scholarly journals Multi-Period E-Closed-Loop Supply Chain Network Considering Consumers’ Preference for Products and AI-Push

2019 ◽  
Vol 11 (17) ◽  
pp. 4571 ◽  
Author(s):  
Duan ◽  
Xiu ◽  
Yao
Keyword(s):  
Supply Chain ◽  
Environmental Protection ◽  
Closed Loop ◽  
Supply Chain Network ◽  
Economic Interests ◽  
Closed Loop Supply Chain ◽  
Protection Measures ◽  
Management Level ◽  
The Government ◽  
Dual Objectives

The remanufacturing industry plays a significant role to improve the utilization of resources and contribute to environmental protection and sustainable development. E-commerce channel (e-channel) has become an important retail channel. The multi-period closed-loop supply chain network of electronic channels (E-CLSCN) is studied. Moreover, this study considers the influence of consumers’ preference for products and artificial intelligence push (AI-push) on network balance under the conditions of dynamic and static. The network consists of manufacturers, remanufacturers, and demand markets. Remanufacturers are responsible for collecting and remanufacturing. The multi-period E-CLSCN model is established through variational inequalities. The study finds that whether dynamic or static, consumers’ preference for new products (NP) has positively correlated with the profits of manufacturers and the whole E-CLSCN. Consumers’ preference for remanufacturer products (RP) has only positively correlated with the profits of remanufactures. In the aspect of AI-push, the manufacturers’ AI-push has positively correlated with the profits of the entire players of E-CLSCN, while the remanufacturers’ AI-push has only positively correlated with the profits of the remanufacturers. Also, considering the multi-period, the sensitivity of the demand markets for the investment of manufacturers’ and remanufacturers’ AI-push gradually reduces, and the marginal benefit of AI-push gradually decreases. Finally, this paper addresses interesting managerial insights from the perspective of government and enterprises. Under the dual objectives of environmental protection and economic interests of enterprises, this study provides a reference for governments and enterprises to develop relevant dynamic policies. Namely, the government formulates dynamic environmental protection measures, and enterprises formulate dynamic strategic plans and implement government policies. While promoting the management level of E-CLSCN, it effectively boosts the development of the remanufacturing industry and recycling sustainable economy.

scholarly journals Three-Echelon Closed-Loop Supply Chain Network Equilibrium under Cap-and-Trade Regulation

2021 ◽  
Vol 13 (11) ◽  
pp. 6472
Author(s):  
Guitao Zhang ◽  
Xiao Zhang ◽  
Hao Sun ◽  
Xinyu Zhao
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Utilization Rate ◽  
Carbon Trading ◽  
Supply Chain Network ◽  
Cap And Trade ◽  
Closed Loop Supply Chain ◽  
Collection Rate ◽  
High Emission ◽  
The Government

This paper investigates the impacts of cap-and-trade (CAT) regulation on a three-echelon closed-loop supply chain network (CLSCN) that consists of suppliers, high-emission and low-emission manufacturers, demand markets and carbon trading centers. The presented CLSCN model includes both product trading and carbon trading subnets. Combining variational inequality theory (VI) with complementary theory, we first characterize the optimal conditions for members in each tier first, and then derive that of the entire CLSCN. In addition, we focus on the effects of carbon caps and EOL collection rate target on CLSCN performances with numerical examples. The results reveal that, in some cases, there is a consistency between carbon emission reduction target of the government and the profit target of enterprises. The government should choose reasonable and moderate carbon caps for all the enterprises to balance the CLSCN members’ economic interests, carbon emissions, as well as resources utilization rate. Moreover, the government should not blindly pursue a high collection rate target. The above conclusions can provide practical guidance for governments and enterprises in a CLSCN under CAT regulation.

scholarly journals Research on Closed-Loop Supply Chain Decision-Making in Different Cooperation Modes with Government’s Reward-Penalty Mechanism

2021 ◽  
Vol 13 (11) ◽  
pp. 6425
Author(s):  
Quanxi Li ◽  
Haowei Zhang ◽  
Kailing Liu
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Stackelberg Game ◽  
Recycling Rate ◽  
Transfer Payment ◽  
Closed Loop Supply Chain ◽  
Asymmetry Of Information ◽  
Optimal Decisions ◽  
The Government ◽  
The Impact

In closed-loop supply chains (CLSC), manufacturers, retailers, and recyclers perform their duties. Due to the asymmetry of information among enterprises, it is difficult for them to maximize efficiency and profits. To maximize the efficiency and profit of the CLSC, this study establishes five cooperation models of CLSC under the government‘s reward–penalty mechanism. We make decisions on wholesale prices, retail prices, transfer payment prices, and recovery rates relying on the Stackelberg game method and compare the optimal decisions. This paper analyzes the impact of the government reward-penalty mechanism on optimal decisions and how members in CLSC choose partners. We find that the government’s reward-penalty mechanism can effectively increase the recycling rate of used products and the total profit of the closed-loop supply chain. According to the calculation results of the models, under the government’s reward-penalty mechanism, the cooperation can improve the CLSC’s used products recycling capacity and profitability. In a supply chain, the more members participate in the cooperation, the higher profit the CLSC obtain. However, the cooperation mode of all members may lead to monopoly, which is not approved by government and customers.

Robust Hazardous Materials Closed-Loop Supply Chain Network Design with Emergency Response Teams Location

2021 ◽  
pp. 036119812199207
Author(s):  
Nasrin Mohabbati-Kalejahi ◽  
Alexander Vinel
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Emergency Response ◽  
Closed Loop ◽  
Hazardous Materials ◽  
Risk Exposure ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Distribution Centers ◽  
Closed Loop Supply Chain

Hazardous materials (hazmat) storage and transportation pose threats to people’s safety and the environment, which creates a need for governments and local authorities to regulate such shipments. This paper proposes a novel mathematical model for what is termed the hazmat closed-loop supply chain network design problem. The model, which can be viewed as a way to combine several directions previously considered in the literature, includes two echelons in the forward direction (production and distribution centers), three echelons in the backward direction (collection, recovery, and disposal centers), and emergency response team positioning. The two objectives of minimizing the strategic, tactical, and operational costs as well as the risk exposure on road networks are considered in this model. Since the forward flow of hazmat is directly related to the reverse flow, and since hazmat accidents can occur at all stages of the lifecycle (storage, shipment, loading, and unloading, etc.), it is argued that such a unified framework is essential. A robust framework is also presented to hedge the optimization model in case of demand and return uncertainty. The performance of both models is evaluated based on a standard dataset from Albany, NY. Considering the trade-offs between cost and risk, the results demonstrate the design of efficient hazmat closed-loop supply chain networks where the risk exposure can be reduced significantly by employing the proposed models.

Multi-Period Multi-Product Closed Loop Supply Chain Network Design: A Relaxation Approach

2021 ◽  
pp. 107191
Author(s):  
Subramanian Pazhani ◽  
Abraham Mendoza ◽  
Ramkumar Nambirajan ◽  
T.T. Narendran ◽  
K. Ganesh ◽  
...  
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Closed Loop ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Closed Loop Supply Chain
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