Performance evaluation of manufacturing collaborative logistics based on BP neural network and rough set

Gao, Jianli

doi:10.1007/s00521-020-05099-9

Cited by 35 publications

(20 citation statements)

References 14 publications

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“…Intelligent technology [23][24][25] is one of the main tools to achieve risk evaluation and risk management. Quantitative analysis can visually measure the risk level, but the quality of data required for quantitative analysis directly affects the credibility of risk evaluation, and it is difficult to achieve quantitative evaluation if there is insufficient data [26]. Qualitative analysis, on the other hand, is an empirical judgment of indicators, which can be used as a reference for quantitative analysis, but care should be taken to avoid the influence of its subjectivity as much as possible.…”

Section: : ð2þmentioning

confidence: 99%

[Retracted] Construction and Analysis of Green Investment Risk Evaluation Index System Based on Information Entropy Fuzzy Hierarchical Analysis Model

Huang

2021

Wireless Communications and Mobile Computing

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This paper adopts the information entropy fuzzy hierarchical analysis model to conduct an in-depth study and analysis of the green investment risk evaluation system and to process and analyze its indicators. The entropy method-fuzzy hierarchical analysis is used to evaluate the green financial capital operation risk. Firstly, I point out the research trends and shortcomings, define the concepts of fund operation, operation risk, risk evaluation, and green finance, and elaborate the relevant basic theories to lay the theoretical foundation for the later research; secondly, I identify the external and internal fund operation risks by describing the development plan, overall operation, and specific operation plan of green finance; then, according to the existing fund operation risks, I integrate commercial banks’ risk. Then, based on the existing fund operation risks, I integrate the risk types of commercial banks, combine the characteristics of green finance business, design the green finance fund operation risk evaluation index system, initially determine the index weights through hierarchical analysis method, introduce the entropy value method to improve the index weights, and use the fuzzy comprehensive evaluation method to obtain the final evaluation results: in the process of green finance fund operation, internal risk is the most important risk, among which credit risk and financial innovation risk have the most significant impact; external risk is the second most important risk, among which economic risk and political risk are the most important. External risks are the second most important risks, among which economic risks and political risks are more obvious. The risks are judged from single-factor aspects, and the scoring values are used to determine the higher possibility of schedule risks caused by human factors, and finally, the schedule risk level of the reservoir project is determined to be medium through a comprehensive evaluation. The reasonableness of the calculation results shows that the entropy theory and the fuzzy comprehensive evaluation method are still applicable to the assessment of green investment risks.

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Section: : ð2þmentioning

confidence: 99%

[Retracted] Construction and Analysis of Green Investment Risk Evaluation Index System Based on Information Entropy Fuzzy Hierarchical Analysis Model

Huang

2021

Wireless Communications and Mobile Computing

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“…e current global economic environment is unpredictable, and increased enterprises are putting increased attention on their financial risk prediction and analysis to ensure sustainable and healthy development. Effective financial risk prediction can not only sound the alarm for enterprises and adjust their financial risk level promptly but also help enterprise managers to manage their business better and make reasonable decisions to avoid financial crises and make their development [2]. It can also help enterprise managers make better management and rational decisions, avoid financial crises, and make enterprise development more long-term, smooth, and healthy.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Risk Prediction and Response Strategies in Corporate Financial Management Based on Optimized BP Neural Network

Zhai¹

2021

Complexity

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This paper mainly analyzes the theories related to the financial risk of the company and combines the principles of principal component analysis, particle swarm optimization algorithm, and artificial neural network to derive the financial risk index system of the company. To improve the accuracy of financial risk prediction, principal component analysis and particle swarm algorithm are applied to optimize the BP neural network model, the input data of the prediction model is improved, and the optimal initial weights and thresholds are given to the BP neural network by using particle swarm algorithm search, whereby the financial risk prediction model of particle swarm optimization BP neural network is constructed. The empirical results show that the model constructed by BP neural network not only has a high accuracy rate for static financial risk evaluation but also has a better prediction effect. After training and testing, the BP neural network-based enterprise financial risk evaluation model can accurately determine the existing financial situation of enterprise financial management and has a good prediction effect. Our research method is a fusion of the processing of the two methods, which belongs to the first integration of results.

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“…KPIs in the manufacturing system have been listed in previous research [8], and there are various subcategories, including availability, utilization, and throughput, which were measured in this study. Evaluation of such performance evaluation factors in the manufacturing system is common, and the evaluation result can be used to identify the importance order of those factors when used to evaluate existing systems [9].…”

Section: Introductionmentioning

confidence: 99%

Production Flow Analysis in a Semiconductor Fab Using Machine Learning Techniques

Singgih

2021

Processes

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In a semiconductor fab, wafer lots are processed in complex sequences with re-entrants and parallel machines. It is necessary to ensure smooth wafer lot flows by detecting potential disturbances in a real-time fashion to satisfy the wafer lots' demands. This study aims to identify production factors that significantly affect the system’s throughput level and find the best prediction model. The contributions of this study are as follows: (1) this is the first study that applies machine learning techniques to identify important real-time factors that influence throughput in a semiconductor fab; (2) this study develops a test bed in the Anylogic software environment, based on the Intel minifab layout; and (3) this study proposes a data collection scheme for the production control mechanism. As a result, four models (adaptive boosting, gradient boosting, random forest, decision tree) with the best accuracies are selected, and a scheme to reduce the input data types considered in the models is also proposed. After the reduction, the accuracy of each selected model was more than 97.82%. It was found that data related to the machines’ total idle times, processing steps, and machine E have notable influences on the throughput prediction.

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Performance evaluation of manufacturing collaborative logistics based on BP neural network and rough set

Cited by 35 publications

References 14 publications

[Retracted] Construction and Analysis of Green Investment Risk Evaluation Index System Based on Information Entropy Fuzzy Hierarchical Analysis Model

[Retracted] Construction and Analysis of Green Investment Risk Evaluation Index System Based on Information Entropy Fuzzy Hierarchical Analysis Model

[Retracted] Risk Prediction and Response Strategies in Corporate Financial Management Based on Optimized BP Neural Network

Production Flow Analysis in a Semiconductor Fab Using Machine Learning Techniques

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