An Evaluation Model of Supply Chain Performances Using 5DBSC and LMBP Neural Network Algorithm

Fan, Xue; Zhang, Shujun; Wang, Longzhao; Yang, Yinsheng; Hapeshi, Kevin

doi:10.1016/s1672-6529(13)60234-6

Cited by 27 publications

(24 citation statements)

References 21 publications

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“…One widely used method is the LMBP neural network algorithm, which has an effective training speed. Although this method requires a larger computational environment, it could also serve an effective function [10,49]. Fan et al [10] reported that the LMBP neural network algorithm was a better method for evaluating supply chain performance.…”

Section: (2) Applications Of Neural Networkmentioning

confidence: 99%

“…Although this method requires a larger computational environment, it could also serve an effective function [10,49]. Fan et al [10] reported that the LMBP neural network algorithm was a better method for evaluating supply chain performance. However, the LMBP neural network algorithm had some limitations such as the fact that the optimal solution can easily fall into a local optimum.…”

Section: (2) Applications Of Neural Networkmentioning

confidence: 99%

“…Although many performance indicators [4,5] and methods [6][7][8][9][10] for GSC performance evaluation have been proposed, the bionics method was considered to be the effective one that could be used in different areas. Consequently, many optimization algorithms based on bionics have been proposed, including the Levenberg Marquardt Back Propagation neural network algorithm (LMBP neural network algorithm) [10], the Fuzzy Neural Network [11], the Bees Algorithm [12], the Ant Colony Optimization [13], and a Genetic Algorithm (GA) [14]. Among these algorithms, neural network algorithms have been widely used to evaluate supply chain performance.…”

Section: Introductionmentioning

confidence: 99%

“…Among these algorithms, neural network algorithms have been widely used to evaluate supply chain performance. Among these neural network algorithms, the LMBP neural network algorithm has been demonstrated to be an effective method for evaluating supply chain performance [10]. However, this method easily falls into a local minimum Among the above algorithms, the GA and artificial neural network algorithms have been widely used for evaluating supply chain performance evaluation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Liu¹,

Yi²

2016

Sustainability

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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.

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Section: (2) Applications Of Neural Networkmentioning

confidence: 99%

Section: (2) Applications Of Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Liu¹,

Yi²

2016

Sustainability

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“…In the returning, the weight of neuron connection is revised one by one. This process is constantly iterative and finally will make the signal error into a permitted range [16].…”

Section: Condition Attribute Decision Attributementioning

confidence: 99%

Study on the Evaluation for the Knowledge Sharing Efficiency of the Knowledge Service Network System in Agile Supply Chain

Wang¹,

Wang²,

Sheng³

2015

TOCSJ

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Abstract:The agility of supply chain as well as the competitiveness of the knowledge service network system is affected by knowledge sharing efficiency. In this paper, an evaluation index system for the knowledge sharing efficiency of the knowledge service network system in agile supply chain was constructed in terms of knowledge system, knowledge sharing environment, coordinating actions and sharing results. After evaluation indexes were reduced with rough set theory, a knowledge sharing efficiency evaluation model for the knowledge service network system in agile supply chain was established with error back propagation (BP) artificial neural network, and the network model was simulated with sample data, proving the evaluation result could comprehensively, accurately reflect the actual situation. Thus, the model possesses a very broad application prospect.

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A systematic review of the research trends of machine learning in supply chain management

Xiao

Lim

2019

Int. J. Mach. Learn. & Cyber.

130

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An Evaluation Model of Supply Chain Performances Using 5DBSC and LMBP Neural Network Algorithm

Cited by 27 publications

References 21 publications

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

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

Study on the Evaluation for the Knowledge Sharing Efficiency of the Knowledge Service Network System in Agile Supply Chain

A systematic review of the research trends of machine learning in supply chain management

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