Document Type : Research Paper
Authors
Abstract
By applying fuzzy set theory in game theory, player’s strategies are determined by fuzzy variable with a definite membership function, where the degree of non-membership is stated by supplement of degree of membership, while determining the value of uncertain parameters of decisions may be associated with a hesitation degree. Therefore, in this paper, intuitionistic fuzzy variables are used to better describe vague and imprecise information, and also deals with uncertainty and ambiguity in product pricing process. To provide the proposed model, a two-echelon supply chain with one manufacturer and one retailer is considered. For Designing the proposed pricing game, we have considered structure of two-level programming in a Stackelberg game form. Finally, using a numerical example, structural validation and the effectiveness of proposed model is shown in product pricing process
Keywords
Aust, G., Buscher, U., (2012), “Vertical cooperative advertising and pricing decisions in a manufacturer–retailer supply chain: A game-theoretic approach”, European Journal of Operational Research, Vol 223, pp. 473-482.
Boran, F.E, Genç, S, Kurt, M, Akay, D, (2009) “A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method” Expert Systems with Applications. 36, 11363-11368.
Cai, G., Zhang, Z.G., Zhang, M., (2009), Game theoretical perspectives on dual-channel supply chain compe tition with price disc ounts and pricing schemes, International journal of Production Economics, Vol. 117, pp. 80–96.
Giri, B.C., Sharma, S., (2014), “Manufacturer’s pricing strategy in a two-level supply chain with competing retailers and advertising cost dependent demand”, Economic Modelling, Vol 38, pp. 102-111.
Heilpern S., (1992) “The expected value of a fuzzy number,” Fuzzy Sets and Systems, vol. 47, pp.81-8.
Huang, Y., Huang, G.Q., Newman, S.T., (2011), “Coordinating pricing and inventory decisions in a multi-level supply chain: A game-theoretic approach”, Transportation Research Part E, Vol 47, pp. 115–129.
Leng, M., Parlar, M., (2012), “Transfer pricing in a multidivisional firm: A cooperative game analysis”, Operations Research Letters, 40, pp. 364-369.
Liu, B. (2002).Theory and practice of uncertain programming. Heidelberg: Physica-Verlag.
23
Liu, B., & Liu, Y. (2002). Excepted value of fuzzy variable and fuzzy expected value models.IEEE Transactions on Fuzzy Systems, 10,
445–450.
Liu, H.W., Wang, G.J., (2007) “Multi-criteria decision-making methods based on intuitionistic fuzzy sets”, European Journal of Operational Research, 179, 220–233.
Nahmias, S. (1978). Fuzzy variables. Fuzzy Sets and Systems, 1, 97–110.
Nehi, H. M., (2010) “A New Ranking Method for Intuitionistic Fuzzy Numbers, International Journal of Fuzzy Systems, Vol. 12, No. 1.
Nehi, H. M. Maleki, H. R. (2005), “Optimization problem”, In Proceedings of the 9th WSEAS international conference on systems, Athens, Greece.
SeyedEsfahani, M.M, Biazaran, M., Gharakhani, M., (2011). A game Theoretic approach to coordinate pricing and vertical co-op advertising in manufacturer–retailer supply chains, European Journal of Operational Research, Vol 211, pp. 263–273.
Wei, J. Zhao, J., (2011), “Pricing decisions with retail competition in a fuzzy closed loop supply chain”, Expert Systems with Applications. 38, pp. 11209–11216.
Wu, J.Z., Zhang, Q., (2010) “Multi-criteria decision making method based on intuitionistic fuzzy weighted entropy”, Expert Systems with Applications,
Ye, J., (2011), “Expected value method for intuitionistic trapezoidal fuzzy multicriteria decision-making problems”, Expert Syst. Appl. doi: 10.1016 /j.eswa. 2011.03.059.
Zadeh, L.A., (1965) “Fuzzy sets”, Information and Control, 8, 338–356.
)