This paper investigates the problems of making optimal decisions on pricing and shelf-space for a fuzzy supply chain with one perishable product, with the help of Radio Frequency Identification Device (RFID) technology to reduce shrinkage. Based on the criterion of Value-at-Risk (VaR) and its minimization, we introduce one centralized decision model and three decentralized decision models to obtain the respective optimal decisions of both the manufacturer and the retailer, by analyzing the fuzzy uncertainties and the relationship among the demand, retail price and shelf-space. The corresponding optimal strategies of the two participants are obtained along with one example.
AgrawalP. and ShardaR., Impact of frequency of alignment of physical and information system inventories on out of stocks: as imulation study, Intentional Journal of Production Economics136(1) (2012), 45–55.
2.
VlachosI.P., A hierarchical model of the impact of rfid practices on retail supply chain performance, Expert Systems with Applications41(1) (2014), 5–15.
3.
CuiL.,
WangL. and DengJ., RFID technology investment evaluation model for the stochastic joint replenishment and delivery problem, Expert Systems with Applications41(4) (2014), 1792–1805.
4.
De VirgilioR. and
MilicchioF., Physical design for distributed rfid-based supply chain management, Distributed and Parallel Databases34(1) (2016), 3–32.
5.
FanH., WuQ., LinY. and ZhangJ., A split-path schemabased rfid data storage model in supply chain management, Sensors13(1) (2013), 5757–5776.
6.
FanT., TaoF., DengS. and LiS., Impact of rfid technology on supply chain decisions with inventory inaccuracies, International Journal of Production Economics159 (2015), 117–125.
7.
ZhangL.H., FanT.J., Wen-ChyuanC. and TaoF., Misplaced inventory and lead-time in the supply chain: Analysis of decisionmaking on rfid investment with service level, Journal of Applied Mathematics159 (2014), 1–18.
8.
ZhuL., HongK.S. and LeeC., Optimal ordering policy of a riskaverse retailer subject to inventory inaccuracy, Mathematical Problems in Engineering2013 (2013), 1–8.
9.
XuC. and ZhaoD., Optimal decisions for adoption of itemlevel rfid in a retail supply chain with inventory shrinkage under cvar criterion, Discrete Dynamics in Nature and Society2016 (2016), 1–17.
10.
LiB., HouP.W., ChenP. and LiQ.H., Pricing strategy and coordination in a dual channel supply chain with a riskaverse retailer, International Journal of Production Economics178 (2016), 154–168.
11.
SzmerekovskyaJ.G. and ZhangJ., Coordination and adoption of item-level rfid with vendor managed inventory, Transportation Research Part C114(1) (2008), 388–398.
12.
SzmerekovskyJ.G., TilsonV. and ZhangJ., Analytical model of adoption of item level rfid in a two-echelon supply chain with shelf-space and price-dependent demand, Decision Support Systems51(1) (2011), 833–841.
13.
LauA. and LauH., Some two-echelon supply-chain games: Improving from deterministic-symmetric-information to stochastic-asymmetric-information models, European Journal of Operational Research161(1) (2005), 203–223.
14.
ZadehL.A., Fuzzy sets, Information and Control8(3) (1965), 338–353.
15.
LiuB.D. and LiuY.K., Expected value of fuzzy variable and fuzzy expected value models, Ieee Transactions on Fuzzy Systems10(4) (2002), 445–450.
16.
SongQ., ShiK., LinS. and XuG., Optimal decisions for a fuzzy two-echelon supply chain, Discrete Dynamics in Nature and Society2014 (2014), 1–8.
17.
PengM., PengY. and ChenH., Post-seismic supply chain risk management: A system dynamics disruption analysis approach for inventory and logistics planning, Computers & Operations Research42 (2014), 14–24.
18.
MaY., YanF., KangK. and WeiX., A novel integrated production-distribution planning model with conflict and coordination in a supply chain network, Knowle dge-Base d Systems105(1) (2016), 119–133.
19.
WanS.P., WangF. and DongJ.Y., A novel group decision making method with intuitionistic fuzzy preference relations for rfid technology selection, Applied Soft Computing38(1) (2016), 405–422.
20.
LiuB., Uncertainty Theory: An Introduction to its Axiomatic Foundations, Springer, Berlin, Germany, 2004.
21.
BaiX. and LiuY., Cvar reduced fuzzy variables and their second order moments, Iranian Journal of Fuzzy Systems12(5) (2015), 45–75.
22.
BaiX. and LiuY., Semideviations of reduced fuzzy variables: a possibility approach, Fuzzy Optimization and Decision Making13(2) (2014), 173–196.
23.
GaoJ. and LiuB., Fuzzy multilevel programming with a hybrid intelligent algorithm, Computers & Mathematics with Applications49(9-10) (2005), 1539–1548.
24.
WangC., TangW. and ZhaoR., On the continuity and convexity analysis of the expected value function of a fuzzy mapping, Journal of Uncertain Systems1(2) (2007), 148–160.
25.
ZhaoR., TangW. and WangC., Static bayesian games with finite fuzzy types and the existence of equilibrium, Information Sciences178 (2008), 4688–4698.
26.
ZadehL.A., A fuzzy-set-theoretic interpretation of linguistic hedges, J Cybern13(2-3) (1972), 4–34.
27.
ZadehL.A., The concept of a linguistic variable and its application to approximate reasoning-i, Information Sciences8(3) (1975), 199–249.
28.
ZhaoJ., TangW. and WeiJ., Pricing decision for substitutable products with retail competition in a fuzzy environment, International Journal of Production Economics135(1) (2012), 144–153.
