-ANP-MAIRCA model. Expert Syst Appl 88:58–80

    Article  Google Scholar 

  44. Saaty TL, Vargas LG (2012) Models, methods, concepts and applications of the analytic hierarchy process. Springer Science and Business Media, pp 175

    Google Scholar 

  45. Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15(3):234–281

    Article  Google Scholar 

  46. Saaty TL, Tran LT (2007) On the invalidity of fuzzifying numerical judgments in the analytic Hierarchy process. Math Comput Model 46(7):962–975

    Article  Google Scholar 

  47. Stevic Z, Pamucar D, Vasiljevic M, Stojic G, Korica S (2017) Novel integrated multi-criteria model for supplier selection: case study construction company. Symmetry 9(11):1–34

    Google Scholar 

  48. Stevic Z, Pamucar D, Zavadskas EK, Cirovic G, Prentkovskis O (2017) The selection of wagons for the internal transport of a logistics company: a novel approach based on rough BWM and rough SAW methods. Symmetry 9(11):1–25

    Google Scholar 

  49. Ghorabaee MK, Amiri M, Zavadskas EK, Hooshmand R, Antuchevičienė J (2017) Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. J Bus Econ Manage 18(1):1–19

    Article  Google Scholar 

  50. Jahan A, Mustapha F, Md YI, Sapuan SM, Bahraminasab M (2011) A comprehensive VIKOR method for material selection. Mater Des 32(3):1215–1221

    Article  CAS  Google Scholar 

  51. Pamucar D, Ćirović G (2015) The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation Area Comparison (MABAC). Expert Syst Appl 42:3016–3028

    Article  Google Scholar 

  52. Badi IA, Abdulshahed AM, Shetwan AG (2018) A case study of supplier selection for a steelmaking company in Libya by using the Combinative Distance-based ASsessment (CODAS) model. Decis Mak: Appl Manage Eng 1(1):1–12

    Google Scholar 

  53. Mukhametzyanov I, Pamucar D (2018) A sensitivity analysis in MCDM problems: a statistical approach. Decis Mak: Appl Manage Eng 1(2):51–80

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Logistics, Military Academy, University of Defence in Belgrade, Pavla Jurisica Sturma 33, Belgrade, Serbia

    Dragan Pamucar

  2. Department of Mechanical Engineering, MCKV Institute of Engineering, Howrah, West Bengal, India

    Prasenjit Chatterjee

  3. Department of Management, Universidad Loyola Andalucía, Andalucía, Spain

    Morteza Yazdani

  4. Department of Production Engineering, Jadavpur University, Kolkata, West Bengal, India

    Shankar Chakraborty

Authors
  1. Dragan Pamucar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prasenjit Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Morteza Yazdani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shankar Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prasenjit Chatterjee .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Beant College of Engineering and Technology, Gurdaspur, Punjab, India

    Preetkanwal Singh Bains

  2. Department of Mechanical Engineering, Beant College of Engineering and Technology, Gurdaspur, Punjab, India

    Sarabjeet Singh Sidhu

  3. Department of Tissue Engineering and Applied Cell Sciences, School of Medicine, Nervous System Stem Cells Research Center, Semnan University of Medical Sciences, Semnan, Iran

    Marjan Bahraminasab

  4. School of Mechanical Engineering, Lovely Professional University, Jalandhar, Punjab, India

    Chander Prakash

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Pamucar, D., Chatterjee, P., Yazdani, M., Chakraborty, S. (2019). A Rough Decision-Making Model for Biomaterial Selection. In: Bains, P., Sidhu, S., Bahraminasab, M., Prakash, C. (eds) Biomaterials in Orthopaedics and Bone Regeneration . Materials Horizons: From Nature to Nanomaterials. Springer, Singapore. https://doi.org/10.1007/978-981-13-9977-0_15

Download citation

Publish with us

Policies and ethics

Search

Navigation