A neural network model for resource scheduling optimization

Author

Fang, Xi ; Chen, Jing

Author_Institution

Sch. of Water Conservancy & Hydropower Eng., Hohai Univ., Nanjing, China

fYear

2010

fDate

16-18 April 2010

Firstpage

430

Lastpage

432

Abstract

Any change in resource planning may lead to change in project duration and resources cost which will impact the total cost of the project when risk factors are taken into account. A neural network model for resource scheduling to minimize the total cost of a project was proposed to improve the situation and Morte Carlo simulation and genetic algorithm were used to solve it. A case study based on the Neural Network model shows the optimization model is better than the Critical Path Method (CPM) network model.

Keywords

Monte Carlo methods; critical path analysis; genetic algorithms; neural nets; resource allocation; scheduling; CPM; Morte Carlo simulation; critical path method; genetic algorithm; neural network model; optimization model; resource planning; resource scheduling optimization; Cost function; Electronic mail; Equations; Genetic algorithms; Hydroelectric power generation; Neural networks; Optimization methods; Random variables; Water conservation; Water resources; network programming; neural network; resource planning;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-5263-7

Electronic_ISBN

978-1-4244-5265-1

Type

conf

DOI

10.1109/ICIME.2010.5477602

Filename

5477602