Pricing Strategies of Closed Loop Supply Chain with Uncertain Demand Based on Ecological Cognition

Author(s):  
Dongjing Yu ◽  
Chunxiang Guo
2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Jie Gao ◽  
Xiong Wang ◽  
Qiuling Yang ◽  
Qin Zhong

The dual-channel closed-loop supply chain (CLSC) which is composed of one manufacturer and one retailer under uncertain demand of an indirect channel is constructed. In this paper, we establish three pricing models under decentralized decision making, namely, the Nash game between the manufacturer and the retailer, the manufacturer-Stackelberg game, and the retailer-Stackelberg game, to investigate pricing decisions of the CLSC in which the manufacturer uses the direct channel and indirect channel to sell products and entrusts the retailer to collect the used products. We numerically analyze the impact of customer acceptance of the direct channel (θ) on pricing decisions and excepted profits of the CLSC. The results show that when the variableθchanges in a certain range, the wholesale price, retail price, and expected profits of the retailer all decrease whenθincreases, while the direct online sales price and manufacturer’s expected profits in the retailer-Stackelberg game all increase whenθincreases. However, the optimal recycling transfer price and optimal acquisition price of used product are unaffected byθ.


2018 ◽  
Vol 10 (11) ◽  
pp. 4072 ◽  
Author(s):  
Xiao Zhao ◽  
Xuhui Xia ◽  
Lei Wang ◽  
Guodong Yu

With the increasing attention given to environmentalism, designing a green closed-loop supply chain network has been recognized as an important issue. In this paper, we consider the facility location problem, in order to reduce the total costs and CO2 emissions under an uncertain demand and emission rate. Particularly, we are more interested in the risk-averse method for providing more reliable solutions. To do this, we employ a coherent risk measure, conditional value-at-risk, to represent the underlying risk of uncertain demand and CO2 emission rate. The resulting optimization problem is a 0-1 mixed integer bi-objective programming, which is challenging to solve. We develop an improved reformulation-linearization technique, based on decomposed piecewise McCormick envelopes, to generate lower bounds efficiently. We show that the proposed risk-averse model can generate a more reliable solution than the risk-neutral model, both in reducing penalty costs and CO2 emissions. Moreover, the proposed algorithm outperforms and classic reformulation-linearization technique in convergence rate and gaps. Numerical experiments based on random data and a ‘real’ case are performed to demonstrate the performance of the proposed model and algorithm.


2013 ◽  
Vol 385-386 ◽  
pp. 1863-1868
Author(s):  
Le Ma ◽  
Yi Chai ◽  
Zheng Lu ◽  
Ying Ying Zhang

This paper studies uncertain demand by a supplier and manufacturer two echelon remanufacturing closed-loop supply chain system model setting up and solving the problem, gets the manufacturers to control the transfer function of the z-domain expression and manufacturer System Model solving analysis.


2012 ◽  
Vol 29 (01) ◽  
pp. 1240003 ◽  
Author(s):  
JIE WEI ◽  
JING ZHAO ◽  
YONGJIAN LI

This paper studies pricing problem for a closed-loop supply chain consisting of a manufacturer and a retailer in a fuzzy environment. The purpose of this paper is to explore how the manufacturer makes his decisions about wholesale price and transfer price and how the retailer makes her decisions about retail price and collecting price in the expected value standard. Each firm's optimal pricing strategies are established by using game theory under the centralized and decentralized decision cases, respectively. Managerial insights into the economic behavior of firms are also investigated, which can serve as the basis for empirical study in the future. Moreover, we analyze numerically the results and give some insights on the influence of some parameters.


2021 ◽  
Vol 14 (11) ◽  
pp. 519
Author(s):  
Agnieszka Szmelter-Jarosz ◽  
Javid Ghahremani-Nahr ◽  
Hamed Nozari

In this paper, a sustainable closed-loop supply chain problem is modelled in conditions of uncertainty. Due to the COVID-19 pandemic situation, the designed supply chain network seeks to deliver medical equipment to hospitals on time within a defined time window to prevent overcrowding and virus transmission. In order to achieve a suitable model for designing a sustainable closed-loop supply chain network, important decisions such as locating potential facilities, optimal flow allocation, and vehicle routing have been made to prevent the congestion of vehicles and transmission of the COVID-19 virus. Since the amount of demand in hospitals for medical equipment is unknown, the fuzzy programming method is used to control uncertain demand, and to achieve an efficient solution to the decision-making problem, the neutrosophic fuzzy method is used. The results show that the designed model and the selected solution method (the neutrosophic fuzzy method) have led to a reduction in vehicle traffic by meeting the uncertain demand of hospitals in different time windows. In this way, both the chain network costs have been reduced and medical equipment has been transferred to hospitals with social distancing.


Sign in / Sign up

Export Citation Format

Share Document