Uncertain random programming with applications

Uncertain random variable is a tool to deal with a mixture of uncertainty and randomness. This paper presents an operational law of uncertain random variables, and shows an expected value formula by using probability and uncertainty distributions. This paper also provides a framework of uncertain random programming that is a type of mathematical programming involving uncertain random variables. Finally, some applications of uncertain random programming are discussed.     

Convex fuzzy random variables

A theory of fuzzy random variables is developed that applies to situations involving both randomness and fuzziness. The use of membership functions that are quasi-concave play an important role in the theory. The expectation of a fuzzy random variable is a fuzzy variable (fuzzy set). The usual linearity properties of probabilistic expectation carry over to fuzzy random variables. A special case of a fuzzy Law of Large Number is proven.     

Order statistics of uncertain random variables with application to <Emphasis Type="Italic">k</Emphasis>-out-of-<Emphasis Type="Italic">n</Emphasis> system

Uncertain random variables are tools to deal with a mixture of uncertainty and randomness. A new concept of order statistics associated with uncertain random variables is proposed, and is applied to analyze k-out-of-n systems with uncertain random lifetimes. The chance distributions of order statistics of uncertain random variables are derived from the operational law of uncertain random variables. Finally, the reliability of k-out-of-n systems with uncertain random lifetimes is discussed.     

Solving project scheduling problem with the philosophy of fuzzy random programming

Project scheduling problem is to determine the schedule of allocating resources to achieve the trade-off between the project cost and the completion time. In real projects, the trade-off between the project cost and the completion time, and the uncertainty of the environment are both considerable aspects for managers. Due to the complex external environment, this paper considers project scheduling problem with coexisted uncertainty of randomness and fuzziness, in which the philosophy of fuzzy random programming is introduced. Based on different ranking criteria of fuzzy random variables, three types of fuzzy random models are built. Besides, a searching approach by integrating fuzzy random simulations and genetic algorithm is designed for searching the optimal schedules. The goal of the paper is to provide a new method for solving project scheduling problem in hybrid uncertain environments.     

Order statistics for nonstationary time series

Order statistics has an important role in statistical inference. The main purpose of this paper is to investigate order statistics, and also explore its applications in the analysis of nonstationary time series. Our results show that linear functions of order statistics for a large class of time series are asymptotically normal. The methods of proof involve approximations of serially dependent random variables by independent ones. The problems of testing for the existence of a linear trend and the problem of testing randomness versus serial dependence are considered as applications.     

Multi-objective optimization in uncertain random environments

Uncertain random variables are used to describe the phenomenon of simultaneous appearance of both uncertainty and randomness in a complex system. For modeling multi-objective decision-making problems with uncertain random parameters, a class of uncertain random optimization is suggested for decision systems in this paper, called the uncertain random multi-objective programming. For solving the uncertain random programming, some notions of the Pareto solutions and the compromise solutions as well as two compromise models are defined. Subsequently, some properties of these models are investigated, and then two equivalent deterministic mathematical programming models under some particular conditions are presented. Some numerical examples are also given for illustration.     

On the Continuity and Absolute Continuity of Random Closed Sets

Abstract

In the case of real-valued random variables, the concept of absolute continuity is well-defined in terms of the absolute continuity of the probability law of a random variable with respect to the usual Lebesgue measure, since both are acting on the same Borel sigma algebra on the real line. Naturally, the same extends to random vectors with real components. A satisfactory and commonly accepted definition of absolute continuity of random closed sets is not available, while in various applications this would help in clarifying the kind of randomness of a random set. We introduce here a definition that is shown to be an extension of the concept related to real-valued random variables, such that also for random sets it is true that absolute continuity implies continuity. Significant examples and counter examples are presented to illustrate the role of our definition in concrete cases. The relationship between our definition and others in well-accepted literature is shown.     

Project scheduling problem with mixed uncertainty of randomness and fuzziness

Project scheduling problem is to determine the schedule of allocating resources so as to balance the total cost and the completion time. This paper considers project scheduling problem with mixed uncertainty of randomness and fuzziness, where activity duration times are assumed to be random fuzzy variables. Three types of random fuzzy models as expected cost minimization model, (α, β)-cost minimization model and chance maximization model are built to meet different management requirements. Random fuzzy simulations for some uncertain functions are given and embedded into genetic algorithm to design a hybrid intelligent algorithm. Finally, some numerical experiments are given for the sake of illustration of the effectiveness of the algorithm.     

A pentatonic classification of extreme events

In this paper we present a classification of the extreme events – very small and very large outcomes – of positive-valued random variables. The classification distinguishes five different categories of randomness, ranging from the very ‘mild’ to the very ‘wild’. In analogy with the common five-tone musical scale we term the classification ‘pentatonic’. The classification is based on the analysis of the inherent Gibbsian ‘forces’ and ‘temperatures’ existing on the logarithmic scale of the random variables under consideration, and provides a statistical-physics insight regarding the nature of these random variables. The practical application of the pentatonic classification is remarkably straightforward, it can be performed by non-experts, and it is demonstrated via an array of examples.     

Truth‐table Schnorr randomness and truth‐table reducible randomness

Schnorr randomness and computable randomness are natural concepts of random sequences. However van Lambalgen’s Theorem fails for both randomnesses. In this paper we define truth‐table Schnorr randomness (defined in 6 too only by martingales) and truth‐table reducible randomness, for which we prove that van Lambalgen's Theorem holds. We also show that the classes of truth‐table Schnorr random reals relative to a high set contain reals Turing equivalent to the high set. It follows that each high Schnorr random real is half of a real for which van Lambalgen's Theorem fails. Moreover we establish the coincidence between triviality and lowness notions for truth‐table Schnorr randomness. © 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim     

Optimisation under hybrid uncertainty

Fuzzy Stochastic Optimisation is emerging as a subfield of Mathematical programming, the disciplinary matrix of which consists of analysis of mathematical programs under fuzziness and randomness along with methods for solving them. The “primum movens” of this paper is to describe a unifying methodological approach, that is suitable for finding a satisfying solution of a mathematical program in the presence of fuzzy data and random variables. Properties of fuzzy random variables (FRVs) serve as the backdrop to this approach which also lends itself better to handling mathematical programs with fuzzy random coefficients. For the paper to be somewhat self-contained, the notion of FRV is briefly discussed and a synopsis of Fuzzy Stochastic Optimisation provided. A systematically solved example aimed at illustrating the proposed approach is also included.     

On dependence of dynamical structure of numerical solutions of fluid simulations on forcibly added randomness

In the present paper, the dependencies of the numerical results of fluid simulations on forcibly added randomness are discussed. The incompressible Navier-Stokes equations and the continuity equation are solved numerically by using the MAC (Maker-And-Cell) method and implicit temporal scheme. The model adopted in the present study is a flow around a two-dimensional circular cylinder and the Reynolds number is 1500. The randomness which is given by using the pseudo-random number is forcibly added in the time marching step of the discretized Navier-Stokes equations. Dependencies of the averaged structure of asymptotic numerical solutions on the randomness are discussed. Furthermore, the dependence of the qualitative structure of the asymptotic solution of each sample calculation on the amplitude of randomness is also studied. It is clarified that forcibly added random errors may cover the nonlinear errors which make the system unstable.     

考虑投资者风险态度的随机模糊投资组合模型及实证

考虑投资者面临证券市场随机和模糊的双重不确定性,把证券收益率视为随机模糊变量。在前景理论下考虑投资者的风险态度,建立不同的随机模糊收益率、期望收益隶属度函数和目标权重,构建考虑投资者风险态度的随机模糊投资组合模型。采用实证方法把市场分为下降和上升两个阶段,研究不同风险态度投资者的投资组合差异及模型表现。结果表明:投资者的风险态度会影响投资组合的结构;考虑投资者风险态度的随机模糊投资组合模型,能够满足不同风险态度投资者对投资收益和风险的差异需求,且在实际投资决策中具有可行性。     

Anderson model with decaying randomness: mobility edge

In this paper we consider the Anderson model with decaying randomness , and , i.i.d. random variables with an absolutely continuous distribution . For a class of we show the following results on a set of full measure. (i) If as , then (ii) . (iii) If for large and , the mobility edges are the two points . Received June 15, 1998; in final form September 15, 1999 / Published online October 11, 2000     

A visual interactive approach for scenario-based stochastic multi-objective problems and an application

In many practical applications of stochastic programming, discretization of continuous random variables in the form of a scenario tree is required. In this paper, we deal with the randomness in scenario generation and present a visual interactive method for scenario-based stochastic multi-objective problems. The method relies on multi-variate statistical analysis of solutions obtained from a multi-objective stochastic problem to construct joint confidence regions for the objective function values. The decision maker (DM) explores desirable parts of the efficient frontier using a visual representation that depicts the trajectories of the objective function values within confidence bands. In this way, we communicate the effects of randomness inherent in the problem to the DM to help her understand the trade-offs and the levels of risk associated with each objective.     

Fuzzy periodic review system with fuzzy random variable demand

In this paper, a periodic review inventory system has been analyzed in a mixed imprecise and uncertain environment where fuzziness and randomness appear simultaneously. A model has been developed with customer demand assumed to be a fuzzy random variable. The lead-time has been assumed to be a constant. The lead-time demand and the lead-time plus one period’s demand have also been assumed to be fuzzy random variables. A methodology has been developed to determine the optimal inventory level and the optimal period of review such that the total expected annual cost in the fuzzy sense is minimized. A numerical example has been presented to illustrate the model.     

Propagation of Disturbances in a Random Medium

In this paper we are concerned with the determination of thestochastic rays along which light disturbances propagate ina random inhomogeneous, time-dependent and isotropic medium.The analysis is formulated in the framework of stochastic optimalcontrol. We state an appropriate stochastic Fermat's Principlewhich upon invoking the principle of optimality in dynamic programmingleads to a parabolic functional differential equation for thetraversal time of a wavefront, the randomness entering as anadditive white-noise in the displacements of the field. Whenthe randomness constitutes small perturbations to the mean velocityfield, the problem becomes one of singular perturbation of theHamilton-Jacobi equation by a small second order term. Approximateexpressions are presented for the traversal time and mean stochasticpath for a time-independent mean-velocity of propagation. Asa specific example we consider these expressions for propagationsin a stratified medium.     

The Queueing Maximal availability location problem: A model for the siting of emergency vehicles

The Maximal Availability Location Problem (MALP) has been recently formulated as a probabilistic version of the maximal covering location problem. The added feature in MALP is that randomness into the availability of servers is considered. In MALP, though, it is assumed that the probabilities of different servers being busy are independent. In this paper, we utilize results from queuing theory to relax this assumption, obtaining a more realistic model for emergency systems: the Queueing MALP or Q-MALP. We also consider in this model that travel times or distances along arcs of the network are random variables. We show here how to site limited numbers of emergency vehicles, such as ambulances, in such a way as to maximize the calls for service which have an ambulance available within a time or distance standard with reliability α — using a queueing theory model for server availability. We also propose some extensions to the basic model. Formulations are presented and computational experience is offered.     

随机偏好连接图的中心极限定理

我们研究了一类具有随机顶点和边的随机连接图模型, 其中顶点的随机性由一个Poisson 点过程所决定, 边的随机性由一个概率连接函数所决定. 我们得到了带偏好的随机连接图模型的关于所有随机边的长度和的一个中心极限定理.