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Motivated by the industrial observation that the e-commerce platform marketplaces (e.g., Amazon) are increasingly launching sustainable strategies, this study aims to build an analytical framework to guide managers on making sustainable decisions. This study builds a stylized game-theoretical model in the sustainable supply chain context, where the competitive traditional product manufacturers sell their products through the platform’s marketplace, while the platform decides whether to introduce the green products and the pricing strategy. We find that, when the evaluation difference for the green product is sufficiently low, the introduction of the green product by the platform benefits the manufacturers (or third-party sellers). Interestingly, a higher platform fee makes a higher likelihood of a win-win situation between the platform and manufacturers. Moreover, when consumers value green products sufficiently higher than traditional products, the traditional products’ manufacturers can also benefit from the green product entry.
Junbin Wang; Xuan Gao; Zhiguo Wang. Sustainable Supply Chain Decisions under E-Commerce Platform Marketplace with Competition. Sustainability 2021, 13, 4162 .
AMA StyleJunbin Wang, Xuan Gao, Zhiguo Wang. Sustainable Supply Chain Decisions under E-Commerce Platform Marketplace with Competition. Sustainability. 2021; 13 (8):4162.
Chicago/Turabian StyleJunbin Wang; Xuan Gao; Zhiguo Wang. 2021. "Sustainable Supply Chain Decisions under E-Commerce Platform Marketplace with Competition." Sustainability 13, no. 8: 4162.
Agricultural extension service is the foundation of sustainable agricultural development. The evaluation and analysis of the agricultural extension service for sustainable agricultural development can provide an effective analytical tool for sustainable agriculture. This paper analyzes the influence of agricultural extension service on sustainable agricultural development, and constructs an evaluation system for sustainable agricultural development from the four dimensions of agricultural environment, society, economy, and agricultural extension service. This work proposes a framework based on the combination of technique for order performance by similarity to ideal solution (TOPSIS) and entropy method to evaluate the performance of the evaluation system. Taking three national modern agriculture demonstration zones in Suzhou in Jiangsu Province as a case study, the method was verified. Moreover, the main factors affecting sustainable agricultural development are discussed, and the improvement measures and management suggestions are also put forward to reduce the obstacles to sustainable agricultural development and improve sustainable agriculture practice.
Zhiguo Wang; Junbin Wang; Guoping Zhang; Zhixiong Wang. Evaluation of Agricultural Extension Service for Sustainable Agricultural Development Using a Hybrid Entropy and TOPSIS Method. Sustainability 2021, 13, 347 .
AMA StyleZhiguo Wang, Junbin Wang, Guoping Zhang, Zhixiong Wang. Evaluation of Agricultural Extension Service for Sustainable Agricultural Development Using a Hybrid Entropy and TOPSIS Method. Sustainability. 2021; 13 (1):347.
Chicago/Turabian StyleZhiguo Wang; Junbin Wang; Guoping Zhang; Zhixiong Wang. 2021. "Evaluation of Agricultural Extension Service for Sustainable Agricultural Development Using a Hybrid Entropy and TOPSIS Method." Sustainability 13, no. 1: 347.
Various types of healthcare waste (or medical waste) generated by urban healthcare activities have increased due to the expansion of urban population and medical needs. As healthcare wastes are harmful to both the environment and human health, managing medical waste is becoming progressively more important. Constructing an optimized medical waste recycling network is one of the key problems in the management of urban healthcare waste. This paper conducts a two-stage reverse logistics network design for urban healthcare waste. The first stage involves the prediction of the amount of medical waste. Based on the Grey GM(1,1) prediction model, the amount of medical waste in multi-period of the target hospitals is predicted. In the second stage, a multi-objective model aimed at minimizing operating costs and minimizing environmental impact is developed for facilities allocation decisions, which include the configuration of key facilities such as hospitals, collection centers, transshipment centers, processing centers, and disposal sites, as well as medical waste flow control among facilities. A dynamic approach for the healthcare waste reverse logistics network is constructed by combining the Grey GM(1,1) prediction method with multi-objective optimization model. Sensitivity analysis of key parameters has been performed to analyze their impact on network performance. Some insightful management practices have been revealed.
Zhiguo Wang; Lufei Huang; Cici Xiao He. A multi-objective and multi-period optimization model for urban healthcare waste’s reverse logistics network design. Journal of Combinatorial Optimization 2019, 1 -28.
AMA StyleZhiguo Wang, Lufei Huang, Cici Xiao He. A multi-objective and multi-period optimization model for urban healthcare waste’s reverse logistics network design. Journal of Combinatorial Optimization. 2019; ():1-28.
Chicago/Turabian StyleZhiguo Wang; Lufei Huang; Cici Xiao He. 2019. "A multi-objective and multi-period optimization model for urban healthcare waste’s reverse logistics network design." Journal of Combinatorial Optimization , no. : 1-28.
This study analyzes the effect of multi-attribute decision making (MADM) on the efficiency of the end-of-life vehicle (ELV) reverse logistics industry in the context of the circular economy to improve resource utilization efficiency. In this paper, the DEA-TOPSIS method, based on a prediction model of Triple Exponential Smoothing (TES), is adopted for multi-attribute decision making with a view to improving industry efficiency, Data Envelopment Analysis (DEA) is used to calculate the input and output indicators’ efficiency values and the slack movements of the indicators of input and output decision-making unit’s (DMU’s) base with TES as the decision-making basis. Meanwhile, the Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) is used to rank alternative decision-making schemes. Moreover, the ordering is also carried out using the Additive Weighting, Weighted Product and Elimination et Choice Translating Reality (ELECTRE) method. In this study, the DEA-TOPSIS method is used to make multi-attribute decisions about industry efficiency. Taking Shanghai’s ELV industry as an example, this study utilizes 2017 data from seven member-enterprises of the Shanghai End-of-life Vehicle Professional Committee; it uses the DEA-TOPSIS method based on TES to conduct an empirical study on multi-attribute decision making to improve efficiency and analyze efficiency improvement through alternative decision-making schemes. The findings show that the DEA-TOPSIS method based on TES is effective for multi-attribute decision-making to improve the ELV reverse logistics industry’s efficiency. The multi-attribute decision-making in this paper facilitates the management and investment decision making of the ELV recycling industry. It also provides an effective solution for managers and researchers in the ELV industry to improve its efficiency.
Zhiguo Wang; Hao Hao; Feng Gao; Qian Zhang; Ji Zhang; Yanjun Zhou. Multi-attribute decision making on reverse logistics based on DEA-TOPSIS: A study of the Shanghai End-of-life vehicles industry. Journal of Cleaner Production 2019, 214, 730 -737.
AMA StyleZhiguo Wang, Hao Hao, Feng Gao, Qian Zhang, Ji Zhang, Yanjun Zhou. Multi-attribute decision making on reverse logistics based on DEA-TOPSIS: A study of the Shanghai End-of-life vehicles industry. Journal of Cleaner Production. 2019; 214 ():730-737.
Chicago/Turabian StyleZhiguo Wang; Hao Hao; Feng Gao; Qian Zhang; Ji Zhang; Yanjun Zhou. 2019. "Multi-attribute decision making on reverse logistics based on DEA-TOPSIS: A study of the Shanghai End-of-life vehicles industry." Journal of Cleaner Production 214, no. : 730-737.
This paper aims to better manage the reverse supply chain of the automotive industry in the context of green, circular, and sustainable development by predicting the number of end-of-life vehicles to be recycled through the establishment of a multi-factor model. The prediction of the number of end-of-life vehicles to be recycled in this paper will support the end-of-life vehicle recycling industry in terms of recycling management and investment decision-making and provide a reference for the formulation and implementation of policies relating to end-of-life vehicles. To solve the problems posed by nonlinear characteristics and uncertainty in the number of end-of-life vehicles recycled, and deal with the multiple factors influencing the recycling number, this paper presents a combined prediction model consisting of a grey model, exponential smoothing and an artificial neural network optimized by the particle swarm optimization (PSO) algorithm. Using Shanghai's end-of-life vehicle reverse logistics industry as an example, this study selects historical data about end-of-life vehicles recycled in Shanghai during the 2005-2016 period, identifies multiple influential factors, and validates the effectiveness and feasibility of the prediction model through empirical research. This paper proposes an effective prediction model for end-of-life vehicle industry managers, researchers, and regulators dealing with the industry’s common challenges.
Hao Hao; Qian Zhang; Zhiguo Wang; Ji Zhang. Forecasting the number of end-of-life vehicles using a hybrid model based on grey model and artificial neural network. Journal of Cleaner Production 2018, 202, 684 -696.
AMA StyleHao Hao, Qian Zhang, Zhiguo Wang, Ji Zhang. Forecasting the number of end-of-life vehicles using a hybrid model based on grey model and artificial neural network. Journal of Cleaner Production. 2018; 202 ():684-696.
Chicago/Turabian StyleHao Hao; Qian Zhang; Zhiguo Wang; Ji Zhang. 2018. "Forecasting the number of end-of-life vehicles using a hybrid model based on grey model and artificial neural network." Journal of Cleaner Production 202, no. : 684-696.