Next Article in Journal
Adopters and Non-Adopters of Low-Cost Household Latrines: A Study of Corbelled Pit Latrines in 15 Districts of Malawi
Next Article in Special Issue
Sustainable and Resilient Garment Supply Chain Network Design with Fuzzy Multi-Objectives under Uncertainty
Previous Article in Journal
Nitrogen and Sediment Capture of a Floating Treatment Wetland on an Urban Stormwater Retention Pond—The Case of the Rain Project
Previous Article in Special Issue
Implementing Coordinative Contracts between Manufacturer and Retailer in a Reverse Supply Chain
Open AccessArticle

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

by Pan Liu and Shuping Yi *
College of Mechanical Engineering, Chongqing University, Shazheng Road 174, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Sustainability 2016, 8(10), 960; https://doi.org/10.3390/su8100960
Received: 29 April 2016 / Revised: 9 September 2016 / Accepted: 12 September 2016 / Published: 26 September 2016
(This article belongs to the Special Issue Sustainability in Supply Chain Management)
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. View Full-Text
Keywords: green supply chain; performance evaluation; Rough Set; Genetic Algorithm; Levenberg Marquardt Back Propagation green supply chain; performance evaluation; Rough Set; Genetic Algorithm; Levenberg Marquardt Back Propagation
Show Figures

Figure 1

MDPI and ACS Style

Liu, P.; Yi, S. New Algorithm for Evaluating the Green Supply Chain Performance in an Uncertain Environment. Sustainability 2016, 8, 960.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop