Using imprecise estimates for weights

In multi-attribute decision problems the decision to differentiate between alternatives will be affected by the precision with which weights are specified. Specifications are imprecise because of the uncertainty characteristic of the judgements on which weights are based. Uncertainties are from two sources, the accuracy with which judgements are articulated and the inconsistency when multiple judgements are made and must be reconciled. These uncertainties are modelled using probabilistic weight estimates integrated by the Dirichlet distribution. This ensures the consistency of the estimates and leads to the calculation of significance of the differences between alternatives. A simple plot of these significant differences helps in the final decision whether this is selection or ranking. The method is used to find weight estimates in the presence of both types of uncertainty acting separately and together.     

Fuzzy optimization for distribution of frozen food with imprecise times

Problems concerning the distribution routes for frozen products need to incorporate constraints that avoid breaks in the cold chain. The decision making process under uncertain environments is a common one in real logistics problems. The purpose of this study is to apply a fuzzy approach which will provide an optimal solution to the distribution of frozen food with uncertainty in its time values. A soft computing approach is used where fuzzy constraints are included in the modeling and the solution of the problem.     

随机环境下技术开发和授权的最优策略

技术创新的价值实现需要后续的商业化投资,在随机不确定的市场环境下,创新企业面对着投资或等待、自身开发或技术授权的策略选择.利用实物期权方法建立了市场不确定时的决策模型,分析开发能力、不确定性等多种因素对企业授权的影响,在多种情况下得到企业的最优决策并对相关现象给出了解释.研究发现不确定性提高了企业投资的临界值,提高了企业的技术授权价格,高度的不确定性减少了技术授权的可能,较高的能力差异会促进技术交易.     

An extended TODIM method for multiple attribute group decision‐making based on 2‐dimension uncertain linguistic Variable

The significant characteristic of the TODIM (an acronym in Portuguese of Interactive and Multiple Attribute Decision Making) method is that it can consider the bounded rationality of the decision makers. However, in the classical TODIM method, the rating of the attributes only can be used in the form of crisp numbers. Because 2‐dimension uncertain linguistic variables can easily express the fuzzy information, in this article, we extend the TODIM method to 2‐dimension uncertain linguistic information. First of all, the definition, characteristics, expectation, comparative method and distance of 2‐dimension uncertain linguistic information are introduced, and the steps of the classical TODIM method for Multiple attribute decision making (MADM) problems are presented. Second, on the basis of the classical TODIM method, the extended TODIM method is proposed to deal with MADM problems in which the attribute values are in the form of 2‐dimension uncertain linguistic variables, and detailed decision steps are given. Its significant characteristic is that it can fully consider the bounded rationality of the decision makers, which is a real action in real decision making. Finally, a numerical example is provided to verify the developed approach and its practicality and effectiveness. © 2014 Wiley Periodicals, Inc. Complexity 21: 20–30, 2016     

Environmental risk modelling under probability-normal interval-valued fuzzy number

In almost all the realistic circumstances, such as health risk assessment and uncertainty analysis of atmospheric dispersion, it is very essential to include all the information into modelling. The parameters associated to a particular model may include different kind of variability, imprecision and uncertainty. More often, it is seen that available informations are interpreted in probabilistic sense. Probability theory is a well-established theory to measure such kind of variability. However, not all of available information, data or model parameters affected by variability, imprecision and uncertainty can be handled by traditional probability theory. Uncertainty or imprecision may occur due to incomplete information or data, measurement errors or data obtained from expert judgement or subjective interpretation of available data or information. Thus, model parameters, data may be affected by subjective uncertainty. Traditional probability theory is inappropriate to represent them. Possibility theory and fuzzy set theory is another branch of mathematics which is used as a tool to describe the parameters with insufficient or vague knowledge. In this paper, an attempt has been made to combine probability knowledge and possibility knowledge and draw the uncertainty. The paper describes an algorithm for combining probability distribution and interval-valued fuzzy number and applied to environmental risk modelling with a case study. The primary aim of this paper is to propagate the proposed method. Computer codes are prepared for the proposed method using MATLAB.     

Modifications of the Hurwicz’s decision rule

The Hurwicz’s criterion is one of the classical decision rules applied in decision making under uncertainty as a tool enabling to find an optimal pure strategy both for interval and scenarios uncertainty. The interval uncertainty occurs when the decision maker knows the range of payoffs for each alternative and all values belonging to this interval are theoretically probable (the distribution of payoffs is continuous). The scenarios uncertainty takes place when the result of a decision depends on the state of nature that will finally occur and the number of possible states of nature is known and limited (the distribution of payoffs is discrete). In some specific cases the use of the Hurwicz’s criterion in the scenarios uncertainty may lead to quite illogical and unexpected results. Therefore, the author presents two new procedures combining the Hurwicz’s pessimism-optimism index with the Laplace’s approach and using an additional parameter allowing to set an appropriate width for the ranges of relatively good and bad payoffs related to a given decision. The author demonstrates both methods on the basis of an example concerning the choice of an investment project. The methods described may be used in each decision making process within which each alternative (decision, strategy) is characterized by only one criterion (or one synthetic measure).     

Approaches to multistage one-shot decision making

In this research, multistage one-shot decision making under uncertainty is studied. In such a decision problem, a decision maker has one and only one chance to make a decision at each stage with possibilistic information. Based on the one-shot decision theory, approaches to multistage one-shot decision making are proposed. In the proposed approach, a decision maker chooses one state amongst all the states according to his/her attitude about satisfaction and possibility at each stage. The payoff at each stage is associated with the focus points at the succeeding stages. Based on the selected states (focus points), the sequence of optimal decisions is determined by dynamic programming. The proposed method is a fundamental alternative for multistage decision making under uncertainty because it is scenario-based instead of lottery-based as in the other existing methods. The one-shot optimal stopping problem is analyzed where a decision maker has only one chance to determine stopping or continuing at each stage. The theoretical results have been obtained.     

供应提前期和需求不确定下库存系统的随机比较

本文研究供应提前期和需求不确定下包含最小化成本和最大化利润的一致化报童问题中随机比较实现的充分条件,并通过随机比较定量刻画提前期和需求不确定性对库存系统决策和利润的影响。首先引入报童收益函数,给出其优化前后满足随机占优及拉普拉斯变换序的充分条件。进一步,在周期需求服从指数分布的情形下,定量刻画固定提前期与提前期服从几何分布两种情况的随机单调性。最后,将上述结论应用到报童模型中,得到提前期需求对一致化报童问题的随机单调性。     

企业技术投资决策模型及其模拟

何时投资购买技术是企业技术战略研究的一个关键问题.传统的技术投资的决策准则是使用净现值(NPV)方法,这种方法会导致企业过早的投资于一项技术,也不能解释企业技术投资时间滞后的现象.企业技术投资时间的滞后可以通过技术本身的不确定性来解释,技术的不确定性主要表现为在技术演化过程中新技术出现的速度和新技术技术效率改进程度的不确定.本文针对企业技术投资中的技术效率决策问题,假设技术出现服从泊松分布、技术改进程度服从均匀分布,构建了技术投资决策模型,并对模型进行模拟,结论表明决策模型比NPV方法能更好的解释企业技术投资决策的行为.     

决策依赖不确定条件下基于复合实物期权估值的工程系统投资决策研究

不确定性是复杂工程系统的内在属性,在决策依赖不确定条件下对工程系统的投资决策需考虑不确定性与决策过程之间的交互作用,使得投资决策问题的求解非常困难.提出了决策依赖不确定条件下复合实物期权估值的最小二乘模拟算法,方法较好地解决了在决策依赖不确定条件下由于不同期权价值相互耦合所带来的计算复杂性,进一步拓展了最小二乘模拟算法在期权估值中的应用,基于该方法,可以比较方便地解决决策依赖不确定条件下工程系统投资决策问题.     

A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty

Location planning for urban distribution centers is vital in saving distribution costs and minimizing traffic congestion arising from goods movement in urban areas. In this paper, we present a multi-criteria decision making approach for location planning for urban distribution centers under uncertainty. The proposed approach involves identification of potential locations, selection of evaluation criteria, use of fuzzy theory to quantify criteria values under uncertainty and application of fuzzy TOPSIS to evaluate and select the best location for implementing an urban distribution center. Sensitivity analysis is performed to determine the influence of criteria weights on location planning decisions for urban distribution centers.The strength of the proposed work is the ability to deal with uncertainty arising due to a lack of real data in location planning for new urban distribution centers. The proposed approach can be practically applied by logistics operators in deciding on the location of new distribution centers considering the sustainable freight regulations proposed by municipal administrations. A numerical application is provided to illustrate the approach.     

The determination of uncertainty levels in robust clustering of subjects with longitudinal observations using the Dirichlet process mixture

In this paper we introduce a new method to the cluster analysis of longitudinal data focusing on the determination of uncertainty levels for cluster memberships. The method uses the Dirichlet-t distribution which notably utilizes the robustness feature of the student-t distribution in the framework of a Bayesian semi-parametric approach together with robust clustering of subjects evaluates the uncertainty level of subjects memberships to their clusters. We let the number of clusters and the uncertainty levels be unknown while fitting Dirichlet process mixture models. Two simulation studies are conducted to demonstrate the proposed methodology. The method is applied to cluster a real data set taken from gene expression studies.     

Hysteresis effects under CIR interest rates

Most decision making research in real options focuses on revenue uncertainty assuming discount rates remain constant. However, for many decisions revenue or cost streams are relatively static and investment is driven by interest rate uncertainty, for example the decision to invest in durable machinery and equipment. Using interest rate models from Cox et al. (1985b), we generalize the work of Ingersoll and Ross (1992) in two ways. Firstly, we include real options on perpetuities (in addition to zero coupon cash flows). Secondly, we incorporate abandonment or disinvestment as well as investment options, and thus model interest rate hysteresis (parallel to revenue uncertainty in Dixit (1989a)). Under stochastic interest rates, economic hysteresis is found to be significant, even for small sunk costs.     

On the decisiveness of a game in a tournament

In sport tournaments in which teams are matched two at a time, it is useful for a variety of reasons to be able to quantify how important a particular game is. The need for such quantitative information has been addressed in the literature by several more or less simple measures of game importance. In this paper, we point out some of the drawbacks of those measures and we propose a different approach, which rather targets how decisive a game is with respect to the final victory. We give a definition of this idea of game decisiveness in terms of the uncertainty about the eventual winner prevailing in the tournament at the time of the game. As this uncertainty is strongly related to the notion of entropy of a probability distribution, our decisiveness measure is based on entropy-related concepts. We study the suggested decisiveness measure on two real tournaments, the 1988 NBA Championship Series and the UEFA 2012 European Football Championship (Euro 2012), and we show how well it agrees with what intuition suggests. Finally, we also use our decisiveness measure to objectively analyse the recent UEFA decision to expand the European Football Championship from 16 to 24 nations in the future, in terms of the overall attractiveness of the competition.     

实物期权方法在自主创新项目投资决策中的应用

传统的投资决策方法由于蕴含着不确定性和可逆转性的假设使其不适应于高风险、高收益并存的自主创新项目投资决策.将实物期权思想融入自主创新项目投资决策方法,考虑了项目由于柔性经营的期权价值,能更准确地反映项目的价值,从而提高投资决策的科学性和合理性.从实物期权理论的基本原理出发,通过具体实例对比说明实物期权方法应用于自主创新项目投资决策的优势.     

An approach to the valuation and decision of ERP investment projects based on real options

The risks and uncertainties inherent in most enterprise resources planning (ERP) investment projects are vast. Decision making in multistage ERP projects investment is also complex, due mainly to the uncertainties involved and the various managerial and/or physical constraints to be enforced. This paper tackles the problem using a real-option analysis framework, and applies multistage stochastic integer programming in formulating an analytical model whose solution will yield optimum or near-optimum investment decisions for ERP projects. Traditionally, such decision problems were tackled using lattice simulation or finite difference methods to compute the value of simple real options. However, these approaches are incapable of dealing with the more complex compound real options, and their use is thus limited to simple real-option analysis. Multistage stochastic integer programming is particularly suitable for sequential decision making under uncertainty, and is used in this paper and to find near-optimal strategies for complex decision problems. Compared with the traditional approaches, multistage stochastic integer programming is a much more powerful tool in evaluating such compound real options. This paper describes the proposed real-option analysis model and uses an example case study to demonstrate the effectiveness of the proposed approach.     

An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty

Analyses of global climate policy as a sequential decision under uncertainty have been severely restricted by dimensionality and computational burdens. Therefore, they have limited the number of decision stages, discrete actions, or number and type of uncertainties considered. In particular, two common simplifications are the use of two-stage models to approximate a multi-stage problem and exogenous formulations for inherently endogenous or decision-dependent uncertainties (in which the shock at time t+1 depends on the decision made at time t). In this paper, we present a stochastic dynamic programming formulation of the Dynamic Integrated Model of Climate and the Economy (DICE), and the application of approximate dynamic programming techniques to numerically solve for the optimal policy under uncertain and decision-dependent technological change in a multi-stage setting. We compare numerical results using two alternative value function approximation approaches, one parametric and one non-parametric. We show that increasing the variance of a symmetric mean-preserving uncertainty in abatement costs leads to higher optimal first-stage emission controls, but the effect is negligible when the uncertainty is exogenous. In contrast, the impact of decision-dependent cost uncertainty, a crude approximation of technology R&D, on optimal control is much larger, leading to higher control rates (lower emissions). Further, we demonstrate that the magnitude of this effect grows with the number of decision stages represented, suggesting that for decision-dependent phenomena, the conventional two-stage approximation will lead to an underestimate of the effect of uncertainty.     

Newsvendor-type models with decision-dependent uncertainty

Models for decision-making under uncertainty use probability distributions to represent variables whose values are unknown when the decisions are to be made. Often the distributions are estimated with observed data. Sometimes these variables depend on the decisions but the dependence is ignored in the decision maker??s model, that is, the decision maker models these variables as having an exogenous probability distribution independent of the decisions, whereas the probability distribution of the variables actually depend on the decisions. It has been shown in the context of revenue management problems that such modeling error can lead to systematic deterioration of decisions as the decision maker attempts to refine the estimates with observed data. Many questions remain to be addressed. Motivated by the revenue management, newsvendor, and a number of other problems, we consider a setting in which the optimal decision for the decision maker??s model is given by a particular quantile of the estimated distribution, and the empirical distribution is used as estimator. We give conditions under which the estimation and control process converges, and show that although in the limit the decision maker??s model appears to be consistent with the observed data, the modeling error can cause the limit decisions to be arbitrarily bad.     

Factors affecting economic output in developed countries: A copula approach to sample selection with panel data

This work aims at determining the factors affecting economic output in developed countries. However, the definition of development depends on the criteria by which different principles provide different criteria of level of development. Therefore, there exists uncertainty about choice of sample or real development country and if the selected samples are not representative of the underlying population of real developed countries then the ordinary least squares coefficients may be biased. This paper examines the determinants of economic output in the panel data of 22 developed countries from 1996 to 2008 utilizing econometric techniques that take into account the selective nature of the samples. In general, there are two approaches to estimate the sample selection model, namely the maximum likelihood method and the method proposed by Heckman (1979) [21]. Moreover, these two approaches require that the joint distribution to be known. In general the multivariate normal distribution is assumed. However, this assumption can often be seen as excessively restrictive and this lead to uncertainty about the structure or assumption of joint distribution. Smith (2003) [37] suggests applying the copula approach, especially the Archimedean copula to the sample selection model and the result also shows that the copula approach is well suited to apply to a model where the sample selection is biased, using cross-section data. In our work, we employ the copula approach to construct the sample selection model in the case of panel data, resulting in the identification of significant factors affecting economic output.     

基于犹豫毕达哥拉斯模糊环境下的TODIM方法

在决策过程中TODIM方法能有效的捕捉决策者的心理行为。犹豫毕达哥拉斯模糊集不但能反映正反两个方面的不确定性,而且能反映决策者的犹豫程度。本文将TODIM方法扩展到犹豫毕达哥拉斯模糊集。首先定义了犹豫毕达哥拉斯模糊环境下的测量函数,用于比较两个犹豫毕达哥拉斯模糊数的大小,其次计算每个备选方案相对其它备选方案的相对优势度,然后根据相对优势度选出最佳方案。最后,用航空公司服务质量的评估来说明本文给出方法的可行性和有效性。