The Dynamic Programming Model for Investment Decision of Enterprise

2014 ◽  
Vol 519-520 ◽  
pp. 1468-1471
Author(s):  
Jun Quan Gong ◽  
Xiao Hong Qin
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Programming ◽  
Capital Investment ◽  
Programming Model ◽  
Investment Decision ◽  
Fuzzy Optimization ◽  
Optimal Investment ◽  
Financial Resources ◽  
Risks And Benefits ◽  
Dynamic Programming Model

Enterprise often face to limit financial resources but also have to consider how to invest effectively on a number of projects in the various factors of the risks and benefits in different periods. In order to assure the optimal investment results of capital investment, this paper has established dynamic programming model which is multi-dimensional and multi-objective and fuzzy optimization, dynamic programming and genetic algorithm is combination to solve investment decision of enterprise. At last, this paper through an example to verify the validity of dynamic programming model.

scholarly journals Dynamic Network Planning of Underground Logistics System on Uncertainty Graph

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
YiHua Zhong ◽  
ShiMing Luo ◽  
Min Bao ◽  
XiaoDie Lv
Keyword(s):  
Dynamic Programming ◽  
Programming Model ◽  
Network Planning ◽  
Dynamic Network ◽  
Planning Problem ◽  
Uncertainty Measure ◽  
Uncertain Graph ◽  
Logistics System ◽  
Nanjing City ◽  
Dynamic Programming Model

When designing the underground logistics system, it is necessary to consider the uncertainty of logistics nodes, high cost, and high risk. This paper employed the theories of uncertain graph and dynamic programming to solve the network planning problem of underground logistics system. Firstly, we proposed the concepts of uncertainty measure matrix and vertices structure uncertainty graph by using uncertainty measure and uncertainty graph. Secondly, vertices structure uncertainty graph of the underground logistics system was constructed based on our proposed vertices structure uncertainty graph and the uncertainty of logistics nodes. Thirdly, the dynamic programming model of the underground logistics system was established, and its solution algorithm was also designed by improving simulated annealing. Finally, the correctness and feasibility of the method was validated by using a numerical example of the underground logistics system in Xianlin district, Nanjing City in China.

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