JACIII Vol.22 No.1 pp. 5-16
doi: 10.20965/jaciii.2018.p0005


Suitable Aggregation Models Based on Risk Preferences for Supplier Selection and Order Allocation Problem

Sirin Suprasongsin*,**, Pisal Yenradee**, Van-Nam Huynh*, and Chayakrit Charoensiriwath***

*School of Knowledge Science, Japan Advanced Institute of Science and Technology
1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan

**School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University
99 Moo 18, Km. 41 on Paholyothin Highway Khlong Luang, Pathum Thani 12120, Thailand

***National Electronics and Computer Technology Center
112 Phahonyothin Road, Khlong Nueng, Khlong Luang District, Pathum Thani 12120, Thailand

November 21, 2016
April 3, 2017
January 20, 2018
fuzzy multiple objective linear programming, aggregation operators, risk preferences of decision makers, supplier selection and order allocation

In this paper, we propose (a) fuzzy multiple objective linear programming models for the Supplier Selection and Order Allocation (SSOA) problem under fuzzy demand and volume/quantity discount environments, and (b) an analysis of how to select the suitable aggregation operator based on the risk preferences of decision makers. The aggregation operators under consideration are additive, maximin, and augmented operators while the risk preferences are classified as risk-averse, risk-taking, and risk-neutral ones. The suitabilities of aggregation operators and risk preferences of decision makers are analyzed by a statistical technique, considering the average and the lowest satisfaction levels of the supplier selection criteria, based on numerical examples. Analysis results reveal that decision makers with different risk preferences will prefer only some aggregation operators and models. Moreover, a particular aggregation operator and model may generate a dominated solution for some situations. Thus, it should be applied with caution.

Cite this article as:
S. Suprasongsin, P. Yenradee, V. Huynh, and C. Charoensiriwath, “Suitable Aggregation Models Based on Risk Preferences for Supplier Selection and Order Allocation Problem,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.1, pp. 5-16, 2018.
Data files:
  1. [1] S. H. Ghodsypour and C. Obrien, “The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint,” Int. J. of Production Economics, Vol.73, No.1, pp. 15-27, 2001.
  2. [2] G. W. Dickson, “An analysis of vendor selection systems and decisions,” J. of Purchasing, Vol.2, No.1, pp. 5-17, 1996.
  3. [3] L. A. Zadeh, “Fuzzy sets,” Information and Control, Vol.8, No.3, pp. 338-353, 1965.
  4. [4] W. Ho, X. Xu, and P. K. Dey, “Multi-criteria decision making approaches for supplier evaluation and selection: A literature review,” European J. of Operational Research, Vol.202, No.1, pp. 16-24, 2010.
  5. [5] Y. Wind and P. J. Robinson, “The determinants of vendor selection: the evaluation function approach,” J. of Purchasing, Vol.4, No.3, pp. 29-42, 1968.
  6. [6] R. Narasimhan, “An analytical approach to supplier selection,” J. of Purchasing and Materials Management, Vol.19, No.4, pp. 27-32, 1983.
  7. [7] D. Schmeidler, “Integral representation without additivity,” Proc. of the American Mathematical Society, Vol.97, No.2, pp. 255-261, 1986.
  8. [8] R. R. Yager, “On ordered weighted averaging aggregation operators in multicriteria decision making,” IEEE Trans. on systems, Man, and Cybernetics, Vol.18, No.1, pp. 183-190, 1988.
  9. [9] W. Xia and Z. Wu, “Supplier selection with multiple criteria in volume discount environments,” Omega, Vol.35, No.5, pp. 494-504, 2007.
  10. [10] T.-Y. Wang and Y.-H. Yang, “A fuzzy model for supplier selection in quantity discount environments,” Expert Systems with Applications, Vol.36, No.10, pp. 12179-12187, 2009.
  11. [11] A. Amid, S. Ghodsypour, and C. Obrien, “A weighted additive fuzzy multiobjective model for the supplier selection problem under price breaks in a supply chain,” Int. J. of Production Economics, Vol.121, No.2, pp. 323-332, 2009.
  12. [12] A. H. Lee, H.-Y. Kang, C.-M. Lai, and W.-Y. Hong, “An integrated model for lot sizing with supplier selection and quantity discounts,” Applied Mathematical Modelling, Vol.37, No.7, pp. 4733-4746, 2013.
  13. [13] J.-l. Zhang and J. Chen, “Supplier selection and procurement decisions with uncertain demand, fixed selection costs and quantity discounts,” Computers & Operations Research, Vol.40, No.11, pp. 2703-2710, 2013.
  14. [14] S. Sirin and Y. Pisal, “Supplier selection with multi criteria and multi products in volume discount and quantity discount environments,” Proc. of Int. Conf. on Image Processing, Computers and Industrial Engineering (ICICIE’2014), pp. 18-22, 2014.
  15. [15] R. Hammami, C. Temponi, and Y. Frein, “A scenario-based stochastic model for supplier selection in global context with multiple buyers, currency fluctuation uncertainties, and price discounts,” European J. of Operational Research, Vol.233, No.1, pp. 159-170, 2014.
  16. [16] M. B. Ayhan and H. S. Kilic, “A two stage approach for supplier selection problem in multi-item/multi-supplier environment with quantity discounts,” Computers & Industrial Engineering, Vol.85, pp. 1-12, 2015.
  17. [17] M. M. Mazdeh, M. Emadikhiav, and I. Parsa, “A heuristic to solve the dynamic lot sizing problem with supplier selection and quantity discounts,” Computers & Industrial Engineering, Vol.85, pp. 33-43, 2015.
  18. [18] F. Çebi and İ. Otay, “A two-stage fuzzy approach for supplier evaluation and order allocation problem with quantity discounts and lead time,” Information Sciences, Vol.339, pp. 143-157, 2016.
  19. [19] A. Amid, S. Ghodsypour, and C. Obrien, “A weighted max–min model for fuzzy multi-objective supplier selection in a supply chain,” Int. J. of Production Economics, Vol.131, No.1, pp. 139-145, 2011.
  20. [20] F. Arikan, “A fuzzy solution approach for multi objective supplier selection,” Expert Systems with Applications, Vol.40, No.3, pp. 947-952, 2013.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Aug. 17, 2018