New Algorithm for Evaluating the Green Supply Chain Performance in an Uncertain Environment

Abstract

An effective green supply chain (GSC) can help an enterprise obtain more benefits and reduce costs. Therefore, developing an effective evaluation method for GSC performance evaluation is becoming increasingly important. In this study, the advantages and disadvantages of the current performance evaluations and algorithms for GSC performance evaluations were discussed and evaluated. Based on these findings, an improved five-dimensional balanced scorecard was proposed in which the green performance indicators were revised to facilitate their measurement. A model based on Rough Set theory, the Genetic Algorithm, and the Levenberg Marquardt Back Propagation (LMBP) neural network algorithm was proposed. Next, using Matlab, the Rosetta tool, and the practical data of company F, a case study was conducted. The results indicate that the proposed model has a high convergence speed and an accurate prediction ability. The credibility and effectiveness of the proposed model was validated. In comparison with the normal Back Propagation neural network algorithm and the LMBP neural network algorithm, the proposed model has greater credibility and effectiveness. In practice, this method provides a more suitable indicator system and algorithm for enterprises to be able to implement GSC performance evaluations in an uncertain environment. Academically, the proposed method addresses the lack of a theoretical basis for GSC performance evaluation, thus representing a new development in GSC performance evaluation theory.

Coupling System Dynamics Model of Cross Border Logistics and Ecological Environment Based on the Sustainable Perspective of Global Value Chain

“…For example, Kaur and Singh (2020) proposed to reduce the negative impact on the environment by promoting environmental protection measures such as energy conservation, emission reduction, and resource recycling [39]. Liu and Yi (2016) propose to further promote the application of green technologies, such as the adoption of advanced energy-saving and emission-reduction technologies by logistics companies, the use of clean energy, the promotion of electric and hybrid vehicles, etc., to reduce carbon emissions and pollution during distribution [40]. In addition, integrating different modes of transportation will minimize the impact on the ecological environment.…”

The effect of green supply chain practices on the firm performance: an empirical research

“…In relation to firm performance, some experimental research has been done on green supply chain management only in recent years. An algorithm was developed to evaluate the green supply chain management performance in an uncertain environment; in 2017, the pricing policies of green supply chain management were analyzed in a big data environment [38].…”

Oportunidades para la transformación digital de la cadena de suministro del sector bananero basado en software con inteligencia artificial

“…Una correcta selección y clasificación de los recursos empleados en los procesos logísticos, son de vital importancia a la hora de medir el rendimiento de las cadenas de suministro, es así que en [68] plantean que las RNA son apropiadas para la selección de proveedores de acuerdo un conjunto de datos históricos de cumplimiento. Así mismo, es posible lograr una evaluación de los proveedores utilizando datos de aprendizaje que permitan una correcta toma de decisiones [69,70,71] y agrupamiento de proveedores por medio de identificación de patrones [72,73].…”