Optimal Portfolio Selection with Delay under the Framework of Uncertainty Theory

This study focuses on investigating the optimal investment strategy for an optimization problem with delay using the uncertainty theory. The financial market is composed of a risk-free asset and a risk asset with an uncertain price process described by an uncertain differential equation. An optimization problem is assumed that its objective is a nonlinear function of decision variable. By deriving the equation of optimality, an analytical solution is obtained for the optimal delay investment strategy, and the optimal delay value function. Finally, an economic analysis and numerical sensitivity analysis are conducted to evaluate the research results.

A case study of blasting vibration attenuation based on wave component characteristics

A typical blasting vibration wave is a composite wave,and its attenuation law is affected by the type of dominant wave component.The purpose of the present study is to establish an attenuation equation of the peak particle velocity(PPV),taking into account the attenuation characteristics of P-,S-and R-waves in the blasting vibration wave.Field blasting tests were carried out as a case to specifically apply the proposed equation.In view of the fact that the discrete properties of rock mass will inevitably cause the uncertainty of blasting vibration,we also carried out a probability analysis of PPV uncertainty,and introduced the concept of reliability to evaluate blasting vibration.The results showed that the established attenuation equation had a higher prediction accuracy,and can be considered as a promising equation implemented on more complex sites.The adopted uncertainty analysis method can comprehensively take account of the attenuation law of blasting vibration measured on site and discrete properties of rock masses.The obtained distribution of the PPV uncertainty factor can quantitatively evaluate the reliability of blasting vibration,which is a powerful and necessary supplement to the PPV attenuation equation.

Approach for uncertain multi-objective programming problems with correlated objective functions under C_(EV) criterion

An uncertain multi-objective programming problem is a special type of mathematical multi-objective programming involving uncertain variables. This type of problem is important because there are several uncertain variables in real-world problems.Therefore, research on the uncertain multi-objective programming problem is highly relevant, particularly those problems whose objective functions are correlated. In this paper, an approach that solves an uncertain multi-objective programming problem under the expected-variance value criterion is proposed. First, we define the basic framework of the approach and review concepts such as a Pareto efficient solution and expected-variance value criterion using an order relation between various uncertain variables.Second, the uncertain multi-objective problem is converted into an uncertain single-objective programming problem via a linear weighted method or ideal point method. Then the problem is transformed into a deterministic single objective programming problem under the expected-variance value criterion. Third, four lemmas and two theorems are proved to illustrate that the optimal solution of the deterministic single-objective programming problem is an efficient solution to the original uncertainty problem. Finally, two numerical examples are presented to validate the effectiveness of the proposed approach.

Research on the Recovery of Irregular Flights under Uncertain Conditions

This paper mainly studies the problem of irregular flights recovery under uncertain conditions. Based on the analysis of the uncertain factors affecting the flight, taking the total delay time and the total cost of flight delay as the objective function, and considering the constraints of flight plan and passenger journey, an uncertain objective programming model is constructed. Finally, taking OVS airport temporarily closed due to bad weather as an example, the results show that better quality optimization scheme can be obtained by integrating passenger recovery with narrow sense flight recovery stage and implementing integrated recovery.

Handling epistemic uncertainties in PRA using evidential networks

In order to overcome the limitations of traditional methods in uncertainty analysis, a modified Bayesian network(BN), which is called evidence network(EN), was proposed with evidence theory to handle epistemic uncertainty in probabilistic risk assessment(PRA). Fault trees(FTs) and event trees(ETs) were transformed into an EN which is used as a uniform framework to represent accident scenarios. Epistemic uncertainties of basic events in PRA were presented in evidence theory form and propagated through the network. A case study of a highway tunnel risk analysis was discussed to demonstrate the proposed approach. Frequencies of end states are obtained and expressed by belief and plausibility measures. The proposed approach addresses the uncertainties in experts' knowledge and can be easily applied to uncertainty analysis of FTs/ETs that have dependent events.

Measuring reliability under epistemic uncertainty:Review on non-probabilistic reliability metrics

In this paper, a systematic review of non-probabilistic reliability metrics is conducted to assist the selection of appropriate reliability metrics to model the influence of epistemic uncertainty. Five frequently used non-probabilistic reliability metrics are critically reviewed, i.e., evidence- theory-based reliability metrics, interval-analysis-based reliability metrics, fuzzy-interval-analysis- based reliability metrics, possibility-theory-based reliability metrics （posbist reliability） and uncertainty-theory-based reliability metrics （belief reliability）. It is pointed out that a qualified reli- ability metric that is able to consider the effect of epistemic uncertainty needs to （ 1 ） compensate the conservatism in the estimations of the component-level reliability metrics caused by epistemic uncertainty, and （2） satisfy the duality axiom, otherwise it might lead to paradoxical and confusing results in engineering applications. The five commonly used non-probabilistic reliability metrics are compared in terms of these two properties, and the comparison can serve as a basis for the selection of the appropriate reliability metrics.

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.

Hybrid QoS-aware semantic web service composition strategies

Hybrid QoS model which consists of certain and uncertain expressions has strong power of semantic QoS description. For solving the hybrid QoS-aware semantic web service composition problem, this paper presents an Uncertain Multi-attribute decision making based Composition algorithm （UMC）. The UMC includes two parts. First, UMC-Core can be used to synthetically evaluate the hybrid service quality information. Second, UMC-DH （Distributed and Heuristic framework for UMC） aims at enhancing the run-time performance of UMC when the problem space increases. The simulation results show that the UMC has lower execution cost, higher approximation ratio and success ratio than other similar approaches.