A Fuzzy MCDM Approach to Determine the Most Influential Logistic Factors

Year 2019, Volume: 22 Issue: 3, 793 - 800, 01.09.2019

Mehmet Burak Şenol , Aylin Adem , Metin Dağdeviren

https://doi.org/10.2339/politeknik.586041

Cited By: 4

Abstract

The art of managing materials; logistics play a
crucial role in efficiency and productivity of companies. It is very
significant to determine most influential logistic factors since logistic costs
account for 30% of total company costs. The factors affecting success of
logistic enterprises such as cost, speed, reliability, customer satisfaction,
distribution channel, company image, environmental friendliness and
technological innovations are investigated and ranked in this study. Three
different approaches based on Pythagorean Fuzzy sets, triangular fuzzy numbers
and Analytic Hierarchy Process is offered for ranking these factors. Although
it has been introduced to literature recently, the pythagorean fuzzy sets are
widely employed in calculating uncertainty. The three most influential logistic
factors are revealed as cost, speed and reliability, respectively by employing
AHP, Fuzzy AHP and Pythagorean Fuzzy AHP, however factor weights are different.
We think that the differences with in the AHP, F-AHP and PF-AHP results may
stem from expressing evaluations in exact values, linguistic terms or in some
cases it may be related to fulfilling the condition of membership and
non-membership. Furthermore, top 10 logistic firms in Turkey are scored and
ranked to these factors. Logistics, Pythagorean fuzzy sets, AHP, Fuzzy AHP,
MCDM.

Keywords

Logistics, pythagorean fuzzy sets, AHP, fuzzy AHP

References

• 1. Hensher, D. and Brewer, A., “Transport: An Economics and Management Perspective”, Oxford University Press, UK (2000).
• 2. Kumru, M. and Kumru, P.Y., “Analytic hierarchy process application in selecting the mode of transport for a logistics company” J. Adv.Transp. 48: 974–999, (2014).
• 3. Du, B., Guo, S., Huang, X., Li, Y. and Guo, J., A “Pareto supplier selection algorithm for minimum life cycle cost of complex product system”, Exp. Sys. with Appl., 42: 4253–4264, (2015).
• 4. Lai, K.-H., Ngai, E. W. T. and Cheng, T. C. E., “Measures for evaluating supply chain performance in transport logistics”, Transp. Res. Part E Log. and Transp. Rev., 38:439–456, (2002).
• 5. Liu, C.L. and Lyons, A. C., “An analysis of third-party logistics performance and service provision”, Transp. Res. Part E Log. and Transp. Rev., 47(4): 547–570, (2011).
• 6. Moghaddam, K. S., “Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty”, Exp. Sys. with Appl., 42:6237–6254, (2015).
• 7. Rezaei, J., Fahim, P. and Tavasszy, L., “Supplier selection in the airline retail industry using a funnel methodology: Conjunctive screening method and fuzzy AHP”, Exp. Sys. with Appl., 41: 8165–8179, (2014).
• 8. Straight, R. L., “Measuring contractors’ performance”, J. Sup.Chain Manag., 35(1):18–28, (1999).
• 9. Wernerfelt, B., “A resource-based view of the firm”, Strat. Manag. J., 5: 171–180, (1984).
• 10. Barney, J., “Firm resources and sustained competitive advantage”, J. Manag., 17(1): 99–120, (1991).
• 11. Hartmann, E. and Grahl, A. D., “The flexibility of logistics service providers and its impact on customer loyalty: an empirical study”, J. Sup. Chain Manag., 47: 63–85, (2011)
• 12. Hunt, S. D., “Commentary – a general theory of competition: Issues, answers and an invitation”, Euro. J. Market., 35: 524–548 (2001)
• 13. Lai, F., Li, D., Wang, Q., and Zhao, X., “The information technology capability of third-party logistics providers: A resource-based view and empirical evidence from China”, J. Sup. Chain Manag., 44: 22–38, (2008)
• 14. Karia, N. and Wong, C. Y., “The impact of logistics resources on the performance of Malaysian logistics service providers”, Manag. Oper., 24(7): 589–606 (2013)
• 15. Churchman C.W., Ackoff R.L. and Arnoff E.L., “Introduction to Operations Research”, John Wiley & Sons: New York (USA), (1957)
• 16. Yager, R., “Pythagorean membership grades in multi criteria decision making”, IEEE Transactions On Fuzzy Systems, 22(4): 958-965 (2014).
• 17. Yucesan, M. and Kahraman, G., “Risk evaluation and prevention in hydropower plant operations: A model based on Pythagorean fuzzy AHP”, Energy Policy, 343-351, (2019)
• 18. Gul, M. and Ak, M.F., “A comparative outline for quantifying risk ratings in occupational health and safety risk assessment”, J. Clean. Prod., 196: 653-664, (2018)
• 19. lbahar, E., Karaşan, A., Cebi, S. and Kahraman, C. , “A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system”, Safety Science, 103: 124-136, (2018)
• 20. Karasan, A., Ilbahar, E. and Kahraman, C. “A novel pythagorean fuzzy AHP and its application to landfill site selection problem”, Soft Comput., article in press, (2018)
• 21. Mete, S., “Assessing occupational risks in pipeline construction using FMEA-based AHP-MOORA integrated approach under Pythagorean fuzzy environment”, Hum. Ecol. Risk Assess., article in press, (2018)
• 22. Gul, M., “Application of Pythagorean fuzzy AHP and VIKOR methods in occupational health and safety risk assessment: the case of a gun and rifle barrel external surface oxidation and coloring unit”, Int. J. Occup. Safety and Ergon., 1-15, (2018)
• 23. Wang, L., Wang, H., Xu, Z. and Ren, Z., “The interval-valued hesitant Pythagorean fuzzy set and its applications with extended TOPSIS and Choquet integral-based method” Int. J. Intel. Sys., 34(6): 1063-1085, (2019)
• 24. Liang, D. and Xu, Z. , “The new extension of TOPSIS method for multiple criteria decision making with hesitant Pythagorean fuzzy sets”, Appl. Soft Comp. J., 60:167-179, (2017)
• 25. Garg, H., “A new improved score function of an interval-valued pythagorean fuzzy set based topsis method” Int. J. Uncer. Quant.,7(5): 463-474, (2017) 26. Liang, D., Zhang, Y., Xu, Z. and Jamaldeen, A., “Pythagorean fuzzy VIKOR approaches based on TODIM for evaluating internet banking website quality of Ghanaian banking industry”, Appl. Soft Comp. J., 78: 583-594, (2019)
• 27. Cui, F. B., You, X. Y., Shi, H. and Liu, H.C., “Optimal siting of electric vehicle charging stations using pythagorean fuzzy vikor approach”, Math. Prob. in Eng., (2018)
• 28. Zhang, Z.X., Hao, W. N., Yu, X. H., Zhang, S. J. and Chen, J.Y., “Pythagorean fuzzy preference ranking organization method of enrichment evaluations”, Int. J. Intel. Sys., article in press, (2019)
• 29. Bolturk, E., “Pythagorean fuzzy CODAS and its application to supplier selection in a manufacturing firm”, J. Enterp. Infor. Manag., 31(4): 550-564, (2018)
• 30. Saaty, T. L., “How to make a decision: the analytic hierarchy process”, Euro. J. Oper. Res., 48: 9-26, (1970)
• 31. Şenol, M.B., Dağdeviren, M., Kurt, M. and Çilingir, C., “Evaluation of cockpit design by using quantitative and qualitative tools”, IEEE Int. Conf. on Ind. Eng. and Eng. Manag., 847-851, (2009).
• 32. Şenol, M.B., Dağdeviren, M., Kurt, M. and Çilingir, C., “Display panel design of a general utility helicopter by applying quantitative and qualitative approaches”, Hum. Fact. Ergon. in Manuf., 20 (1): 73-86, (2010).
• 33. Şenol, M.B., Dağdeviren, M. and Kurt, M., “A multi criteria approach for aircraft cockpit interface evaluation”, J. Fac. Eng. Arch. Gazi Univ., 28 (4): 685-693, (2013)
• 34. Dağdeviren, M., “Decision making in equipment selection: an integrated approach with AHP and PROMETHEE”, J. Intel. Manuf., 19: 397-406, (2008)
• 35. Dağdeviren, M, Eren, T., "Analytic hierarchy process and use of 0-1 goal programming methods in selecting supplier firm", J. Fac. Eng. Archit., Gazi Univ. Cilt 16, 41-52, (2001)
• 36. Bozdağ, C.E., Kahraman, C. and Ruan, D., "Fuzzy group decision making for selection among computer integrated manufacturing systems", Comp. Ind., 5: 13-29, (2003)
• 37. Büyüközkan, G., Ertay, T., Kahraman, C. and Ruan, D., "Determining the importance weights for the design requirements in the house of quality using the fuzzy analytic network approach", Int. J. Intel. Syst., 19: 443-461, (2004)
• 38. Xiaohua, W. and Zhenmin, F., "Sustainable development of rural energy and its appraising system in Chine", Renew. Sustain. Energ., 6: 395-404, (2002)
• 39. Yedla, S. and Shresta, R.M., "Multi-criteria approach for the selection of alternative options for environmentally sustainable transport system in Delhi", Transp. Res., 37: 717-729, (2003)
• 40. Aras, H., Erdogmus, S. and Koc, E., "Multi-criteria selection for a wind observation station location using analytic hierarchy process", Renew. Energ., 29: 1383-1392, (2004)
• 41. Tolga, E, Demircan, M.L., and Kahraman, C., "Operating system selection using fuzzy replacement analysis and analytic hierarchy process", Int. J. Prod. Econ., 97: 89-117, (2005)
• 42. Kim, P. P., Lee K. J. and Lee B.W., "Selection of an optimal nuclear fuel cycle scenario by goal programming & analytic hierarchy process", Ann. of Nucl. Energy, 26: 449-460, (1999)
• 43. Topraklı, A. Y., Kabak, M., Özceylan, E. and Adem, A., “Prioritization of mosque facility site selection criteria under fuzzy environment”, 13. Int. Conf. on Theory and Appl. of Fuzzy Sys. Soft Comp., (2018)
• 44. Zhang, X. and Xu, Z., “Extension of topsis to multiple criteria decision making with pythagorean fuzzy sets” Int. J. Intel. Syst., 1061-1078, (2014)
• 45. Peng, X. and Yong, Y., “Some results for pythagorean fuzzy sets” Int. J. Intel. Syst., 1133-1160, (2014)