A discrete manufacturing SCOS framework based on functional interval parameters and fuzzy QoS attributes using moving window FPA

2021 ◽  
pp. 1063293X2110323
Author(s):  
Jie Gao ◽  
Xianguo Yan ◽  
Hong Guo
Keyword(s):  
Service Composition ◽  
Evaluation Model ◽  
Good Effect ◽  
Flower Pollination Algorithm ◽  
Moving Window ◽  
Linear Characteristic ◽  
Fitness Value ◽  
Discrete Manufacturing ◽  
Non Linear ◽  
Low Efficiency

Manufacturing service composition and optimal selection (SCOS) is a key technology that improves resource utilization and reduces the cost in discrete manufacturing. However, the lack of evaluation of the service composition function and the unconformity of the actual composition vague characteristics, resulting in the incomplete evaluation of the service composition. Additionally, various optimization and selection algorithms have defects of premature convergence and low efficiency. At the same time, the fitness value distribution of the service composition has a non-linear characteristic. In this article, a framework called discrete manufacturing SCOS (DMSCOS) is proposed to overcome these issues. DMSCOS uses the functional interval parameter and fuzzy QoS attribute aware evaluation model (FIPFQA) to achieve composition evaluation and introduces a moving window flower pollination algorithm (MWFPA) to achieve optimization and selection for the non-linear characteristic population. Experiments show that DMSCOS has good performance for optimization and selection. The FIPFQA has a good effect on service composition evaluation. Furthermore, compared with two other extended algorithms, the proposed MWFPA performs better when addressing the optimal and selection problem.

scholarly journals A Manufacturing SCOS Model (MSCOS) Based on the Similarity of Parameter Sequences Between Tasks and Service Composition

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jie Gao ◽  
Hong Guo ◽  
Xianguo Yan
Keyword(s):  
Service Composition ◽  
Evaluation Model ◽  
Cloud Manufacturing ◽  
Flower Pollination Algorithm ◽  
Optimal Selection ◽  
Correlation Degree ◽  
Order Preference ◽  
Parameter Sequence ◽  
Core Issue ◽  
Grey Correlation Degree

AbstractService composition and optimal selection (SCOS) is a core issue in cloud manufacturing (CMfg) when integrating distributed manufacturing services for complex manufacturing tasks. Generally, a set of recommended task parameter sequences (Tps) will be given when publishing manufacturing tasks. The similarity between the service composition parameter sequence (SCps) and Tps also reflects the rationality of the service composition. However, various evaluation models based on QoS have been proposed, ignoring the rationality between the Tps and SCps. Considering the similarity of the Tps and SCps in an evaluation model, we propose a manufacturing SCOS framework called MSCOS. The framework includes two parts: an evaluation model and an algorithm for both optimization and selection. In the evaluation model, based on the numerical proximity and geometric similarity between the Tps and SCps, improving the technique for order preference by similarity to an ideal solution (TOPSIS) with the grey correlation degree (GC), we propose the GC&TOPSIS (GTOPSIS). In the optimization and selection algorithm, an improved flower pollination algorithm (IFPA) is proposed to achieve optimization and selection based on polyline characteristics between the fitness values in the population. Experiments show that the MSCOS evaluation effect and optimal selection offer better performance than commonly used algorithms.

Comprehensive evaluation of logistics enterprise competitiveness based on SEM model

2020 ◽  
pp. 1-11
Author(s):  
Yang Yaxu
Keyword(s):  
Latent Variables ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Value Added ◽  
Learning Technology ◽  
Logistics Enterprise ◽  
Logistics Mode ◽  
Enterprise Competitiveness ◽  
Low Efficiency ◽  
Enterprise Logistics

The loose logistics market, the weak value-added service capabilities of enterprises, and the backward construction and operation of logistics networks have led to high logistics costs and low efficiency in some enterprises. In order to improve the comprehensive evaluation effect of enterprise logistics enterprise competitiveness, this paper builds a comprehensive evaluation model of logistics enterprise competitiveness based on SEM model based on machine learning technology. Moreover, in order to more accurately grasp the law of customer logistics mode selection behavior, this paper adds the adaptive value of the latent variables of the logistics mode service characteristics obtained through the SEM model to the utility function of the logistics mode to obtain the SEM-NL integrated model. In addition, starting from the analysis of the key factors affecting the competitiveness of enterprise logistics, this paper constructs an evaluation model of enterprise logistics competitiveness, and analyzes and studies the comprehensive competitiveness of enterprise logistics through two aspects of logistics actual competitiveness and logistics future development potential. The research results show that the model constructed in this paper is suitable for the comprehensive evaluation of the competitiveness of logistics enterprises.

Autonomous evolution of service system in cluster supply chain

Kybernetes ◽  
2015 ◽  
Vol 44 (1) ◽  
pp. 139-158
Author(s):  
Xiao Xue ◽  
Shufang Wang ◽  
Hao Chao
Keyword(s):  
Supply Chain ◽  
Service Composition ◽  
Service System ◽  
Evaluation Model ◽  
Service Component ◽  
Evolution Mechanism ◽  
Content Type ◽  
External Customer ◽  
Qos Evaluation ◽  
Autonomous Evolution

Purpose – The purpose of this paper is to provide strong theoretical and technical support for the dynamic evolution of service system in “Cluster Supply Chain”(CSC), which can deal with two kinds of context changes: the internal service component changes and the external customer requirement changes. Design/methodology/approach – A “feedback-based” evolution mechanism of service system for CSC is proposed in this study. By means of the feedback update of enterprise service’s Quality of Service (QoS) attribute and the adjustment of the assumed QoS evaluation model, the evolution of service system can be achieved to suit the dynamic market demands. Findings – Results of the study suggest: by means of the “feed-back” evolution mechanism of service system, the enterprises in CSC can handle the context changes effectively to maintain the optimized operation status. Practical implications – The implementation of evolution mechanism in service system can keep the effectiveness of enterprise service composition to face the frequent service component changes and the unpredictable market turbulence. Originality/value – This paper proposes a method to realize the autonomous evolution of service system in CSC, which can support the flexibility and adaptability of enterprise service composition in the changing environment.

scholarly journals Improved Set Pair Analysis and Its Application to Environmental Impact Evaluation of Dam Break

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 821 ◽  
Author(s):  
Meimei Wu ◽  
Wei Ge ◽  
Zongkun Li ◽  
Zening Wu ◽  
Hexiang Zhang ◽  
...  
Keyword(s):  
Environmental Impact ◽  
Impact Evaluation ◽  
Evaluation Model ◽  
Rapid Development ◽  
Dam Break ◽  
Good Effect ◽  
Set Pair Analysis ◽  
Management Measures ◽  
The Index System ◽  
Pair Analysis

Despite the rapid development of risk analysis in dam engineering, there is a relative absence of research on the environmental impact of dam break. As a systematic theory, set pair analysis has a good effect in dealing with uncertainties, although the result is relatively rough and easy to distort. A connection degree of five grades and a generalized set of potential are introduced to improve traditional set pair analysis. Combined with the index system, the evaluation model of the environmental impact of dam break is established, which is based on generalized set pair analysis. Taking Sheheji Reservoir dam as an example, a comparison of evaluation results of fuzzy mathematics theory and generalized set pair analysis is made, which verifies the scientificity and practicability of the method proposed in this paper. The results show that the evaluation grade of the environmental impact of dam break at Sheheji Reservoir is serious, and appropriate management measures should be taken to reduce the risk.

Non-Linear Control of a Neutralisation Process

1971 ◽  
Vol 4 (9) ◽  
pp. T151-T157 ◽  
Author(s):  
P D Roberts
Keyword(s):  
Simulation Study ◽  
Digital Simulation ◽  
Linear Control ◽  
Attractive Alternative ◽  
Error Signal ◽  
Linear Controller ◽  
Linear Characteristic ◽  
Non Linear ◽  
On Line ◽  
Extra Parameter

The paper describes a digital simulation study of the application of a non-linear controller to the regulation of a single stage neutralisation process. In the controller, the proportional gain increases with amplitude of controller error signal. The performance of the non-linear controller is compared with that of a conventional linear controller and with the performance obtained by employing a linear controller with a linearisation network designed to compensate for the non-linear characteristic of the neutralisation curve. Although the performance of the non-linear controller is inferior to that obtained by employing a perfect linearisation network, its performance is still considerably superior to that obtained by using a conventional linear controller when operating at a symmetrical point on the neutralisation curve. In contrast to the linearisation network technique, the non-linear controller contains only one extra parameter and can be readily tuned on-line without prior knowledge of the neutralisation curve. Hence, it can be considered as an attractive alternative for the control of neutralisation processes.

An AI based design of student performance prediction and evaluation system in college physical education

2020 ◽  
pp. 1-9
Author(s):  
Zhang Yangsheng
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Physical Education ◽  
Student Performance ◽  
Internal Structure ◽  
Process Evaluation ◽  
Performance Prediction ◽  
Evaluation System ◽  
Evaluation Model ◽  
Good Effect

College physical education is too one-sided, which makes the teaching process evaluation meaningless. Based on this, based on neural network technology, this article combines artificial intelligence teaching system to build an artificial intelligence sports teaching evaluation model based on neural network. The artificial intelligence model starts from the process evaluation and the final evaluation. Moreover, it uses a recurrent neural network for data training and analysis, and introduces a new decoder to perform data processing, and introduces a simplified gated neural network internal structure diagram to build the internal structure of the model.In addition, this study designs a control experiment to evaluate the performance of the model constructed in this study. The research results show that the artificial intelligence model constructed in this paper has a good effect in the performance prediction and evaluation of college sports students.

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