Water Environment Random Evaluation Model based on the improved TOPSIS method and Bayesian Theory and its Application

2019 ◽  
Vol 46 (3) ◽  
pp. 344-352
Author(s):  
Jinchao Xu ◽  
Yaqian Chen ◽  
Jun Zhao ◽  
Qingfeng Hang ◽  
Xuechun Li
2009 ◽  
Vol 29 (10) ◽  
pp. 2849-2851
Author(s):  
Li-lun ZHANG ◽  
Jian-ping WU ◽  
Jun-qiang SONG

2020 ◽  
pp. 1-11
Author(s):  
Guo Yunfeng ◽  
Li Jing

In order to improve the effect of the teaching method evaluation model, based on the grid model, this paper constructs an artificial intelligence model based on the grid model. Moreover, this paper proposes a hexahedral grid structure simplification method based on weighted sorting, which comprehensively sorts the elimination order of candidate base complexes in the grid with three sets of sorting items of width, deformation and price improvement. At the same time, for the elimination order of basic complex strings, this paper also proposes a corresponding priority sorting algorithm. In addition, this paper proposes a smoothing regularization method based on the local parameterization method of the improved SLIM algorithm, which uses the regularized unit as the reference unit in the local mapping in the SLIM algorithm. Furthermore, this paper proposes an adaptive refinement method that maintains the uniformity of the grid and reduces the surface error, which can better slow down the occurrence of geometric constraints caused by insufficient number of elements in the process of grid simplification. Finally, this paper designs experiments to study the performance of the model. The research results show that the model constructed in this paper is effective.


Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Zhiru Li ◽  
Wei Xu ◽  
Huibin Shi ◽  
Qingshan Zhang ◽  
Fengyi He

Combined with the research of mass customization and cloud manufacturing mode, this paper discussed the production planning of mass customization enterprises in the context of cloud manufacturing in detail, analyzed the attribute index of manufacturing resource combination, and given a system considering the characteristics of batch production in mass customization and the decentralization of manufacturing resources in cloud manufacturing environment. Then, a multiobjective optimization model has been constructed according to the product delivery date, product cost, and product quality that customers care most about. The Pareto solution set of production plan has been obtained by using NSGA-II algorithm. This paper established a six-tier attribute index system evaluation model for the optimization of production planning scheme set of mass customization enterprises in cloud manufacturing environment. The weight coefficients of attribute indexes were calculated by combining subjective and objective weights with analytic hierarchy process (AHP) and entropy weight method. Finally, the combined weights calculated were applied to the improved TOPSIS method, and the optimal production planning scheme has been obtained by ranking. This paper validated the effectiveness and feasibility of the multiobjective model and NSGA-II algorithm by the example of company A. The Pareto effective solution has been obtained by solving the model. Then the production plan set has been sorted synthetically according to the comprehensive evaluation model, and the optimal production plan has been obtained.


Author(s):  
Xiaokun Wang ◽  
Dong Ni

To scientifically and reasonably evaluate and pre-warn the congestion degree of subway transfer hub, and effectively know the risk of subway passengers before the congestion time coming. We analyzed the passenger flow characteristics of various service facilities in the hub. The congested area of the subway passenger flow interchange hub is divided into queuing area and distribution area. The queuing area congestion evaluation model selects M/M/C and M/G/C based on queuing theory. The queuing model and the congestion evaluation model of the distribution area select the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. Queue length and waiting time are selected as the evaluation indicators of congestion in the queuing area, and passenger flow, passenger flow density and walking speed are selected as the evaluation indicators of congestion in the distribution area. And then, K-means cluster analysis method is used to analyze the sample data, and based on the selected evaluation indicators and the evaluation model establishes the queuing model of the queuing area and the TOPSIS model of the collection and distribution area. The standard value of the congestion level of various service facilities and the congestion level value of each service facility obtained from the evaluation are used as input to comprehensively evaluate the overall congestion degree of the subway interchange hub. Finally we take the Xi’an Road subway interchange hub in Dalian as empirical research, the data needed for congestion evaluation was obtained through field observations and questionnaires, and the congestion degree of the queue area and the distribution area at different times of the workday was evaluated, and the congestion of each service facility was evaluated. The grade value is used as input, and the TOPSIS method is used to evaluate the degree of congestion in the subway interchange hub, which is consistent with the results of passenger congestion in the questionnaire, which verifies the feasibility of the evaluation model and method.


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