Reducing the Risk of Transportation Disruption in Supply Chain: Integration of FUZZY-AHP and TOPSIS

Ahmad Jafarnejad Chaghooshi, Moein Hajimaghsoudi

Abstract


This research focuses on estimating and understanding the causes of transportation related supply chain disruptions. In-depth interviews with logistics managers were undertaken to investigate how companies perceive transportation related supply chain disruptions and what they are doing to respond and address them. This paper presents an integrated approach for selecting the best solution to reduce the risk of transportation disruption. In this paper key performance indicator (KPI) are criteria and solutions are alternatives. FUZZY-AHP and TOPSIS are used in the integrated approach. FUZZY-AHP is used to determine the fuzzy weights of criteria because it can effectively determine various criteria’s weights in a hierarchical structure. TOPSIS aims to rank solutions with respect to the criteria. We apply the integrated approach in real case to demonstrate the application of the proposed method.


Full Text:

PDF

References


Arreola-Risa, A., &DeCroix, G. A. (1998).Inventory management under randomsupply disruptions and partial backorders. Naval Research Logistics, 45, 687–703.

Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3),233–247.

Cachon, G.P., (2004). The allocation of inventory risk in a supply chain: push, pull, and advance-purchase discount contracts. Management Science 2 (50), 222–238.

Chen, F., Drezner, Z., Ryan, J.K., Simchi-Levi, D., (2000).Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times, and information. Management Science 3 (46), 436–443.

Chen, J., Zhao, X., & Zhou, Y. (2012).A periodic-review inventory system with a capacitated backup supplier for mitigating supply disruptions. European Journal of Operational Research, 219, 312–323.

Cheng, C. H. (1997). Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. European Journal of Operational Research, 96(2), 343–350.

Cheng-Shiung, W& Chin-Tsai, L and Chuan, L, (2010), Optimal marketing strategy: A decision-making with ANP and TOPSIS, International Journal of Production Economics, volume 197, pp.190 -196

Chopra, S.C., Sodhi, M.S., (2004).Managing risk to avoid supply-chain breakdown. MIT Sloan Management Review 1 (46), 53–61.

Chopra, S., Reinhardt, G., & Mohan, U. (2007). The importance of decoupling recurrent and disruption risks in a supply chain. Naval Research Logistics, 54, 544–555.

Hwang, Chen, (1929): Decision making; Fuzzy systems; Mathematical models, Springer-Verlag, Book (ISBN 3540549986 ) xii, 536 p.

Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational research, 95(3), 649–655

Cheng, C. H. (1997). Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. European Journal of Operational Research, 96(2), 343–350.

Clausen, J, Larsen, J, Larsen, A & Hansen, J (2001), Disruption management - operations research between planning and execution

Dag ˘deviren, M., Yüksel, _ I., & Kurt, M. (in press). A fuzzy analytic network process.(ANP) model to identify faulty behavior risk (FBR) in work system. Safety Science.

Deng, H. (1999). Multicriteria analysis with fuzzy pairwise comparison. International Journal of Approximate reasoning, 21(3), 215–231.

Leung, L. C., & Cao, D. (2000). On consistency and ranking of alternatives in fuzzy AHP. European Journal of operational Research, 124(1), 102–113.

Mikhailov, L. (2004). A fuzzy approach to deriving priorities from interval pairwise comparison judgments. European Journal of Operational Research, 159(3), 687–704.

H. Hishamuddin, R. A. Sarker, D. Essam (2013).A recovery model for a two-echelon serial supply chain with consideration of transportation disruption Computers and Industrial Engineering , Volume 64 Issue 2

Hwang .C.L and K. Yoon(1981)., Multiple Attributes Decision Making Methods and Applications, spring, New York

Irfan Ertugrul, Nilsen Karakasoglu(2009): Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Syst. Appl. 36(1): 702-715

Kleindorfer, P.R., Saad, G.H., (2005). Managing disruption risks in supply chains. Production and Operations Management 1 (14), 53–68.

Lee, A. H. I., Chen, W.-C., & Chang, C.-J. (2008). A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Systems with Applications, 34(1), 96–107.

Lee, H.L., (2002). Aligning supply chain strategies with product uncertainties. California Management Review 3 (44), 105–119.

Li, Z., Xu, S. H., &Hayya, J. (2004).A periodic-review inventory system with supplyinterruptions. Probability in the Engineering and Informational Sciences, 18,33–53.

Liang, G. S., & Wang, M. J. (1994). Personnel selection using fuzzy MCDM algorithm.European Journal of Operational Research, 78, 22–33.

Martha C. Wilson (2007):The impact of transportation disruptions on supply chain performance, Transportation Research Part E: Logistics and Transportation Review Volume 43, Issue 4, July 2007, Pages 295–320

Moinzadeh, Prabhu Aggarwal(1997) Analysis of a Production/Inventory System Subject to Random Disruptions, 43 (11) , pp. 1577–1588

Mohebbi, E. (2003). Supply interruptions in a lost-sales inventory system withrandom lead time: [doi: 10.1016/S0305-0548(01)00108-3]. Computers &Operations Research, 30, 411–426.

Opricovic. S and Tzeng. G.H, (2003), Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS, European Journal of Operational Research 156 (2) ,pp. 445–455.

Schmitt, A. J., Snyder, L. V., &Shen, Z.-J.M. (2010). Inventory systems with stochastic demand and supply: Properties and approximations. European Journal of Operational Research, 206, 313–328.

Sheffi, Y. (2005). The resilient enterprise: Overcoming vulnerability for competitive advantage. Massachusetts: Cambridge.

Shyur, L.-F.*, Chen, J.-L., and Yang, N.-S. (2006) Truncated glucanase with enhanced activity and method for making the same. (US patent No. 7037696; ROC patent No. 201683)

Snyder, L. V., Atan, Z., Peng, P., Rong, Y., Schmitt, A. J., &Sinsoysal, B. (2012). OR/MS Models for Supply Chain Disruptions: A Review. Available from http://ssrn.com/abstract=1689882.

Tomlin, B. (2006). On the value of mitigation and contingency strategies for managing supply chain disruption risks. Management Science, 52, 639–657.

Triantaphyllou, E., and C. Lin, (1996), "Development and Evaluation of Five Fuzzy Multi-Attribute Decision-Making Methods," Approximate Reasoning, Vol. 14, No. 4, pp. 281-310.

Van Laarhoven, P. J. M., &Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1–3), 229–241.

Wang .T.C and Chang. T.H, (2007), Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment, Expert Systems with Applications 33 ,pp. 870–880.

Xiao and Yu(2006) Managing Supply Chains on the Silk Road: Strategy, Performance, and Risk

Yu, C. S. (2002). A GP-AHP method for solving group decision-making fuzzy AHP problems. Computers and Operations Research, 29, 1969–2001.

Zsidisin, G.A., Ellram, L.M., (2003). An agency theory investigation of supply risk management. The Journal of Supply Chain Management(Summer), 15–27.

Zhu, Q.H., Sarkis, J., Geng, Y., (2005). Green supply chain management in China: pressures, practices and performance. International Journal of Operations and Production Management 25, 449–468.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Copyright ©2013 Academic Journals Center

To make sure that you can receive messages from us, please add the 'academicjournalscenter.org' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.