Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand

Noh, Jiseong; Kim, Jong Soo

doi:10.1016/j.jclepro.2018.10.124

Cited by 55 publications

(31 citation statements)

References 39 publications

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“…One possible strategy for business firms is to commit to environmental issues by focusing on either internal or external organization [27]. From external organization perspective, suppliers play an important role in building capability and competitive advantage for firms because they provide key materials and components for firms [28]. In order for firms to fulfill external stakeholders' expectations, firms may seek and collaborate with different suppliers to solve environmental issues [29].…”

Section: Environmental Orientation and Gscmmentioning

confidence: 99%

Environmental Orientation, Green Supply Chain Management, and Firm Performance: Empirical Evidence from Chinese Small and Medium-Sized Enterprises

Dang

Wang

et al. 2020

IJERPH

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This study investigates the relationship between environmental orientation and firm performance with the mediating role of green supply chain management (GSCM). This study uses a survey questionnaire to collect data from 247 CEOs from Chinese small and medium-sized enterprises (SMEs). Structural equation modeling is used to analyze data and test hypotheses. Empirical results show that internal and external environmental orientations are positively related to the three elements of GSCM, namely, environmental selection, monitoring, and collaboration with suppliers which are also positively related to firm performance. In addition, results show that environmental selection, monitoring, and collaboration with suppliers mediates the relationship between internal and external environmental orientations and firm performance. The findings provide important implications for academic researchers and business managers in planning and implementing environmental strategies. In terms of theoretical implications, this study sheds a new light to current knowledge about the effect of environmental orientation on GSCM and firm performance of SMEs. This study also provides empirical evidence to clarify the mediating mechanism of GSCM in the link between environmental orientation and firm performance of SMEs. In terms of practical implications, this study provides knowledge for managers of SMEs to better understand the important role of environmental orientation and green supply chain management. Findings of this study provide knowledge for managers of SMEs to make their business policies better.

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Section: Environmental Orientation and Gscmmentioning

confidence: 99%

Environmental Orientation, Green Supply Chain Management, and Firm Performance: Empirical Evidence from Chinese Small and Medium-Sized Enterprises

Dang

Wang

et al. 2020

IJERPH

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“…GA is one of the famous meta heuristic algorithms based on a stochastic optimization method. The major benefit of GA is to obtain an optimal or a near optimal solution of a large scale problem under reasonable computing time [28]. Holland [29] developed GA based on Darwin's The Origin of Species, so it utilizes natural evolution.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management

et al. 2020

Self Cite

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Product demand forecasting plays a vital role in supply chain management since it is directly related to the profit of the company. According to companies’ concerns regarding product demand forecasting, many researchers have developed various forecasting models in order to improve accuracy. We propose a hybrid forecasting model called GA-GRU, which combines Genetic Algorithm (GA) with Gated Recurrent Unit (GRU). Because many hyperparameters of GRU affect its performance, we utilize GA that finds five kinds of hyperparameters of GRU including window size, number of neurons in the hidden state, batch size, epoch size, and initial learning rate. To validate the effectiveness of GA-GRU, this paper includes three experiments: comparing GA-GRU with other forecasting models, k-fold cross-validation, and sensitive analysis of the GA parameters. During each experiment, we use root mean square error and mean absolute error for calculating the accuracy of the forecasting models. The result shows that GA-GRU obtains better percent deviations than other forecasting models, suggesting setting the mutation factor of 0.015 and the crossover probability of 0.70. In short, we observe that GA-GRU can optimally set five types of hyperparameters and obtain the highest forecasting accuracy.

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“…In the above analysis, we can see that in the joint inventory management, the manufacturer's cost decreases while the retailer's cost increases [25]. Therefore, in the process of realizing the optimization of supply chain, we must consider the redistribution of the interests of both the supplier and the demander, so that the manufacturer can reduce the cost of the retailer, and at the same time, reduce or keep the cost of the retailer unchanged.…”

Section: The Profit Distribution Between Manufacturer and Retailer Inmentioning

confidence: 99%

Supply Chain Joint Inventory Management and Cost Optimization Based on Ant Colony Algorithm and Fuzzy Model

Hou

Xia

et al. 2019

Teh. vjesn.

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With the advancement of the marketization process, inventory management has transformed from a single backup protection function to an essential function for enterprises, which helps to survive and develop. Inventory control in supply chain management is the important content of supply chain management. The new management mode makes inventory management present many new characteristics and problems compared with traditional inventory management. From the view of system theory and integration theory, it is imperative to reexamine the problem of inventory control, put forward new inventory management strategies adapted to integrated supply chain management, and improve the integration of the whole supply chain, which can enhance the agility and market response speed of enterprises. Based on the in-depth study of the joint inventory management model, this paper analyzed the current situation of the joint inventory management to optimize the inventory. In view of the achievements and shortcomings of the current research, a more systematic and improved optimization model of the supply chain inventory was proposed by using the basic ideas of ant colony algorithm and fuzzy model.

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Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand

Cited by 55 publications

References 39 publications

Environmental Orientation, Green Supply Chain Management, and Firm Performance: Empirical Evidence from Chinese Small and Medium-Sized Enterprises

Environmental Orientation, Green Supply Chain Management, and Firm Performance: Empirical Evidence from Chinese Small and Medium-Sized Enterprises

Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management

Supply Chain Joint Inventory Management and Cost Optimization Based on Ant Colony Algorithm and Fuzzy Model

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