Supply Chain Partner Selection of Offshore Oil Engineering in Fuzzy Environment: Modelling and Reviewing

Authors:Guo Liping


The focus of supply chain management has shifted from the original local process optimization to the unified coordination of the entire supply chain. The nodal enterprises and links of the supply chain are no longer independent, but take maximizing the satisfaction of the final customer needs as the mutual goal. The selection of partners under supply chain conditions is to enhance the overall competitiveness of the supply chain, and the evaluation and selection of partners is the basis for the operation of the supply chain cooperation relationship. In this paper, fuzzy mathematics was used to solve the fuzzy uncertainty problem in decision-making and optimization of supply chain management of offshore oil engineering. The important task is to choose the appropriate upstream and downstream partners. First, define the enterprise fuzzy set and its eigenfunctions in the regional industry, and then build the supply chain partner output forecasting model, and finally select the supply chain partners of offshore oil engineering based on the output value.

Full Text:



Amin, S.H. and G. Zhang. 2012. An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Systems with Applications, 39 (8): 6782-6791.

Amindoust, A., S. Ahmed, A. Saghafinia and A. Bahreininejad. 2012. Sustainable supplier selection: A ranking model based on fuzzy inference system. Applied Soft Computing, 12 (6): 1668-1677.

Chan, F. and H.J. Qi. 2003. An innovative performance measurement method for supply chain management. Supply Chain Management-An International Journal, 8 (3-4): 209-223.

Chen, C.L. and W.C. Lee. 2004. Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, 28 (6-7): 1131-1144.

Chen, C.T., C.T. Lin and S.F. Huang. 2006. A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102 (2): 289-301.

Chou, S. and Y. Chang. 2008. A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach. Expert Systems with Applications, 34 (4): 2241-2253.

Chou, S., Y. Chang and C. Shen. 2008. A fuzzy simple additive weighting system under group decision-making for facility location selection with objective / subjective attributes. European Journal of Operational Research, 189 (1): 132-145.

Duan, M., Z. Liu, D. Yan, W. Peng and A. Baghban. 2018. Application of LSSVM algorithm for estimating higher heating value of biomass based on ultimate analysis. Energy Sources Part A-Recovery Utilization and Environmental Effects, 40 (6): 709-715.

Fahimnia, B., C.S. Tang, H. Davarzani and J. Sarkis. 2015. Quantitative models for managing supply chain risks: A review. European Journal of Operational Research, 247 (1): 1-15.

Gao, W. and W.F. Wang. 2017a. The fifth geometric-arithmetic index of bridge graph and carbon nanocones. Journal of Difference Equations and Applications, 23 (1-2): 100-109.

Gao, W. and W.F. Wang. 2017b. A tight neighborhood union condition on fractional (g, f, n, m)-critical deleted graphs. Colloquium Mathematicum, 149 (2): 291-298.

Gao, W. and W.F. Wang. 2017c. New isolated toughness condition for fractional (g, f, n)-critical graphs. Colloquium Mathematicum, 147 (1): 55-66.

Gao, W., A.Q. Baig, H. Ali, W. Sajjad and M.R. Farahani. 2017a. Margin based ontology sparse vector learning algorithm and applied in biology science. Saudi Journal of Biological Sciences, 24 (1): 132-138.

Gao, W., L.L. Zhu, Y and Guo, K.Y. Wang. 2017b. Ontology learning algorithm for similarity measuring and ontology mapping using linear programming. Journal of Intelligent & Fuzzy Systems 33 (5): 3153-3163.

Gao, W., M.R. Farahani, A. Aslam and S. Hosamani. 2017c. Distance learning techniques for ontology similarity measuring and ontology mapping. Cluster Computing-The Journal of Networks Software Tools and Applications, 20 (2): 959-968.

Gao, W., Y.Q. Wang, B. Basavanagoud and M.K. Jamil. 2017d Characteristics studies of molecular structures in drugs. Saudi Pharmaceutical Journal, 25 (4): 580-586.

Gao, W., Y.Q. Wang, W.F. Wang and L. Shi. 2017e. The first multiplication atom-bond connectivity index of molecular structures in drugs. Saudi Pharmaceutical Journal, 25 (4): 548-555.

Giannoccaro, I., P. Pontrandolfo and B. Scozzi. 2003. A fuzzy echelon approach for inventory management in supply chains. European Journal of Operational Research, 149 (1): 185-196.

Govindan, K., R. Khodaverdi and A. Jafarian. 2013. A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. Journal of Cleaner Production, 47: 345-354.

Govindan, K., R. Khodaverdi and A. Vafadarnikjoo. 2015. Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain. Expert Systems with Applications, 42 (20): 7207-7220.

Kahraman, C., T. Ertay and G. Buyukozkan. 2006. A fuzzy optimization model for QFD planning process using analytic network approach. European Journal of Operational Research, 171 (2): 390-411.

Kannan, D., A.B. Lopes De Sousa Jabbour and C.J. Chiappetta Jabbour. 2014. Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company. European Journal of Operational Research, 233 (2SI): 432-447.

Kannan, D., K. Govindan and S. Rajendran. 2015. Fuzzy Axiomatic Design approach based green supplier selection: A case study from Singapore. Journal of Cleaner Production, 96 (SI): 194-208.

Khan, M., M.Y. Jaber, A.L. Guiffrida and S. Zolfaghari. 2011. A review of the extensions of a modified EOQ model for imperfect quality items. International Journal of Production Economics, 132 (1): 1-12.

Liao, C. and H. Kao. 2011. An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management. Expert Systems with Applications, 38 (9): 10803-10811.

Lin, R. 2013. Using fuzzy DEMATEL to evaluate the green supply chain management practices. Journal of Cleaner Production, 40: 32-39.

Liu, S.T. and C. Kao. 2004. Solving fuzzy transportation problems based on extension principle. European Journal of Operational Research, 153 (3): 661-674.

Liu, Z., D. Zhang and W. Peng. 2018. A Novel ANFIS-PSO Network for forecasting oil flocculated asphaltene weight percentage at wide range of operation conditions. Petroleum Science and Technology, 36 (14): 1044-1050.

Liu, Z., J. Feng and B. Liu. 2019. Pricing and service level decisions under a sharing product and consumers’ variety-seeking behavior. Sustainability, 11 (24), 6951.

Lu, L.Y.Y., C.H. Wu and T.C. Kuo. 2007. Environmental principles applicable to green supplier evaluation by using multi-objective decision analysis. International Journal of Production Research, 45 (18-19): 4317-4331.

Mirhedayatian, S.M., M. Azadi and R.F. Saen. 2014. A novel network data envelopment analysis model for evaluating green supply chain management. International Journal of Production Economics, 147 (SIB): 544-554.

Mula, J., R. Poler, J.P. Garcia-Sabater and F.C. Lario. 2006. Models for production planning under uncertainty: A review. International Journal of Production Economics, 103 (1): 271-285.

Peidro, D., J. Mula, R. Poler and J. Verdegay. 2009. Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160 (18): 2640-2657.

Qin, J., X. Liu and W. Pedrycz. 2017. An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment. European Journal of Operational Research, 258 (2): 626-638.

Rostamzadeh, R., K. Govindan, A. Esmaeili and M. Sabaghi. 2015. Application of fuzzy VIKOR for evaluation of green supply chain management practices. Ecological Indicators, 49: 188-203.

Sanayei, A., S.F. Mousavi and A. Yazdankhah. 2010. Group decision making process for supplier selection with VIKOR under fuzzy environment. Expert Systems with Applications, 37 (1): 24-30.

Shaw, K., R. Shankar, S.S. Yadav and L.S. Thakur. 2012. Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Systems with Applications, 39 (9): 8182-8192.

Shen, L., L. Olfat, K. Govindan, R. Khodaverdi and A. Diabat. 2013. A fuzzy multi criteria approach for evaluating green supplier's performance in green supply chain with linguistic preferences. Resources Conservation and Recycling, 74: 170-179.

Tseng, M. and A.S.F. Chiu. 2013. Evaluating firm's green supply chain management in linguistic preferences. Journal of Cleaner Production, 40: 22-31.

Wang, J.T. and Y.F. Shu. 2005. Fuzzy decision modeling for supply chain management. Fuzzy Sets and Systems, 150 (1): 107-127.

Wang, X., H.K. Chan, R.W.Y. Yee and I. Diaz-Rainey. 2012. A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain. International Journal of Production Economics, 135 (2): 595-606.

Xu, Z., X. Chen, L. Meng, M. Yu, L. Li and W. Shi. 2019. Sample consensus model and unsupervised variable consensus model for improving the accuracy of a calibration model. Applied Spectroscopy, 73 (7): 747-758.

Yu, D., H. Zhu, W. Han and D. Holburn. 2019. Dynamic multi agent-based management and load frequency control of PV / Fuel cell / wind turbine / CHP in autonomous microgrid system. Energy, 173: 554-568.

Zhao, S. 2009. The nature and value of common sense to decision making. Management Decision, 47 (3): 441-453.

Zhu, B., B. Su and Y. Li. 2018. Input-output and structural decomposition analysis of India's carbon emissions and intensity, 2007/08-2013/14. Applied Energy, 230: 1545-1556.

Zhu, B., S. Ye, M. Jiang, P. Wang, Z. Wu, R. Xie, J. Chevallier and Y. Wei. 2019. Achieving the carbon intensity target of China: A least squares support vector machine with mixture kernel function approach. Applied Energy, 233: 196-207.


  • There are currently no refbacks.