Two-Echelon Logistic Model Based on Game Theory with Fuzzy Variable

This paper applies Game Theory Based on Two–Echelon Logistic Models for Competitive behaviors in Logistics developed by Watada et al, which proposed the optimal decision method under two-echelon situation for logistic service providers. This study used three types of game theory; Cournot, Collusion, and Stackelberg to gain the optimizing strategies of exporters in each scenario. The aim of this paper is to realize optimal decision-making under competitiveness of these logistics service providers where they perform different game behaviors for achieving optimum solutions. Due to uncertain demand in the real world, fuzzy demands were applied for game theory in the two-echelon logistic model and compared results between fuzzy and non-fuzzy case. Numerical example is presented to clearly illustrate results by using fuzzy case and using crisp number. We obtain higher profits of both a shipper and forwarders when comparing the results yielded by non-fuzzy and fuzzy approaches.

[1]  Nureize Arbaiy,et al.  Fuzzy random regression based multi-attribute evaluation and its application to oil palm fruit grading , 2014, Ann. Oper. Res..

[2]  J. Watada,et al.  Optimal decision methods in two-echelon logistic models , 2014 .

[3]  Junzo Watada Member,et al.  A parametric assessment approach to solving facility-location problems with fuzzy demands , 2014 .

[4]  Baoding Liu,et al.  A survey of credibility theory , 2006, Fuzzy Optim. Decis. Mak..

[5]  J. Watada,et al.  A parametric assessment approach to solving facility‐location problems with fuzzy demands , 2014 .

[6]  Shanlin Yang,et al.  Two-echelon supply chain models: Considering duopolistic retailers' different competitive behaviors , 2006 .

[7]  吳柏林 Identifying the distribution difference between two populations of fuzzy data based on a nonparametric statistical method , 2013 .

[8]  J. Watada,et al.  Decision making of facility locations based on Fuzzy Probability Distribution Function , 2010, 2010 IEEE International Conference on Industrial Engineering and Engineering Management.

[9]  Junzo Watada,et al.  Risk Assessment of a Portfolio Selection Model Based on a Fuzzy Statistical Test , 2013, IEICE Trans. Inf. Syst..

[10]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.

[11]  Junzo Watada,et al.  Kolmogorov-Smirnov Two Sample Test with Continuous Fuzzy Data , 2010, IUM.

[12]  Hon-Shiang Lau,et al.  Some two-echelon supply-chain games: Improving from deterministic-symmetric-information to stochastic-asymmetric-information models , 2005, Eur. J. Oper. Res..

[13]  Yian-Kui Liu,et al.  Expected value of fuzzy variable and fuzzy expected value models , 2002, IEEE Trans. Fuzzy Syst..

[14]  Hon-Shiang Lau,et al.  Effects of a demand-curve's shape on the optimal solutions of a multi-echelon inventory/pricing model , 2003, Eur. J. Oper. Res..

[15]  Nureize Arbaiy,et al.  BUILDING FUZZY GOAL PROGRAMMING WITH FUZZY RANDOM LINEARPROGRAMMING FOR MULTI-LEVEL MULTI-OBJECTIVE PROBLEM , 2011 .

[16]  Wansheng Tang,et al.  Two-echelon supply chain games in a fuzzy environment , 2008, Comput. Ind. Eng..

[17]  M. Puri,et al.  Fuzzy Random Variables , 1986 .