DocumentCode :
2964627
Title :
The bi-objective stochastic chance-constrained optimization model of multi-project & multi-item investment combination based on the view of real options
Author :
Xu, Bin ; Yu, Jing ; Meng, Yan
Author_Institution :
Sch. of Accountancy, Central Univ. of Finance & Econ., Beijing, China
fYear :
2009
fDate :
8-11 Dec. 2009
Firstpage :
749
Lastpage :
753
Abstract :
This paper presents a new stochastic chance-constrained 0-1 integer programming model for investigating the investment combination problem in multi-project multi-item investment combination. The proposed model includes two objectives with stochastic constraints to construct a 0-1 integer programming model. On the one hand, the risk value will be measured by negative entropy; on the other hand, the pursued value objective will be composed of two parts, which including classical NPV and the corresponding real option for any item of any project. Then how to use DE to solve the model with a small modification of constraint-handling rule will be illustrated. A simulation experiment is employed to illustrate the application of the proposed model to get the Pareto-optimal solutions by applying the modified algorithm DE.
Keywords :
Pareto optimisation; integer programming; investment; risk management; Pareto optimal solutions; bi-objective stochastic chance-constrained optimization model; constraint handling rule; differential evolution algorithm; multiproject multi item investment combination; negative entropy; net present value; risk value; stochastic chance-constrained 0-1 integer programming model; Content addressable storage; Costs; Entropy; Finance; Investments; Linear programming; Numerical simulation; Portfolios; Security; Stochastic processes; investment combination; modified DE; multi-project-multiple-item; real option; stochastic optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Engineering and Engineering Management, 2009. IEEM 2009. IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-4869-2
Electronic_ISBN :
978-1-4244-4870-8
Type :
conf
DOI :
10.1109/IEEM.2009.5372926
Filename :
5372926
Link To Document :
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