Attitude data-based deep hybrid learning architecture for intelligent fault diagnosis of multi-joint industrial robots

Decision-making and coordination of green closed-loop supply chain with fairness concern

Feed-in tariff or tax-rebate regulation? Dynamic decision model for the solar photovoltaic supply chain

Whether the upstream and downstream members in a supply chain (considering environmental objectives) simultaneously stabilize economic benefits has become an important problem in the process of green development. However, few quantitative studies on green supply chains have considered environmental and economic benefits to realize multi-objective optimization. To study operation and cooperation strategies with a consideration of the different objective on the level of supply chain, we first establish a green supply chain game model with profit and environment objectives simultaneously considered by the manufacturer. Then, we analyze the multi-objective decisions of the supply chain members under centralized control using a manufacturer-led Stackelberg game and revenue-sharing contract. Using the manufacturer’s environmental preference as a variable, the effects of environmental benefits on the supply chain are also investigated. Finally, this study determines that the manufacturer’s profit will be reduced after considering the objective of environmental benefits, while the retailer’s profit, product greenness, and environmental benefits will be improved. Meanwhile, the total profit of the green supply chain will first increase and then decrease. In particular, a revenue-sharing contract can facilitate the coordination of multiple objectives; in this way, both the manufacturer and the retailer achieve higher profits and environmental benefits compared to a decentralized control condition, which is of great significance in achieving a win–win situation for the economy and the environment.

A Multi-Objective Optimization Model for Green Supply Chain Considering Environmental Benefits

The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.,A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naive Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.,The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.,The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.,Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

What attracts vehicle consumers’ buying

Taking an environment-friendly green closed-loop supply chain as the research object, this work established a two-stage closed-loop supply chain game model. Considering the influence of the environmental protection input on the whole supply chain, there are different decisions among the participants in the supply chain, and the different choices will have impacts on the benefits of the whole supply chain when manufacturers select a closed-loop supply chain model of third-party recycling. Hence, this work compared and analyzed the impact of centralized decision-making and decentralized decision-making on the returns and pricing strategies of each participant. Finally, an optimized cooperative mechanism decision model considering a cost profit sharing contract was further designed. The model is conducive to obtaining the maximum profit value in centralized decision-making and avoids the negative impact of a “double marginal effect” on supply chain income in decentralized decision-making, and finally, improves the overall coordination and profit of a green closed-loop supply chain. The numerical examples are conducted to verify the effectiveness and practicality of the proposed models. This work provides a helpful decision support and guidance for enterprises and the government on the used products recycling decisions to better manage the green closed-loop supply chain.

https://www.mdpi.com/2071-1050/11/19/5335/pdf

Jie Zhang

Papers

A Green Closed-Loop Supply Chain Coordination Mechanism Based on Third-Party Recycling