Supplier risk assessment based on trapezoidal intuitionistic fuzzy numbers and ELECTRE TRI-C: a case illustration involving service suppliers

Many companies today have embraced the concept of risk management, usually in the form of enterprise risk management or supply chain risk management. Both are based on a holistic view of risks. Hence, risks related to specific functions within a company must be considered more broadly than previously. Risks, however, involve uncertainty, and the less specific the context in which risks are viewed, the more uncertainty will be involved. One particular way to express uncertainty is through trapezoidal intuitionistic fuzzy numbers (TrIFNs). In this paper, risks that are relevant for supplier risk assessments are first collected from the literature. Then it is illustrated how the multi-criteria decision analysis method ELECTRE TRI-C can be used for sorting suppliers into risk categories, when the risks as well as some of the method’s parameters are expressed with TrIFNs. In order to do this, we make use of a small modification of an existing method for converting TrIFNs into crisp values. The approach is illustrated in a case problem based on a company that is looking for service providers (suppliers) of electrical maintenance. The problem involves 20 suppliers that are sorted into three risk categories based on evaluations from 27 criteria. Results from the case study point to two low risk suppliers. A further ad-hoc analysis suggests one of these to be less risky than the other.     

Global investing risk: a case study of knowledge assessment via rough sets

This paper presents an application of knowledge discovery via rough sets to a real life case study of global investing risk in 52 countries using 27 indicator variables. The aim is explanation of the classification of the countries according to financial risks assessed by Wall Street Journal international experts and knowledge discovery from data via decision rule mining, rather than prediction; i.e. to capture the explicit or implicit knowledge or policy of international financial experts, rather than to predict the actual classifications. Suggestions are made about the most significant attributes for each risk class and country, as well as the minimal set of decision rules needed. Our results compared favorably with those from discriminant analysis and several variations of preference disaggregation MCDA procedures. The same approach could be adapted to other problems with missing data in data mining, knowledge extraction, and different multi-criteria decision problems, like sorting, choice and ranking.     

The optimal quantity discount that a supplier should offer

Quantity discounts are a useful mechanism for coordination. Here we investigate such discounts from the supplier's perspective, both from a non-cooperative game-theoretical approach and a joint decision model. Taking into account the price elasticity of demand, this analysis aids a sole supplier in establishing an all-unit quantity discount policy in light of the buyer's best reaction. The Stackelberg equilibrium and Pareto optimal solution set are derived for the non-cooperative and joint-decision cases, respectively. Our research indicates that channel efficiency can be improved significantly if the quantity discount decision is made jointly rather than non-cooperatively. Moreover, we extend our model in three directions: (1) the product is transported by a private fleet; (2) the buyer may choose to offer her customers a different percentage discount than that she obtained from the supplier; and (3) the case of heterogeneous buyers. Numerical case studies are employed throughout the paper to illustrate the practical applications of the models presented and the sensitivity to model parameters.     

Conditions for strong ellipticity and M-eigenvalues

The strong ellipticity condition plays an important role in nonlinear elasticity and in materials. In this paper, we define M-eigenvalues for an elasticity tensor. The strong ellipticity condition holds if and only if the smallest M-eigenvalue of the elasticity tensor is positive. If the strong ellipticity condition holds, then the elasticity tensor is rank-one positive definite. The elasticity tensor is rank-one positive definite if and only if the smallest Z-eigenvalue of the elasticity tensor is positive. A Z-eigenvalue of the elasticity tensor is an M-eigenvalue but not vice versa. If the elasticity tensor is second-order positive definite, then the strong ellipticity condition holds. The converse conclusion is not right. Computational methods for finding M-eigenvalues are presented.      

Modeling international investment decisions for financial holding companies

This research analyzes the internationalization process model developed by Johanson and Vahlne and derives two integer programming investment decision models that consider the risk attitudes of investment firms. Johanson and Vahlne’s model provides a starting point for building a model that suits the investment approach and decision making process of financial holding companies. In practice, when firms make an international investment decision, there is a need for a model that can generate outputs based on financial measures such as profit, investment returns, and tolerable levels of risk. Thus, in this paper, Johanson and Vahlne’s concepts are studied and financial managers are interviewed to derive models that match the investment decision procedures of the firms. The model helps firms manage the risks of their investments and derive accurate investment strategies based on investment objectives and constraints.     

A new approach for quantitative risk analysis

Project risk management aims to provide insight into the risk profile of a project as to facilitate decision makers to mitigate the impact of risks on project objectives such as budget and time. A popular approach to determine where to focus mitigation efforts, is the use of so-called ranking indices (e.g., the criticality index, the significance index etc.). Ranking indices allow the ranking of project activities (or risks) based on the impact they have on project objectives. A distinction needs to be made between activity-based ranking indices (those that rank activities) and risk-driven ranking indices (those that rank risks). Because different ranking indices result in different rankings of activities and risks, one might wonder which ranking index is best. In this article, we provide an answer to this question. Our contribution is threefold: (1) we set up a large computational experiment to assess the efficiency of ranking indices in the mitigation of risks, (2) we develop two new ranking indices that outperform existing ranking indices and (3) we show that a risk-driven approach is more effective than an activity-based approach.     

Structured risk management: filling a gap in decision analysis education

Risk management is a standard management tool that does not generally appear in decision analysis textbooks nor is it explicitly cited as part of the standard decision-analysis paradigm. In contrast, risk management articles and books describe how decision trees can be used to evaluate specific risk management strategies. In this paper we describe a series of steps that should be a routine part of every decision tree analysis. They are designed to assess the expected value of developing a risk management strategy with regard to different aspects of uncertainty. The method is intended to trigger a focused brainstorming session to search for specific strategies to manage targeted risks. The procedure adds structure to the value-enhancing dimension of decision analysis that creates new strategies with less risk and higher expected values. The material presented here can easily be incorporated into even an overview of decision analysis in a survey class of operational research.     

Measuring the coupled risks: A copula-based CVaR model

Integrated risk management for financial institutions requires an approach for aggregating risk types (such as market and credit) whose distributional shapes vary considerably. The financial institutions often ignore risks’ coupling influence so as to underestimate the financial risks. We constructed a copula-based Conditional Value-at-Risk (CVaR) model for market and credit risks. This technique allows us to incorporate realistic marginal distributions that capture essential empirical features of these risks, such as skewness and fat-tails while allowing for a rich dependence structure. Finally, the numerical simulation method is used to implement the model. Our results indicate that the coupled risks for the listed company’s stock maybe are undervalued if credit risk is ignored, especially for the listed company with bad credit quality.     

Risk taking with background risk under recursive rank-dependent utility

This paper examines how background risk affects risk taking under rank-dependent utility. I assume that a decision-maker facing a risk taking decision in the presence of background risk views these risks as composing a compound lottery, and recursively evaluates this compound lottery using rank-dependent utility. I show that adding background risk increases risk aversion whenever the utility-for-wealth function is risk vulnerable (Gollier and Pratt, 1996) in this model.     

Stochastic Optimization of Insurance Portfolios for Managing Exposure to Catastrophic Risks

A catastrophe may affect different locations and produce losses that are rare and highly correlated in space and time. It may ruin many insurers if their risk exposures are not properly diversified among locations. The multidimentional distribution of claims from different locations depends on decision variables such as the insurer's coverage at different locations, on spatial and temporal characteristics of possible catastrophes and the vulnerability of insured values. As this distribution is analytically intractable, the most promising approach for managing the exposure of insurance portfolios to catastrophic risks requires geographically explicit simulations of catastrophes. The straightforward use of so-called catastrophe modeling runs quickly into an extremely large number of what-if evaluations. The aim of this paper is to develop an approach that integrates catastrophe modeling with stochastic optimization techniques to support decision making on coverages of losses, profits, stability, and survival of insurers. We establish connections between ruin probability and the maximization of concave risk functions and we outline numerical experiments.     

Multistage decision-making fuzzy methodology for optimal investments based on experts’ evaluations

A new methodology of making a decision on an optimal investment in several projects is proposed. The methodology is based on experts’ evaluations and consists of three stages. In the first stage, Kaufmann’s expertons method is used to reduce a possibly large number of applicants for credit. Using the combined expert data, the credit risk level is determined for each project. Only the projects with low risks are selected.     

Precautionary paying for stochastic improvements under background risks

In a two-dimensional framework, we propose a general two-period decision model which extends the temporal precautionary saving and effort model. We relate the role of cross-prudence to the impact of background risks on paying for stochastic improvements of the future risk. We find that the effect of background risks introduced in the first period is consistent to signing cross derivatives of bivariate utility functions, which is independent of the type of stochastic improvements brought by additional paying; however, when the background risk occurs in the second period, that is not the case.     

先验及后验概率下最优决策的选择

如何以最低代价获得最优决策方案是现代企业管理所面临的基本问题之一。在进行风险型决策过程中,若能结合抽样理论,就可以以最低的代价找到先验概率下及修正后的后验概率下选择最优决策方案。     

Operational risk: Emerging markets,sectors and measurement

The role of decision support systems in mitigating operational risks in firms is well established. However, there is a lack of investment in decision support systems in emerging markets, even though inadequate operational risk management is a key cause of discouraging external investment. This has also been exacerbated by insufficient understanding of operational risk in emerging markets, which can be attributed to past operational risk measurement techniques, limited studies on emerging markets and inadequate data.     

Managing risk in the real world

Exploitation by corporate management of what we know about decision analysis and simulation techniques is not limited by an understanding of the relevant analytical frameworks or the availability of the computer programs necessary for data manipulations. Rather, effectively utilizing the power of these concepts is inhibited by our inability to weave them into the fabric of the real management systems and decision-making processes of the firm.Texts on decision analysis often belabor the unwillingness on the part of management to employ these sophisticated tools. Management, on the other hand, is neither enchanted nor convinced by the ‘black boxes’ (which are perceived as black magic) of the decision analyst. This paper will not argue the merits of where the responsibility lies. Rather, it will present a method for effectively integrating decision and simulation analysis into the real strategic decision-making process that actually exists within the firm. At least, the capability of employing such a method is now under our control; the author argues that if it is effected, then exploitation of these powerful analytical approaches by management can follow.Specifically, we will deal here with the application of decision analysis to the management of risk and return in petroleum exploration. This is in itself an important application of great practical significance. However, the principles illustrated through this example are widely applicable to many other areas. It is especially applicable to areas where risks are great and uncertainties are large. Such areas would include research, the marketing of new products, and proposed large capital projects. Therefore, careful attention will be given to the underlying principles involved, and to the implications of applying these principles in other areas.     

现金押运路线问题——基于多类型的风险

以人民币现金押运为研究背景,考虑了一种基于多类型风险的现金押运路线问题,以在途风险成本、库存现金风险成本以及运输成本为优化目标,建立了混合整数线性规划模型,并提出了一种基于多样化策略和改进邻域搜索的混合遗传算法,其中遗传算法对押运路线进行选择,贪心算法用来求解各类风险指标。数值实验分别对问题特性和算法性能进行了分析。实验结果表明:1)混合遗传算法能求解更大规模的问题,得到较好的解,并很好地平衡了运行时间和求解质量;2)多类型风险影响了行驶路线;3)客户的期望需求影响了库存现金风险。     

Comparative performance evaluation under uncertainty

In this article CCPR, a multidimensional framework for comparative performance evaluation is proposed, which is elaborated and illustrated through a real-life case. A particular feature of the approach is that it takes account of and corrects for the influence of risks, which are beyond the control of the decision maker. Here risk is seen as a multidimensional measure; it is expressed by means of sensitivities to unexpected changes of a multitude of risk factors. Furthermore, the approach corrects for differences in characteristics between the firms that are being compared. Some characteristics are fixed and thus uncontrollable for the decision maker, whereas others may be changed and controlled by the decision maker. The approach also answers whether the changes in firm characteristics made by management have been appropriate or not.     

On the statistical foundations of non-linear utility theory: The case of status quo-dependent preferences

An account of non-expected utility is given which resumes concepts of μ–σ-analysis from statistical decision theory and combines them with standard principles of preference theory such as weak order, continuity and stochastic dominance. A three-parameter family of probability-dependent utility functions is specified, which is governed by the decision maker's aspiration level, distribution of present wealth, or status quo, and discount parameter for future risks. The approach offers a simple resolution of the Allais Paradox and explains basic patterns of probability-dependent risk attitudes arising in theoretical and applied decision analysis.     

基于贝叶斯信息更新的风险规避库存策略研究

在贝叶斯库存控制研究中一个著名的结论是：当缺货需求不能被观测到时,最优贝叶斯库存水平总会高于短视策略库存水平,原因是决策者需要通过多订货来获取对需求分布的认识. 这是基于风险中性的研究,然后现实中决策者都期望规避风险. 基于贝叶斯信息更新研究了风险规避背景下需求部分可观测的多周期报童问题,决策者的周期内效用函数满足独立可加性公理. 通过引入非正规化概率,研究发现,对风险规避的决策者,当其效用函数具有不变绝对风险规避特征时,最优贝叶斯库存水平也会高于短视策略库存水平. 非正规化概率简化了动态规划方程与结果的证明.     

基于多维风险视角的政府卫生保健资源配置决策行为研究

研究在疾病风险和医学治疗风险同时共存的情形下,政府卫生保健资源的优先配置行为决策问题.当存在治疗风险(患病风险)时,分析病人患病的不确定性(治疗的不确定性)对政府卫生保健资源的配置效应,同时给出配置更多的卫生资源到更高风险病人群体的社会规划者的风险偏好条件.当这两类风险是局部的或正象限依赖的风险时,研究两种来源的风险对政府卫生保健资源的配置的联合影响.将之前学者提出的卫生保健资源的配置模型扩展到两类风险共存的情形,同时对于不确定下的卫生保健资源配置决策问题提供新的见解.