Shengguo Li

Uncertain programming models for portfolio selection with uncertain returns

In an indeterminacy economic environment, experts’ knowledge about the returns of securities consists of much uncertainty instead of randomness. This paper discusses portfolio selection problem in uncertain environment in which security returns cannot be well reflected by historical data, but can be evaluated by the experts. In the paper, returns of securities are assumed to be given by uncertain variables. According to various decision criteria, the portfolio selection problem in uncertain environment is formulated as expected-variance-chance model and chance-expected-variance model by using the uncertainty programming. Within the framework of uncertainty theory, for the convenience of solving the models, some crisp equivalents are discussed under different conditions. In addition, a hybrid intelligent algorithm is designed in the paper to provide a general method for solving the new models in general cases. At last, two numerical examples are provided to show the performance and applications of the models and algorithm.

Uncertain programming model for uncertain minimum weight vertex covering problem

In this paper, the minimum weight vertex covering problem with uncertain vertex weights is investigated. By virtue of the uncertainty distribution operation of independent uncertain variables, the uncertainty distribution of the minimum weight of vertex cover is derived, and the concept of the

Spare parts allocation by improved genetic algorithm and Monte Carlo simulation

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Distortion risk measures of uncertain systems

α-minimum cover among uncertain weight vertex covers is proposed within the framework of uncertain programming. Then an

Analysis of Different Versions of the Credibilistic Value at Risk

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Bridges between Fuzzy Dominance and Credibilistic VaR Dominance

α-minimum model for uncertain weight vertex covering problem is established and discussed. Taking advantage of some properties of uncertainty theory, the model can be transformed into the corresponding deterministic form. At last, a numerical example is presented to show the performance of the model.