International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

With fixed running times at sections, cooperative scheduling (CS) approach optimizes the dwell times and the headway time to coordinate the accelerating and braking processes for trains, such that the recovery energy generated from the braking trains can be used by the accelerating trains. In practice, trains always have stochastic departure delays at busy stations. For reducing the divergence from the given timetable, the operation company generally adjusts the running times at the following sections. Focusing on the randomness on delay times and running times, this paper proposes a stochastic cooperative scheduling (SCS) approach. Firstly, we estimate the conversion and transmission losses of recovery energy, and then formulate a stochastic expected value model to maximize the utilization of the recovery energy. Furthermore, we design a binary-coded genetic algorithm to solve the optimal timetable. Finally, we conduct experimental studies based on the operation data from Beijing Yizhuang subway line. The results show that the SCS approach can save energy by 15.13% compared with the current timetable, and 8.81% compared with the CS approach.

Focusing on finding a pre-specified basis path in a network, this research formulates a two-stage stochastic optimization model for the least expected time shortest path problem, in which random scenario-based time-invariant link travel times are utilized to capture the uncertainty of the realworld traffic network. In this model, the first stage aims to find a basis path for the trip over all the scenarios, and the second stage intends to generate the remainder path adaptively when the realizations of random link travel times are updated after a pre-specified time threshold. The GAMS optimization software is introduced to find the optimal solution of the proposed model. The numerical experiments demonstrate the performance of the proposed approaches.

In this paper, we employ the resampling method to reduce the sample errors and increase the robustness of the classic mean variance model. By comparing the performances of the classic mean variance portfolio and the resampled portfolio, we show that the resampling method can enhance the investment efficiency. Through an empirical study of Chinese investors who invest in both Chinese market and other twelve major financial markets, we show that the resampling method helps to improve the performance of the mean variance model.

In this paper we propose an approach for multiobjective programming problems with fuzzy number coefficients. The main idea behind our approach is to approximate involved fuzzy numbers by their respective nearest interval approximation counterparts. An algorithm that returns a nearest interval approximation to a given fuzzy number, plays a pivotal role in the proposed method.

Our approach contrasts markedly with those based on deffuzification operators which replace a fuzzy set by a single real number leading to a loss of many important information. A numerical example is also provided for the sake of illustration.

In this paper, we study the problem of targeting a set of individuals to trigger a cascade of influence in a social network such that the influence diffuses to all individuals with the minimum time, given that the cost of initially influencing each individual is with randomness and that the budget available is limited. We adopt the incremental chance model to characterize the diffusion of influence, and propose three stochastic programming models that correspond to three different decision criteria respectively. A modified greedy algorithm is presented to solve the proposed models, which can flexibly trade off between solution performance and computational complexity. Experiments are performed on random graphs, by which we show that the algorithm we present is effective.

Optimal control is an important field of study both in theory and in applications. Based on uncertainty theory, an expected value model of uncertain optimal control problem was studied by Zhu. In this paper, an optimistic value model for uncertain optimal control problem is investigated. Applying Bellman's principle of optimality, the principle of optimality for the model is presented. And then the equation of optimality is obtained for the optimistic value model of uncertain optimal control. Finally, a portfolio selection problem is solved by this equation of optimality.

In credibilistic bimatrix games, the solution concept of (α, β)-optimistic equilibrium strategy was proposed for dealing with the situation that the two players want to optimize the optimistic value of their fuzzy objectives at confidence levels α and β, respectively. This paper goes further by assuming that the confidence levels are private information of the two players. And the so-called credibilistic bimatrix game with asymmetric information is investigated. A solution concept of Bayesian optimistic equilibrium strategy as well as its existence theorem are presented. Moreover, a sufficient and necessary condition is given for finding the Bayesian optimistic equilibrium strategy. Finally, an example is provided for illustrating purpose.

In order to deal with learning problems of random set samples encountered in real-world, according to random set theory and convex quadratic programming, a new support vector machine based on random set samples is constructed. Experimental results show that the new support vector machine is feasible and effective.

A model is constructed for a type of multi-period inventory problem with deteriorating items, in which demands are assumed to be uncertain variables. The objective is to minimize the expected total cost including the ordering cost, inventory holding cost and deteriorating cost under constraints that demands should be satisfied with some service level in each period. To solve the model, two methods are proposed in different cases. When uncertain variables are linear, a crisp equivalent form of the model is provided. For the general cases, a hybrid algorithm integrating the 99-method and genetic algorithm is designed. Two examples are given to illustrate the effectiveness of the model and solving methods.

In practice, security returns cannot be accurately predicted due to lack of historical data. Therefore, statistical methods and experts' experience are always integrated to estimate future security returns, which are hereinafter regarded as random fuzzy variables. Random fuzzy variable is a powerful tool to deal with the portfolio optimization problem including stochastic parameters with ambiguous expected returns. In this paper, we first define the semivariance of random fuzzy variable and prove its several properties. By considering the semivariance as a risk measure, we establish the mean-semivariance models for portfolio optimization problem with random fuzzy returns. We design a hybrid algorithm with random fuzzy simulation to solve the proposed models in general cases. Finally, we present a numerical example and compare the results to illustrate the mean-semivariance model and the effectiveness of the algorithm.