Air material inventory optimization model based on genetic algorithm

Author(s):  
Jinmei Liu ◽  
Hui Gao ◽  
Jun Wang
2012 ◽  
Vol 6-7 ◽  
pp. 566-570
Author(s):  
Yang Liu

Electronic commerce has rapidly become a major player in the business market .This paper proposes a new electronic commerce negotiation optimization model based on improved genetic algorithm which depends on not only price, but also other factors of commodity. The proposed model illustrates the relationship between the business components required to support the e-commerce processes with the value creation factor and the controlling complexity. The experiment results show that the proposed algorithm can gain the optimal negotiation result more efficiently than other three kinds of negotiation algorithms in competitive bilateral multi-issue negotiation.


Author(s):  
Jianying Li ◽  
Zhe Zhou ◽  
Liying Wang

Multi-task combinatorial optimization of a complex system is an important aspect of multi-task planning. To address the existing defects and limitations of the existing multi-task combinatorial optimization methods, the paper proposes a multi-task combinatorial model based on genetic algorithm. As a complex multi-task combinatorial optimization, the curriculum planning for higher education applies to itself the multi-task combinatorial model, which is based on genetic algorithm. Having fully considered such factors as teaching resources distribution, students’ intention and teachers’ intention, the paper designs a more efficient fitness function that has flexibly distributed courses and time in curriculum planning to meet the need of teaching in higher schools. Meanwhile, the paper utilizes specific cases of higher education to verify and analyze the algoritnm, and also carryies out a simulation test under the Matlab environment. The result indicates that the multi-task combinatorial optimization model based on genetic algorithm can relatively significantly optimize curriculum planning of higher education.


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