管理者风险态度对系统能力受损后的应急采购决策影响研究

通信、电力和石化等大型运作系统核心能力受损后的应急期间内,管理者并非遵循风险规避的准则进行决策,为降低应急管理的成本,管理者是否应该采取激进或者保守的态度？为验证该观点,本文在文献[3]研究的基础上进行了拓展,在伙伴企业应急期间提供快速和正常两种能力支援的情境下,将管理者风险态度引入能力应急采购的决策问题。通过理论证明和数值仿真,本文得到了3个有益的应急管理结论:管理者的风险态度并不是越激进越好,"极端事件下采取双源供应"并非最优选择,应急期间管理者应首要考虑获得伙伴企业的能力恢复技术支援。     

Impact of Number of Interactions,Different Interaction Patterns,and Human Inconsistencies on Some Hybrid Evolutionary Multiobjective Optimization Algorithms*

We investigate the impact of the number of human–computer interactions, different interaction patterns, and human inconsistencies in decision maker responses on the convergence of an interactive, evolutionary multiobjective algorithm recently developed by the authors. In our context “an interaction” means choosing the best and worst solutions among a sample of six solutions. By interaction patterns we refer to whether preference questioning is more front‐, center‐, rear‐, or edge‐loaded. As test problems we use two‐ to four‐objective knapsack problems, multicriteria scheduling problems, and multiobjective facility location problems. In the tests, two different preference functions are used to represent actual decision maker preferences, linear and Chebyshev. The results indicate that it is possible to obtain solutions that are very good or even nearly optimal with a reasonable number of interactions. The results also indicate that the algorithm is robust to minor inconsistencies in decision maker responses. There is also surprising robustness toward different patterns of interaction with the decision maker. The results are of interest to the evolutionary multiobjective (EMO) community actively developing hybrid interactive EMO approaches.     

Multiple Attribute Decision Making with Inexact Value-Function Assessment

A major issue in value-function assessment is the possibility of receiving an indefinite response from a decision maker to a question about preferences. The conventional treatment requires a single, definite response from the decision maker. This approach either assumes that response error is zero or accepts the final solution as only approximate, without knowing how approximate it might be. In this paper we examine the issue of indefinite responses, define it more precisely, and present a method for incorporating it into decision making. The method is adaptive in that it is iterative and interactive with the decision maker who, at each iteration, is provided with information regarding the potential advantage of answering more trade-off questions and/or answering questions more carefully to reduce response error. In contrast to other approaches that “force” rationality and decisiveness, we work with irrationality and indecisiveness on the part of the decision maker. The method is demonstrated for the case of concave, increasing value functions.     

MRP-WSCI: Multiple reference point based weak and strong composite indicators

The construction of composite indicators is becoming an increasingly important way of managing great amounts of information obtained from multiple single indicators. Obviously, the aggregation of several indicators in a single synthetic measure always implies some loss of information on the way. For this reason, this aggregation must be done in the most informative possible way, so that the results obtained can be easily interpreted, and in an efficient way, so that the information contained in the composite indicator is maximal. In this paper, we propose a novel methodology based on the multicriteria reference point scheme. The decision maker can establish any number of reference levels for each indicator, and the final outcome can be interpreted in terms of the position with respect to these levels. Besides, two different aggregations are proposed: the weak indicator, allowing for full compensation among the single indicators, and the strong indicator, not allowing for any compensation. The joint visualization of both composite indicators provides valuable information that may be unnoticed using other existing approaches. The approach is illustrated using an example based on the EU-Regional Social Progress Index (EU-SPI).     

Describing decision bias in the newsvendor problem: A prospect theory model

In the newsvendor setting, we consider a prospect theory model with a stochastic-subjective reference point. The reference point is based on the pay-offs corresponding to the extreme values of demand; and, is influenced by the perception of costs associated with understocking and overstocking, and the pessimism level of the decision maker. Our prospect theory model describes the recent observations in the newsvendor experiments—the non-linear ordering behavior with respect to the profit margin and behavior at the extreme profit margins, in addition to the pull-to-center effect and asymmetry in ordering.     

基于后悔理论和DEMATEL的语言型多属性决策方法

针对目前语言型多属性决策方法大多基于期望效用理论且不考虑指标间影响关系的不足，提出了一种将后悔理论和决策试验与评价试验法相结合的语言型多属性决策方法。首先，依据后悔理论的思想，定义了语言后悔-欣喜函数，给出了方案感知效用值的计算公式；然后，利用决策试验与评价试验法分析指标间的影响关系，给出了基于语言DEMATEL的指标权重确定方法，再通过指标总容量最大优化模型给出了基于注水原理的指标权重确定方法，并在此基础上求解方案的综合感知效用值，据此对方案进行排序择优；最后，通过两个应用实例来验证所提方法的可行性和有效性。实例结果表明，由于该方法同时考虑了决策者的心理行为和指标间的影响关系，因此可使决策结果更加贴近现实且更为可靠。     

RUTA: A framework for assessing and selecting additive value functions on the basis of rank related requirements

In this paper, we present a new preference disaggregation method, called RUTA, which infers a set of additive value functions from the preference information referring to the desired ranks of some reference alternatives. Real-life experience indicates that people willingly refer to the range of allowed ranks that a particular alternative should attain, or to constraints on the final scores of the alternatives. We develop a mathematical model for incorporating such preference information via mixed-integer linear programming (MILP). Then, we discuss how decision making could be supported with the use of the already proposed extreme ranking analysis (ERA), which indicates the best and worst ranks gained by each alternative over the set of compatible preference model instances. We also introduce a new interactive UTA-like technique, which aims at selecting a single value function representing the outcomes of ERA. In the interactive process, the decision maker (DM) is assigning priorities to different pre-defined targets, which are built on results of ERA, and refer to the comparison of the best and/or worst ranks for pairs of alternatives. In particular, the DM may choose to emphasize or neglect the advantage of some alternatives over the others, in terms of results of ERA. In this way, one obtains a synthetic representation of extreme ranking analysis at a higher level of abstraction.     

基于一致性度量的概率模糊语言多属性群决策方法

人们通常习惯用语言术语来表达他们的偏好，因此概率型语言术语集（Probabilistic Linguistic Term Set，PLTS）在决策过程中有着十分重要的作用。目前PLTS的研究刚刚起步，有关PLTS的相关研究没有关注到一致性度量的问题，对于PLTS的多属性群决策方法有待进一步研究。首先，给出了一种新的PLTS的集结方法，并且在已有的PLTS可能度公式的基础上，构建了PLTS的相似度量方法，在此基础上，进一步提出了基于PLTS一致性度量的多属性群决策方法。该方法在各决策者权重未知的情况下，考虑到各决策者之间的一致性。首先，定义PLTS的一致性度量公式，确定决策者权重；并根据PLTS的集结方法，集结各决策者的评价信息；最后，利用可能度公式对PLTS进行排序。通过案例分析验证了该方法的可行性和优越性。     

Quantitative Comparison of Approximate Solution Sets for Bi‐criteria Optimization Problems*

We present the Integrated Preference Functional (IPF) for comparing the quality of proposed sets of near‐pareto‐optimal solutions to bi‐criteria optimization problems. Evaluating the quality of such solution sets is one of the key issues in developing and comparing heuristics for multiple objective combinatorial optimization problems. The IPF is a set functional that, given a weight density function provided by a decision maker and a discrete set of solutions for a particular problem, assigns a numerical value to that solution set. This value can be used to compare the quality of different sets of solutions, and therefore provides a robust, quantitative approach for comparing different heuristic, a posteriori solution procedures for difficult multiple objective optimization problems. We provide specific examples of decision maker preference functions and illustrate the calculation of the resulting IPF for specific solution sets and a simple family of combined objectives.     

A system for decision support without explicit objective functions

This paper presents a conversational decision support system: Resource Allocation in Business Operation under Uncertain Worth (RAINBOW). The focus is placed on the loosely structured decision situation where the multiple objective functions are given only implicitly and should be locally approximated by the decision maker as the decision making process proceeds. Basically, RAINBOW supports the process of convergence to a preferred alternative by giving the decision maker information which will help him form consistent evaluations of the utility function, the objective functions and the solution for the decision problem.     

纵向沟通中隐性信息获取的激励兼容机制研究

下属隐性信息的获取是确保上级决策者有效决策的一个重要基础,现有的研究未能解决隐性信息获取的效率问题与预算平衡的两难问题。本文通过确认决策者的作用,在分析模型中引入决策者成本,并借鉴政府征收"所得税"的思路,不仅解决了隐性信息获取的真实性,而且解决了原有方案未能同时兼顾的预算平衡、利润最大化以及向下属转移损失等问题。     

纵向沟通中隐性信息获取的激励兼容机制研究

下属隐性信息的获取是确保上级决策者有效决策的一个重要基础,现有的研究未能解决隐性信息获取的效率与预算平衡的两难问题。本文通过确认决策者的作用,在分析模型中引入决策者成本,并借鉴政府征收“所得税”的思路,不仅解决了隐性信息获取的真实性,而且解决了原有方案未能同时兼顾的预算平衡、利润最大化以及向下属转移损失等问题。     

Coordinating Service‐Sensitive Demand and Capacity by Adaptive Decision Making: An Application of Case‐Based Decision Theory*

The subject of this article is the simultaneous choice of product price and manufacturing capacity if demand is stochastic and service‐level sensitive. In this setting, capacity as well as price have an impact on demand because several aspects of service level depend on capacity. For example, delivery time will be reduced if capacity is increased given a constant demand rate. We illustrate the relationship between service level, capacity, and demand reaction by a stylized application problem from the after‐sales services industry. The reaction of customers to variations in service level and price is represented by a kinked price‐demand‐rate function. We first derive the optimal price‐capacity combination for the resulting decision problem under full information. Subsequently, we focus on a decision maker (DM) who lacks complete knowledge of the demand function. Hence the DM is unable to anticipate the service level and consequently cannot identify the optimal solution. However, the DM will acquire additional information during the sales process and use it in subsequent revisions of the price‐capacity decision. Thus, this decision making is adaptive and based on experience. In contrast to the literature, which assumes certain repetitive procedures somewhat ad hoc, we develop an adaptive decision process based on case‐based decision theory (CBDT) for the price‐capacity problem. Finally, we show that a CBDT DM in our setting eventually finds the optimal solution, if the DM sets the price based on absorption costs and adequately adjusts the capacity with respect to the observed demand.     

A comparative study of three methods of eliciting preference information

The study reported in this paper compared three methods of eliciting preference information from a decision maker and estimating weights with this information for use in a multiple objective decision making model. The design of the study included issues of implementing computerized interactive models vs more traditional question and answer techniques in the light of different decision models (in terms of level of complexity) and differing levels of decision maker experience. Results indicate that decision makers, regardless of prior exposure to computer terminals, are not intimidated by their use. Additionally, methods which required non-quantitative statements of preferences were preferred over techniques which requested numerical estimates of tradeoffs or marginal rates of substitution between objectives.     

Locating Facilities in the Presence of Disruptions and Incomplete Information*

In this article, we analyze a location model where facilities may be subject to disruptions. Customers do not have advance information about whether a given facility is operational or not, and thus may have to visit several facilities before finding an operational one. The objective is to locate a set of facilities to minimize the total expected cost of customer travel. We decompose the total cost into travel, reliability, and information components. This decomposition allows us to put a value on the advance information about the states of facilities and compare it to the reliability and travel cost components, which allows a decision maker to evaluate which part of the system would benefit the most from improvements. The structure of optimal solutions is analyzed, with two interesting effects identified: facility centralization and co‐location; both effects appear to be stronger than in the complete information case, where the status of each facility is known in advance.     

HIGH-LEVEL DECISIONS: A LESSON FROM THE IRAN HOSTAGE RESCUE OPERATION

We use the analytic hierarchy process to analyze the role of subjective factors in decision making as illustrated in the Iran rescue operation. Essentially, we show that a decision maker and that decision maker's advisors may differ in their estimates on whether an action should or should not be taken depending on what intangible factors the leader holds as personally important, factors that the others would not necessarily include in their thinking-a serious point for which we provide a particularly effective methodology.     

LEARNING WITH DETERMINISTIC DECISION RULES

While many problems of uncertainty are commonly analyzed by means of stochastic models, under certain circumstances this may not be an appropriate approach. The latter situation arises when the decision maker knows that the uncertain variables are not generated by a stochastic process, or when he is unwilling, or unable, to compute subjective probabilities. One of the nonstochastic approaches to uncertainty is the expectational approach in which the decision maker forms deterministic expectations about the uncertain aspects of his environment. This paper is concerned with some criteria for selecting among available expectations, or anticipations functions, and the possibility of ordering them according to these criteria. This study focuses especially on the learning criterion. The discussion brings out conceptual problems in connection with the definition of learning, as well as some technical difficulties that one encounters when attempting to compare different anticipations functions from the point of view of the learning criterion. As an illustration of the issues discussed, the paper reports on the results of some simulated decision rules. These show that decision rules in which no learning takes place, and in which some information is ignored, may perform better than more sophisticated rules.     

Preference-Order Recursion for Finding Relevant Pure,Admissible and Optimal Statistical Decision Functions*

A preference-order recursion algorithm for obtaining a relevant subset of pure, admissible (non-dominated, efficient) decision functions which converges towards an optimal solution in statistical decision problems is proposed. The procedure permits a decision maker to interactively express strong binary preferences for partial decision functions at each stage of the recursion, from which an imprecise probability and/or utility function is imputed and used as one of several pruning mechanisms to obtain a reduced relevant subset of admissible decision functions or to converge on an optimal one. The computational and measurement burden is thereby mitigated significantly, for example, by not requiring explicit or full probability and utility information from the decision maker. The algorithm is applicable to both linear and nonlinear utility functions. The results of behavioral and computational experimentation show that the approach is viable, efficient, and robust.     

考虑决策者后悔规避的风险投资项目选择方法

在风险投资项目选择过程中,决策者通常是有限理性的,会尽量避免选择可能会令其感到后悔的风险投资项目,而在涉及多个行业的风险投资项目选择过程中如何考虑决策者后悔规避的心理行为因素,这方面的研究特别需要关注。本文提出了一种涉及多个行业的考虑决策者后悔规避的风险投资项目选择方法。在该方法中,首先计算各风险投资项目在不同市场状态下的项目价值;然后,计算各组合市场状态发生的概率;进一步地,构建后悔函数刻画决策者后悔的心理感知,并通过计算不同行业的各风险投资项目的综合效用值得到风险投资项目的排序结果。最后,通过一个算例说明了本文提出方法的可行性与有效性。