Decision support for foreign investment strategy under hybrid uncertainty

Empirical evidences show that Japan-based companies moved their major operations to the USA due to the currency appreciation of Japanese Yen in 1980s. However, the multinational firms moving their operations abroad still face both the risk of foreign price and the risk of foreign exchange rate. According to the purchasing power parity (PPP) and interest rate parity (IRP), the foreign exchange rate has associations with the relative price and the relative interest rate between two countries. Therefore, when the price and the interest rate evolve stochastically as proposed by many scholars, we can anticipate the randomness of the foreign exchange rate. By integrating these three sources of risks as a hybrid uncertainty, we propose a framework for corporate valuation and investment strategy. We analytically derive corporate value for those multinationals in question and then numerically obtain the real option value by Monte Carlo simulations, based on which we investigate the optimal entry strategy. The sensitivity for corporate value, optimal entry time, and real option value are analyzed. The results suggest a decision support process for foreign investment timing strategy under the hybrid uncertainty. The managerial implication of this work is that the optimal investment strategy for the multinational firms with foreign operations depends on some market and risk factors, as well as correlations among them.     

The uncertainty recovery analysis for interdependent infrastructure systems using the dynamic inoperability input–output model

In this paper, an innovatory modelling framework is proposed to conduct the uncertainty recovery analysis for the interdependent infrastructure sectors based on the dynamic inoperability input–output model (DIIM). The DIIM captures the inoperability of infrastructure systems, and therefore can easily analyse how perturbations propagate among interconnected infrastructures and how to implement effective mitigation efforts after a disaster. In this paper, based on the random recovery time distribution, we apply the Monte Carlo simulation to obtain the distributions of the economic losses for the critical interdependent infrastructure sectors after a disaster. The proposed method can provide the decision-makers the guidance in making suitable risk-management decisions as well as how the risks can be mitigated, if the disaster cannot be avoided to happen in the first place.     

基于不确定度评价的Kinect深度图预处理

针对应用在机器人三维(3D)场景感知测量中,Kinect深度图的联合双边滤波(JBF)存在降低原始场景深度信息精确度的制约性问题,提出一种新的预处理算法。首先,通过构建深度图的测量和采样模型,得到深度图的蒙特卡罗不确定度评价模型;其次,依据该模型计算得到深度值估计区间,实现噪声点与非噪声点的判定及滤除;最后,利用估计区间均值完成噪声点的修复。实验结果表明,该算法在噪声滤波的同时保证了非噪声的不变性;非噪声的不变性以及基于估计均值的噪声修复使原始深度梯度具有不变性;与联合彩色深度图的双边滤波相比,预处理结果图物体边缘轮廓清晰不变且其均方误差降低了15.25%~28.79%。因此,该预处理算法达到了提高三维场景深度信息精确度的目的。     

A comprehensive framework for project selection problem under uncertainty and real-world constraints

This paper proposes a comprehensive framework for project selection problem under uncertainty and subject to real-world constraints, like segmentation, logical, and budget constraints. The framework consists of two main phases. In the first phase, the candidate projects are ranked considering the uncertainty, through a Monte Carlo simulation linked to a multi-criteria approach. In the second phase, the overall complete preorder of the projects in different iterations is first determined and then used in another Monte Carlo simulation linked to an integer programming module in order to effectively drive the final portfolio selection while satisfying the budget, segmentation and other logical constraints. The proposed framework is implemented in a case study to show its usefulness and applicability in practice. Finally, a comparison is carried out between the proposed approach and its deterministic counterpart and the corresponding results are discussed.     

A stochastic cross‐efficiency data envelopment analysis approach for supplier selection under uncertainty

This paper addresses one of the key objectives of the supply chain strategic design phase, that is, the optimal selection of suppliers. A methodology for supplier selection under uncertainty is proposed, integrating the cross‐efficiency data envelopment analysis (DEA) and Monte Carlo approach. The combination of these two techniques allows overcoming the deterministic feature of the classical cross‐efficiency DEA approach. Moreover, we define an indicator of the robustness of the determined supplier ranking. The technique is able to manage the supplier selection problem considering nondeterministic input and output data. It allows the evaluation of suppliers under uncertainty, a particularly significant circumstance for the assessment of potential suppliers. The novel approach helps buyers in choosing the right partners under uncertainty and ranking suppliers upon a multiple sourcing strategy, even when considering complex evaluations with a high number of suppliers and many input and output criteria.     

Computation of the effects of uncertainty in volatility on option pricing and hedging

We quantify the effect of uncertainty in the volatility parameter σ on the Black–Scholes price of the European and American put. We apply probabilistic uncertainty analysis to the Black–Scholes model and compare the results with those of the Uncertain Volatility model. From historical data, we calibrate a probability distribution for the volatility. We then use Monte Carlo (MC) and a surrogate Polynomial Chaos (PC)/MC method to compute uncertainty bounds. The calibrated probability distribution is not one related to a standard orthogonal basis, so a basis is constructed numerically for the PC approximation. We show how to construct one stably from the probability distribution. We show that both methods give the same results, and quantify the relative speedup of the surrogate method. Finally, we investigate the effect of the parametric uncertainty, and show, for example, that the presence of uncertainty smoothes out the optimal exercise boundary of the American put.     

ReliaCloud‐NS: A scalable web‐based simulation platform for evaluating the reliability of cloud computing systems

This paper discusses the implementation, architecture, and use of a graphical web‐based application called ReliaCloud‐NS that allows users to (1) evaluate the reliability of a cloud computing system (CCS) and (2) design a CCS to a specified reliability level for both public and private clouds. The software was designed with a RESTful application programming interface for performing nonsequential Monte Carlo simulations to perform reliability evaluations of a CCS. Simulation results are stored and presented to the user in the form of interactive charts and graphs from within a web browser. The software contains multiple types of CCS components, simulations, and virtual machine allocation schemes. ReliaCloud‐NS also contains a novel feature that evaluates CCS reliability across a range of varying virtual machine allocations and establishes and graphs a CCS reliability curve. This paper discusses the software architecture, the interactive web‐based interface, and the different types of simulations available in ReliaCloud‐NS and presents an overview of the results generated from a simulation.     

A new algorithm for importance analysis of the inputs with distribution parameter uncertainty

Importance analysis is aimed at finding the contributions by the inputs to the uncertainty in a model output. For structural systems involving inputs with distribution parameter uncertainty, the contributions by the inputs to the output uncertainty are governed by both the variability and parameter uncertainty in their probability distributions. A natural and consistent way to arrive at importance analysis results in such cases would be a three-loop nested Monte Carlo (MC) sampling strategy, in which the parameters are sampled in the outer loop and the inputs are sampled in the inner nested double-loop. However, the computational effort of this procedure is often prohibitive for engineering problem. This paper, therefore, proposes a newly efficient algorithm for importance analysis of the inputs in the presence of parameter uncertainty. By introducing a ‘surrogate sampling probability density function (SS-PDF)’ and incorporating the single-loop MC theory into the computation, the proposed algorithm can reduce the original three-loop nested MC computation into a single-loop one in terms of model evaluation, which requires substantially less computational effort. Methods for choosing proper SS-PDF are also discussed in the paper. The efficiency and robustness of the proposed algorithm have been demonstrated by results of several examples.     

Comparison of interval and Monte Carlo simulation for the prediction of postprandial glucose under uncertainty in type 1 diabetes mellitus

In this paper, the problem of tackling uncertainty in the prediction of postprandial blood glucose is analyzed. Two simulation approaches, Monte Carlo and interval models, are studied and compared. Interval simulation is carried out using modal interval analysis. Simulation of a glucoregulatory model with uncertainty in insulin sensitivities, glucose absorption and food intake is carried out using both methods. Interval simulation is superior in predicting all severe and mild hyper- and hypoglycemia episodes. Furthermore, much less computational time is required for interval simulation than for Monte Carlo simulation.     

不确定性数据的超球支持向量机分类方法

针对区间不确定性数据的分类问题,提出一种基于超球支持向量机的多分类方法。采用超椭球凸集模型描述数据的不确定性信息;建立超球支持向量机的不确定约束规划模型,将其转化为两层嵌套约束规划问题;通过上下两层子优化交替迭代寻优的方法求解最优超球面,利用泰勒展开法,直接推导下层子优化线性近似问题的最优解,以降低计算复杂度。实验结果表明,该方法具有较高的分类精度及较好的抗噪性和鲁棒性,适合解决区间不确定性数据多分类问题。     

Use of a relevance vector machine for groundwater quality monitoring network design under uncertainty

This paper presents a methodology for groundwater quality monitoring network design. This design takes into account uncertainties in aquifer properties, pollution transport processes, and climate. The methodology utilizes a statistical learning algorithm called relevance vector machines (RVM), which is a sparse Bayesian framework that can be used for obtaining solutions to regression and classification tasks. Application of the methodology is illustrated using the Eocene Aquifer in the northern part of the West Bank, Palestine. The procedure presented in this paper utilizes a Monte Carlo (MC) simulation process to capture the uncertainties in recharge, hydraulic conductivity, and nitrate reaction processes through the application of a groundwater flow model and a nitrate fate and transport model. This MC modeling approach provides several thousand realizations of nitrate distribution in the aquifer. Subsets of these realizations are then used to design the monitoring network. This is done by building a best-fit model of nitrate concentration distribution everywhere in the aquifer for each Monte Carlo subset using RVM. The outputs from the RVM model are the distribution of nitrate concentration everywhere in the aquifer, the uncertainty in the characterization of those concentrations, and the number and locations of “relevance vectors” (RVs). The RVs form the basis of the optimal characterization of nitrate throughout the aquifer and represent the optimal locations of monitoring wells. In this paper, the number of monitoring wells and their locations where chosen based on the performance of the RVM model runs. The results from 100 model runs show the consistency of the model in selecting the number and locations of RV‘s. After implementing the design, the data collected from the monitoring sites can be used to estimate nitrate concentration distribution throughout the entire aquifer and to quantify the uncertainty in those estimates.     

On the incorporation of parameter uncertainty for inventory management using simulation

The main purpose of this paper is to discuss how a Bayesian framework is appropriate to incorporate the uncertainty on the parameters of the model that is used for demand forecasting. We first present a general Bayesian framework that allows us to consider a complex model for forecasting. Using this framework, we specialize (for simplicity) in the continuous‐review system to show how the main performance measures that are required for inventory management (service levels and reorder points) can be estimated from the output of simulation experiments. We discuss the use of two estimation methodologies: posterior sampling (PS) and Markov chain Monte Carlo (MCMC). We show that, under suitable regularity conditions, the estimators obtained from PS and MCMC satisfy a corresponding Central Limit Theorem, so that they are consistent, and the accuracy of each estimator can be assessed by computing an asymptotically valid half‐width from the output of the simulation experiments. This approach is particularly useful when the forecasting model is complex in the sense that analytical expressions to obtain service levels and/or reorder points are not available.     

Improved algorithm for tolerance allocation based on Monte Carlo simulation and discrete optimization

The allocation of design and manufacturing tolerances has a significant effect on both manufacturing cost and quality. This paper considers nonlinearly constrained tolerance allocation problems. The purpose is to minimize the ratio between the sum of the manufacturing costs (tolerances costs) and the risk (probability of the respect of geometrical requirements). The techniques of Monte Carlo simulation and genetic algorithm are adopted to solve these problems. As the simplest and the popular method for non-linear statistical tolerance analysis, the Monte Carlo simulation is introduced into the frame. Moreover, in order to make the frame efficient, the genetic algorithm is improved according to the features of the Monte Carlo simulation. An illustrative example (hyperstatic mechanism) is given to demonstrate the efficiency of the proposed approach.     

基于扩展原理的混合型证据推理不确定决策方法

提出一种基于扩展原理的混合证据推理不确定决策模型.通过α截集将同一决策问题中各属性使用的精确数、区间数和模糊数等异构评估信度统一分解为区间结构,采用区间证据推理方法求解各隶属度下的效用区间,并按隶属度次序重组方案效用;化简模糊数质心公式,并用于模糊定量评估的信度计算和方案模糊效用的排序;最后,通过具体实例验证了所提出方法的有效性和可行性.将该方法在算例中的适用情况进行比较和分析,结果表明所提出的方法具有良好的适应性.     

A new algorithm based on copulas for VaR valuation with empirical calculations

This paper concerns the application of copula functions in VaR valuation. The copula function is used to model the dependence structure of multivariate assets. After the introduction of the traditional Monte Carlo simulation method and the pure copula method we present a new algorithm based on mixture copula functions and the dependence measure, Spearman’s rho. This new method is used to simulate daily returns of two stock market indices in China, Shanghai Stock Composite Index and Shenzhen Stock Composite Index, and then empirically calculate six risk measures including VaR and conditional VaR. The results are compared with those derived from the traditional Monte Carlo method and the pure copula method. From the comparison we show that the dependence structure between asset returns plays a more important role in valuating risk measures comparing with the form of marginal distributions.     

基于概率断裂力学的管道疲劳寿命分析

采用奇异单元模拟裂纹尖端应力场的奇异性,计算裂纹尖端的应力强度因子和张开应力.以概率论为基础,结合确定性疲劳断裂力学估算方法,考虑参数的不确定性和随机性,应用蒙特卡洛模拟法分析管道的疲劳寿命.结果表明：通过J积分计算得到的裂纹尖端张开应力与计算得到的管道工作应力基本相等.采用蒙特卡洛模拟法进行的一定可靠度和置信度下的疲劳寿命预测能反映评定参数的不确定性,较传统的断裂力学计算结果更安全.     

Modelling decision support and uncertainty for large transport infrastructure projects: The CLG-DSS model of the Øresund Fixed Link

This paper presents a decision support system, named the CLG-DSS model, which makes it possible for decision makers to assess various uncertainties in project appraisal in a systematic and explicit way. This model, a decision support system (DSS) developed within the Danish Centre for Logistics and Freight Transport (CLG) is based on cost-benefit analysis (CBA) embedded in a wider multi-criteria analysis (MCA) by the use of some principles for composite modelling assessment (COSIMA). The CLG-DSS model is set up to make use of scenario analysis (SA) and Monte Carlo simulation (MCS). A particular concern in the model is the handling of varying information across the assessment criteria and the application of SA to inform the MCS parameter setting. After the presentation of the modelling principles some ex-post case calculations for the Øresund Fixed Link are illuminating different aspects of appraisal uncertainty and thereby, at the same time, demonstrate the features of the CLG-DSS model as a useful decision support tool. It is finally concluded that appraisal of large infrastructure projects can be effectively supported by dealing with uncertainty issues in accordance with the principles described.     

基于鲁棒估计的动态数据校正方法

针对目前动态数据校正方法存在的缺陷，本文在以前的工作基础上把基于鲁棒估计原理构造的鲁棒估计函数用于含异常点类型的过失误差的数据校正，Monte Carlo模拟结果及对TE问题的校正计算结果表明，这种基于鲁棒估计的过换误差侦破和数据协调同步方法可以在得到协调数据的同时很准确的侦破和识别出测量数据中所含的过失误差，具有较强的优越性。     

基于计算机仿真和层次分析法的船舶泊靠港选择决策支持

以港口作业计算机仿真模型为基础,阐述船舶泊靠港选择中港口这一复杂对象所涉及的主要因素,构造港口选择决策AHP模型层次结构中的准则,建立数学模型进行定量分析,并得到有效验证.     

An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms

This paper proposes an optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty (FOU) of the membership functions, considering three different cases to reduce the complexity problem of searching the parameter space of solutions. For the optimization method, we propose the use of a genetic algorithm (GA) to optimize the type-2 fuzzy inference systems, considering different cases for changing the level of uncertainty of the membership functions to reach the optimal solution at the end.