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Article type: Research Article
Authors: Wang, Jingjinga; b | Xu, Minlia; * | Jian, Huiyuna
Affiliations: [a] School of Business, Central South University, Changsha, Hunan Province, China | [b] School of Mathematics and Finance, Hunan University of Humanities, Science and Technology, Loudi, Hunan Province, China
Correspondence: [*] Corresponding author. Minli Xu, Business School of Central South University, Changsha 410083, Hunan Province, China. E-mail: [email protected].
Abstract: This paper considers a two-stage supply chain consisting of one manufacturer and one retailer, exploring the impact of the fuzzy uncertainty of product yield and demand and the deciders’ risk attitudes on the optimal order quantity of the retailer. At the same time, this study tries to analyze the coordination problem in the two-stage supply chain with consideration of the retailer and the manufacturer’s risk attitudes. Firstly, this study develops a supply chain optimal decision model in a centralized decision framework. In the proposed model, the L-R fuzzy numbers are used to depict the yield and demand with fuzzy characteristics. Then, the coordination of quantity discount in a supply chain is studied. Consequently, this research further investigates a special case in which the market demand and yield are assumed to be triangular fuzzy numbers, and the optimal solution of the order quantity and the wholesale price are obtained. At last, this paper utilizes several numerical examples to validate the proposed model. The results show that the quantity discount contract can coordinate the supply chain in a fuzzy environment, and the optimal order quantity decreases with the increasing of the risk bias coefficient of the retailer and the manufacturer. It also suggests that risk-seeking retailer will order more products, in addition, the manufacturer tend to choose a risk-seeking retailer as partner and the retailer is more likely to choose a risk-seeking rather than risk-aversion manufacturer as partner.
Keywords: Fuzzy yield, fuzzy demand, weighted average value, L-R fuzzy number, risk preference
DOI: 10.3233/JIFS-182693
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 27-41, 2021
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