A model combining a Bayesian network with a modified genetic algorithm for green supplier selection

SIMULATION ◽  
2019 ◽  
Vol 95 (12) ◽  
pp. 1165-1183 ◽  
Author(s):  
Hao Zhang ◽  
Yan Cui
Keyword(s):  
Genetic Algorithm ◽  
Bayesian Network ◽  
Evaluation System ◽  
Improved Genetic Algorithm ◽  
Selection Scheme ◽  
Evaluation Indexes ◽  
Agricultural Modernization ◽  
Model Combining ◽  
Agricultural Means Of Production ◽  
Green Suppliers

With the advancement of agricultural modernization, many suppliers of agricultural means of production have delivery delay problems and have created environmental pollution and other issues, which affect the coordination and overall efficiency of the agricultural supply chain. Focusing on the green suppliers, this paper puts forward a series of evaluation indexes and considers the influence of environmental performance for performing uncertainty event reasoning based on a Bayesian network – establishing a complete selection and evaluation system for retail enterprises and downstream customers. In addition, an improved genetic algorithm is combined with the Bayesian approach to quantify the evaluation indicators, which solves the problems of the traditional methods of information occlusion and an unreasonable selection scheme, and provides an intelligent and efficient selection of green suppliers.

scholarly journals Optimal Sensor Placement for Health Monitoring of High-Rise Structure Based on Genetic Algorithm

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Ting-Hua Yi ◽  
Hong-Nan Li ◽  
Ming Gu
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Large Scale ◽  
Sensor Placement ◽  
Optimal Placement ◽  
Improved Genetic Algorithm ◽  
Optimal Sensor Placement ◽  
Selection Scheme ◽  
Dual Structure ◽  
Coding Method

Optimal sensor placement (OSP) technique plays a key role in the structural health monitoring (SHM) of large-scale structures. Based on the criterion of the OSP for the modal test, an improved genetic algorithm, called “generalized genetic algorithm (GGA)”, is adopted to find the optimal placement of sensors. The dual-structure coding method instead of binary coding method is proposed to code the solution. Accordingly, the dual-structure coding-based selection scheme, crossover strategy and mutation mechanism are given in detail. The tallest building in the north of China is implemented to demonstrate the feasibility and effectiveness of the GGA. The sensor placements obtained by the GGA are compared with those by exiting genetic algorithm, which shows that the GGA can improve the convergence of the algorithm and get the better placement scheme.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals Subjective Product Evaluation System Based on Kansei Engineering and Analytic Hierarchy Process

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1340 ◽  
Author(s):  
Yaxue Zuo ◽  
Zhenya Wang
Keyword(s):  
Evaluation System ◽  
Comprehensive Evaluation ◽  
Subjective Evaluation ◽  
Reliability And Validity ◽  
Objective Evaluation ◽  
Product Evaluation ◽  
Kansei Engineering ◽  
Analytic Hierarchy ◽  
Evaluation Indexes ◽  
Hierarchy Process

Product evaluation is very important for product improvement and development, and subjective product evaluation determines customer’s evaluation of products to some extent, so the purpose of this study is to establish a reasonable subjective product evaluation system. In this study, we comprehensively determine the evaluation indexes based on Kansei engineering (KE), establish an overall product evaluation system by using analytic hierarchy process (AHP), and establish the subjective product evaluation system by classifying the evaluation indexes in the overall product evaluation system into “objective evaluation index” and “subjective evaluation index”, removing the objective evaluation indexes, and retaining the subjective evaluation indexes. Additionally, we select some modern chairs as experimental samples to verify the reliability and validity of this subjective product evaluation system by means of questionnaires. The experimental results show that, in this subjective product evaluation system, the subjective evaluation of the product is positively correlated with the “favorite” level of the product in comprehensive evaluation, and negatively correlated with the “least favorite” level of the product in comprehensive evaluation, indicating that this subjective product evaluation system realizes a symmetry between subjective product evaluation and comprehensive product evaluation. Therefore, it can be concluded that this subjective product evaluation system based on KE and AHP proposed in this study has reliability and validity, and can be used for product evaluation to judge the popularity of products and enhance the competitiveness of products.

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