Evaluation of Supply Chain of a Shipping Company in Iran by a Fuzzy Relational Network Data Envelopment Analysis Model

Document Type : Article

Authors

1 Faculty of Industrial Engineering, Urmia University of Technology, P.O. Box 57155-419, Band street, Urmia, Iran

2 School of Industrial Engineering and Research Institute of Energy Management and Planning, College of Engineering, University of Tehran, ‎ P.O. Box: 515-14395 after Jalal Ale Ahmad - Tehran North Kargar - Tehran – Iran

Abstract

The existing relational network data envelopment analysis (DEA) models evaluate the performance of decision making units (DMUs) with precise data. Whereas in the real world applications, there are many supply chain (SC) networks with imprecise and vague figures. This paper develops a relational network DEA model for evaluating the performance of supply chains with fuzzy numbers. The proposed fuzzy model is capable of evaluating the performance of all kinds of network structures. A pair of two-level mathematical program is utilized to convert the fuzzy relational network DEA to a conventional crisp one. For this purpose, the upper and lower bounds of the efficiencies are calculated by α-cut concept. The proposed model is implemented using actual data from the supply chain of an international shipping company in Iran.

Keywords

Main Subjects


References

1. Consulting, D., Energizing the Supply Chain: Trends and Issues in Supply Chain management, New York,NY (1999).

2. Xiao, T. and Yang, D. Price and service competition of supply chains with risk-averse retailers under demand uncertainty", Int. J. Prod. Econ., 114(1), pp.
187-200 (2008).
3. Bhaskar, V. and Lallement, P. Modeling a supply
chain using a network of queues", Appl. Math. Model.,
34(8), pp. 2074-2088 (2010).
4. Ross, A. and Droge, C. An integrated benchmarking
approach to distribution center performance using
DEA modeling", J. of Oper. Man., 20(1), pp. 19-32
(2002).
5. Easton, L., Murphy, D.J. and Pearson, J.N. Purchasing
performance evaluation: with data envelopment
analysis", Eur. J. of Purch. & Supply Man., 8(3), pp.
123-134 (2002).
6. Talluri, S., Narasimhan, R., and Nair, A. Vendor
performance with supply risk: A chance-constrained
DEA approach", Int. J. Prod. Econ., 100(2,) pp. 212-
222 (2006).
7. Kao, C. and Hwang, S.-N. Eciency measurement
for network systems: IT impact on rm performance",
Decis. Support Syst., 48(3), pp. 437-446 (2010).
8. Kao, C. and Liu, S.-T. Eciencies of two-stage
systems with fuzzy data", Fuzzy Set Syst., 176(1), pp.
20-35 (2011).
9. Kao, C. and Lin, P.-H. Eciency of parallel production
systems with fuzzy data", Fuzzy Set Syst., 198(0),
pp. 83-98 (2012).
H. Omrani et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 868{890 889
10. Charnes, A., Cooper, W.W., and Rhodes, E. Measuring
the eciency of decision making units", Eur. J.
Oper. Res., 2(6), pp. 429-444 (1978).
11. Lewis, H.F. and Sexton, T.R. Network DEA: e-
ciency analysis of organizations with complex internal
structure", Comput. Oper. Res., 31(9), pp. 1365-1410
(2004).
12. Seiford, L.M. and Zhu, J. Pro tability and marketability
of the top 55 U.S. commercial banks", Man.
Sci., 45(9) pp. 1270-1288 (1999).
13. Sexton, T. and Lewis, H. Two-Stage DEA: An application
to major league baseball", J. Prod. Anal.,
19(2-3), pp. 227-249 (2003).
14. Zhu, J. Multi-factor performance measure model with
an application to Fortune 500 companies", Eur. J.
Oper. Res., 123(1), pp. 105-124 (2000).
15. Fare, R. and Grosskopf, S. Network DEA", Socio.
Econ. Plann. Sci., 34(1), pp. 35-49 (2000).
16. Kao, C. and Hwang, S.-N. Eciency decomposition in
two-stage data envelopment analysis: An application
to non-life insurance companies in Taiwan", Eur. J.
Oper. Res., 185(1), pp. 418-429 (2008).
17. Kao, C. Eciency decomposition in network data
envelopment analysis: A relational model", Eur. J.
Oper. Res., 192(3), pp. 949-962 (2009).
18. Kao, C. Dynamic data envelopment analysis: A
relational analysis", Eur. J. Oper. Res., 227(2), pp.
325-330 (2013).
19. Guan, J. and Chen, K. Measuring the innovation
production process: A cross-region empirical study of
China's high-tech innovations", Technovation, 30(5),
pp. 348-358 (2010).
20. Yang, C. and Liu, H.-M. Managerial eciency in
Taiwan bank branches: A network DEA", Econ.
Model., 29(2), pp. 450-461 (2012).
21. Hsieh, L.-F. and Lin, L.-H. A performance evaluation
model for international tourist hotels in Taiwan-An
application of the relational network DEA", Int. J.
Hosp. Manag., 29(1), pp. 14-24 (2010).
22. Chen, C. and Yan, H. Network DEA model for supply
chain performance evaluation", Eur. J. Oper. Res.,
213(1), pp. 147-155 (2011).
23. Toloo, M., Emrouznejad, A., and Moreno, P. A linear
relational DEA model to evaluate two-stage processes
with shared inputs", J. Comput. Appl. Math., pp. 1-17
(2015).
24. Hatami-Marbini, A., Emrouznejad, A., and Tavana,
M. A taxonomy and review of the fuzzy data envelopment
analysis literature: Two decades in the making",
Eur. J. Oper. Res., 214(3), pp. 457-472 (2011).
25. Zadeh, L.A. Fuzzy sets", Inf. Control, 8(3), pp. 338-
353 (1965).
26. Karsak, E.E. Using data envelopment analysis for
evaluating
exible manufacturing systems in the presence
of imprecise data", Int. J. Adv. Manuf. Tech.,
35(9-10), pp. 867-874 (2008).
27. Sengupta, J.K. A fuzzy systems approach in data
envelopment analysis", Comput. Math. Appl., 24(8-9),
pp. 259-266 (1992).
28. Triantis, K. and Girod, O. A mathematical programming
approach for measuring technical eciency in a
fuzzy environment", J. Prod. Anal., 10(1), pp. 85-102
(1998).
29. Chiang, T.-A. and Che, Z.H. A fuzzy robust evaluation
model for selecting and ranking NPD projects
using Bayesian belief network and weight-restricted
DEA", Expert Syst. Appl., 37(11), pp. 7408-7418
(2010).
30. Azadeh, A. and Alem, S.M. A
exible deterministic,
stochastic and fuzzy Data Envelopment Analysis
approach for supply chain risk and vendor selection
problem: Simulation analysis", Expert Syst. Appl.,
37(12), pp. 7438-7448 (2010).
31. Juan, Y.-K. A hybrid approach using data envelopment
analysis and case-based reasoning for housing
refurbishment contractors selection and performance
improvement", Expert Syst. Appl., 36(3, Part 1), pp.
5702-5710 (2009).
32. Bagherzadeh Valami, H. Cost eciency with triangular
fuzzy number input prices: An application of
DEA", Chaos Soliton Fract., 42(3), pp. 1631-1637
(2009).
33. Wen, M. and Li, H. Fuzzy data envelopment analysis
(DEA): Model and ranking method", J. Comput. Appl.
Math., 223(2), pp. 872-878 (2009).
34. Wen, M., You, C., and Kang, R. A new ranking
method to fuzzy data envelopment analysis", Com.
Math. Appli., 59(11), pp. 3398-3404 (2010).
35. Lozano, S. and Moreno, P. Network fuzzy data envelopment
analysis", Per. Measur. Fuzzy Data Envelop.
Anal., Springer, pp. 207-230 (2014).
36. Jitsuzumi, T. and Nakamura, A. Causes of ine-
ciency in Japanese railways: Application of DEA for
managers and policymakers", Socio. Econ. Plann. Sci.,
44(3), pp. 161-173 (2010).
37. Azadeh, A., Ghaderi, S.F., and Izadbakhsh, H. Integration
of DEA and AHP with computer simulation for
railway system improvement and optimization", Appl.
Math. Com., 195(2), pp. 775-785 (2008).
38. Scheraga, C.A. Operational eciency versus nancial
mobility in the global airline industry: a data envelopment
and Tobit analysis", Transportation Research
Part A Policy and Practice, 38(5), pp. 383-404 (2004).
39. Sampaio, B.R., Neto, O.L., and Sampaio, Y. E-
ciency analysis of public transport systems: Lessons for
institutional planning", Transportation Research Part
A Policy and Practice, 42(3), pp. 445-454 (2008).
40. Martn, J.C. and Roman, C. An application of DEA
to measure the eciency of Spanish airports prior to
privatization", J. Air Transp. Manag., 7(3), pp. 149-
157 (2001).
890 H. Omrani et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 868{890
41. Cullinane, K., Wang, T.-F., Song, D.-W., and Ji, P.
The technical eciency of container ports: Comparing
data envelopment analysis and stochastic frontier
analysis", Transportation Research Part A Policy and
Practice, 40(4), pp. 354-374 (2006).
42. Hung, S.-W., Lu, W.-M., and Wang, T.-P. Benchmarking
the operating eciency of Asia container
ports", Eur. J. Oper. Res., 203(3), pp. 706-713 (2010).
43. Markovits-Somogyi, R. Measuring eciency in transport:
the state of the art of applying data envelopment
analysis", Transport, 26(1), pp. 11-19 (2011).
44. Bang, H.-S., Kang, H.-W., Martin, J., and Woo, S.-
H. The impact of operational and strategic management
on liner shipping eciency: a two-stage DEA
approach", Marit. Policy Manag., 39(7), pp. 653-672
(2012).
45. Panayides, P.M., Lambertides, N., and Savva, C.S.
The relative eciency of shipping companies", Transport
Res. E. Log., 47(5), pp. 681-694 (2011).
46. Charnes, A. and Cooper, W.W. The non-archimedean
CCR ratio for eciency analysis: A rejoinder to Boyd
and Fare", Eur. J. Oper. Res., 15(3), pp. 333-334
(1984).
47. Zadeh, L.A. Fuzzy sets as a basis for a theory of
possibility", Fuzzy Set. Syst., 1(1), pp. 3-28 (1978).