An intuitionistic fuzzy OWA-TOPSIS method for collaborative network formation considering matching characteristics

Document Type : Article

Authors

1 College of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, P.R. China

2 College of Energy Engineering, Zhejiang University, Hangzhou 310027, P.R. China

Abstract

Collaborative network (CN) as a new emerging paradigm can rapidly answer market demands by effective enterprise collaboration and coordination. Nowadays, it has become a potential solution for different organizations to manage their businesses effectively. Thus, selecting a suitable partner combination is critical to CN success. Matching characteristic is very important for partner combination selection in the CN formation, while it is neglected in the existing research. This paper is to propose a method and model for partner combination selection of CN considering matching utility. Firstly, the matching factors are developed from four aspects, supply capability, goal, culture and technology. And then a hybrid approach is designed to integrate intuitionistic fuzzy Ordered Weighted Averaging (IFOWA) operators into the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) procedure. And matching utility combination method amongst multi-partners is advanced to establish the partner combination model. Moreover, a decision support system is applied in a practical enterprise to illustrate the advantage of the proposed method. Finally a sensitivity analysis is conducted to investigate the robustness of solutions ranking to changes in matching factor. The result shows that ranking the solutions for forming CN is relatively sensitive to its matching factor.

Keywords

Main Subjects

References

References
1. Cao, M. and Zhang, Q. \Supply chain collaborative
advantage: A rm's perspective", Int. J. Prod. Econ.,
128(1), pp. 358-367 (2010).
2. Lopez-Ortega, O. and Ramrez-Hernandez, M. \A
formal framework to integrate express data models
in an extended enterprise context", J. Intell. Manuf.,
18(3), pp. 371-381 (2007).
3. Liu, P., Raahemi, B., and Benyoucef, M. \Knowledge
sharing in dynamic virtual enterprises: A sociotechnological
perspective", Knowl.-Based Syst., 24(3),
pp. 427-443 (2011).
4. Li, Y. and Liao, X. \Decision support for risk analysis
on dynamic alliance", Decis. Support Syst., 42(4), pp.
2043-2059 (2007).
5. Camarinha-Matos, L.M. and Afsarmanesh, H. \Collaborative
networks A new scienti c discipline", J.
Intell. Manuf., 16(4-5), pp. 439-452 (2005).
6. Camarinha-Matos, L.M. and Afsarmanesh, H. \A
comprehensive modeling framework for collaborative
networked organizations", J. Intell. Manuf., 18(5), pp.
529-542 (2007).
7. Chituc, C. and Nof, S. \The join/leave/remain (JLR)
decision in collaborative networked organizations",
Comput. Ind. Eng., 53(1), pp. 173-195 (2007).
8. Camarinha-Matos, L.M. and Afsarmanesh, H. \Collaborative
networks-value creation in a knowledge
society", Knowl. Enterp., IFIP, 207, pp. 26-40 (2006).
9. Camarinha-Matos, L.M. \Collaborative networked organizations:
Status and trends in manufacturing",
Annu. Rev. Control, 33(2), pp. 199-208 (2009).
10. Wu, C. and Barnes, D. \Formulating partner selection
criteria for agile supply chains: A Dempster-Shafer
belief acceptability optimisation approach", Int. J.
Prod. Econ., 125(2), pp. 284-293 (2010).
11. Rezaei, J. \A two-way approach to supply chain
partner selection", Int. J. Prod. Res., 53(16), pp. 4888-
4902 (2015).
12. Awasthi, A., Adetiloye, T., and Crainic, T.G. \Collaboration
partner selection for city logistics planning under
municipal freight regulations", Appl. Math. Model.,
40(1), pp. 510-525 (2016).
13. Mat, N.A.C. and Cheung, Y. \Partner selection
criteria for successful collaborative network", 20th
Aust.Conf. on Inform. Syst., Melbourne, Australia,
pp. 631-641 (2009).
14. Feng, B., Fan, Z., and Ma, J. \A method for partner selection
of codevelopment alliances using individual and
collaborative utilities", Int. J. Prod. Econ., 124(1), pp.
159-170 (2010).
15. Dymova, L., Sevastjanov, P., and Tikhonenko, A. \An
interval type-2 fuzzy extension of the TOPSIS method
using alpha cuts", Knowl.-Based Syst., 83, pp. 116-127
(2015).
16. Zhang, X.L. and Xu, Z.S. \Hesitant fuzzy QUALIFLEX
approach with a signed distance-based comparison
method for multiple criteria decision analysis",
Expert Syst. Appl., 42(2), pp. 873-884 (2015).
17. Rodriguez, R.M., Martinez, L., Torra, V., Xu, Z.S.,
and Herrera, F. \Hesitant fuzzy sets: state of the art
and future directions", Int. J. Intell. Syst., 29(6), pp.
495-524 (2014).
18. Wei, C.P., Ren, Z.L., and Rodriguez, R.M. \A hesitant
fuzzy linguistic TODIM method based on a score
function", Int. J. Comput. Int. Sys., 8(4), pp. 701-712
(2015).
19. Wang, H. and Xu, Z.S. \Multi-groups decision making
using intuitionistic-valued hesitant fuzzy information",
Int. J. Comput. Int. Sys., 9(3), pp. 468-482 (2016).
20. Xu, Z.S. and Liao, H.C. \Intuitionistic fuzzy analytic
hierarchy process", IEEE T. Fuzzy Syst., 22(4), pp.
749-761 (2014).
21. Boran, F.E., Genc, S., Kurt, M., and Akay, D.
\A multi-criteria intuitionistic fuzzy group decision
making for supplier selection with TOPSIS method",
Expert Syst. Appl., 36(8), pp. 11363-11368 (2009).
22. Zadeh, L.A. \Fuzzy sets", Inform. Control, 8(3) pp.
338-353 (1965).
23. Wang, H. and Xu, Z. \Admissible orders of typical hesitant
fuzzy elements and their application in ordered
information fusion in multi-criteria decision making",
Inform. Fusion, 29, pp. 98-104 (2016).
1686 T. Wang et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 1671{1687
24. Khatibi, V. and Montazer, G.A. \Intuitionistic fuzzy
set vs. fuzzy set application in medical pattern recognition",
Arti cial Intelligence in Medicine, 47(1) pp.
43-52 (2009).
25. Buyukozkan, G. and Guleryuz, S. \A new integrated
intuitionistic fuzzy group decision making approach for
product development partner selection", Comput. Ind.
Eng., 102, pp. 383-395 (2016).
26. Atanassov, K.T. \Intuitionistic fuzzy sets", Fuzzy Set
Syst., 20(1), pp. 87-96 (1986).
27. Xu, Z. and Yager, R.R. \Some geometric aggregation
operators based on intuitionistic fuzzy sets", Int. J.
Gen. Syst., 35(4), pp. 417-433 (2006).
28. Xu, Z. \Intuitionistic fuzzy aggregation operators",
IEEE T. Fuzzy Syst., 15(6), pp. 1179-1187 (2007).
29. Szmidt, E. and Kacprzyk, J. \Distances between
intuitionistic fuzzy sets", Fuzzy Set Syst., 114(3), pp.
505-518 (2000).
30. Yager, R.R. \On ordered weighted averaging aggregation
operators in multicriteria decision making", IEEE
T. Syst., Man Cyb., 18(1), pp. 183-190 (1988).
31. Hwang, C.L. and Yoon, K., Multiple Attribute Decision
Making: Methods and Applications: a State-of-the-Art
Survey, Springer-Verlag New York (1981).
32. Gerogiannis, V., Fitsilis, P., and Kameas, A. \Using
a combined intuitionistic fuzzy set-topsis method for
evaluating project and portfolio management information
systems", In Arti cial Intelligence Applications
and Innovations, pp. 67-81, Springer Berlin Heidelberg
(2011).
33. Camarinha-Matos, L.M., Afsarmanesh, H., Galeano,
N., and Molina, A. \Collaborative networked organizations
-concepts and practice in manufacturing enterprises",
Comput. Ind. Eng., 57(1), pp. 46-60 (2009).
34. Shuman, J. and Twombly, J., Collaborative Network
Management - An Emerging Role for Alliance Management,
The Rythm of Business (2008).
35. Wang, T., Guo, S., Sarker, B.R., and Li, Y. \Process
planning for collaborative product development with
CD-DSM in optoelectronic enterprises", Adv. Eng.
Inform., 26(2), pp. 280-291 (2012).
36. Jun, H.B. and Suh, H.W. \A modeling framework for
product development process considering its characteristics",
IEEE Trans. Eng. Manage., 55(1), pp. 103-119
(2008).
37. Sandy, D.J. \Pie-expansion e orts: collaboration processes
in buyer-supplier relationships", J. Marketing
Res., 36(4), pp. 461-475 (1999).
38. Chen, Y., Li, K.W., and Liu, S.-F. \An OWA-TOPSIS
method for multiple criteria decision analysis", Expert
Syst. Appl., 38(5), pp. 5205-5211 (2011).
39. Wang, T., Liu, J., Li, J., and Niu, C. \An integrating
OWA-TOPSIS framework in intuitionistic fuzzy settings
for multiple attribute decision making", Comput,
Ind. Eng., 98, pp. 185-194 (2016).
40. Herreraa, F. and Herrera-Viedmaa, E. \A fusion approach
for managing multi-granularity linguistic term
sets in decision making", Fuzzy Set Syst., 114(1), pp.
43-58 (2000).
41. Lin, W.-L., Lo, C.-C., Chao, K.-M., and Younas,
M. \Consumer-centric QoS-aware selection of web
services", J. Compu.t Syst. Sci., 74(2), pp. 211-231
(2008).
42. Tao, F., Zhao, D., and Zhang, L. \Resource service
optimal-selection based on intuitionistic fuzzy set and
non-functionality QoS in manufacturing grid system",
Knowl. Inf. Syst., 25(1), pp. 185-208 (2010).
43. Ma, J., Lu, J., and Zhang, G. \Decider: A fuzzy multicriteria
group decision support system", Knowl.-Based
Syst., 23(1), pp. 23-31 (2010).
44. Patil, S.K. and Kant, R. \A fuzzy AHP-TOPSIS
framework for ranking the solutions of knowledge
management adoption in supply chain to overcome
its barriers", Expert Syst. Appl., 41(2), pp. 679-693
(2014).

Scientia Iranica
Volume 25, Issue 3
Transactions on Industrial Engineering (E)
May and June 2018
Pages 1671-1687

Files

History

  • Receive Date: 08 August 2015
  • Revise Date: 12 September 2016
  • Accept Date: 20 February 2017

Share

How to cite

Statistics