基于消费者时间偏好和决策者风险偏好的定价与订货策略*

在供应链管理过程中, 消费者时间偏好和决策者风险偏好是影响产品定价与订货决策的两个重要因素。本文以累积前景理论为框架, 将消费者时间偏好与价格依赖等影响产品市场需求的因素和决策者风险偏好与参考依赖等影响决策的因素共同纳入模型考量范围, 建立了基于消费者的时间偏好和决策者风险偏好的定价与订货模型。研究表明：随着消费者时间偏好的增大, 产品的最优定价和订货量都将减少;随着决策者心理参考点的增大, 产品的最优定价降低,最优订货量增加;随着决策者损失规避程度的增大, 产品最优定价增加, 而最优订货量减少。考虑消费者时间偏好和决策者的风险偏好的定价与订货模型能够提高供应链中决策者的最大累积前景效用。     

基于CARA效用函数的报童决策偏差形成机理

经典报童模型假设决策者为风险中性, 但决策者的实际订购行为受风险偏好的影响, 导致实际订购行为与报童模型最优解之间存在偏差. 为此, 基于常绝对风险厌恶(CARA) 效用函数构建库存决策模型, 从风险偏好的角度解释报童决策偏差的形成机制. 行为实验结果表明: 随着产品的相对成本增加, 决策者的订购行为将出现风险逆转; 在高(低) 利润产品中, 决策者分别表现出风险规避(风险追逐) 的订购行为, 且订购行为受风险规避的影响更为显著. 报童决策偏差现象揭示, 库存管理实践中考虑决策者的风险偏好, 有利于缩小理论模型与管理实践之间的差距, 提高库存管理效率.

     

考虑供应商风险偏好的供应链系统动力学分析

基于前景理论,以供应商缺货损失及供应商备货过剩损失为供应商决策者风险感知来源,建立供应商决策者风险偏好模型。接着构建由供应商、制造商、分销商构成的三级供应链系统动力学模型,通过分析供应商决策者的风险偏好（回避、追求、中立）类型,调整风险感知来源的参数,分析供应商的风险偏好对供应链库存量、积压订单量、发货率等影响程度。研究表明,供应商决策者风险偏好对供应商的期初库存量、发货率以及积压订单量有较大影响,结合对风险感知来源的考虑可以弱化牛鞭效应,减少订单积压问题。仿真实验结果表明模型具有较高的可行性与稳定性,能结合决策者风险偏好对供应链整体运作优化提供理论支持。     

Solving multi-criteria decision making with incomplete linguistic preference relations

When there are n criteria or alternatives in a decision matrix, a pairwise comparison methodology of analytic hierarchy process (AHP) with the time of n(n ? 1)/2 is frequently used to select, evaluate or rank the neighboring alternatives. But while the number of criteria or comparison level increase, the efficiency and consistency of a decision matrix decrease. To solve such problems, this study therefore uses horizontal, vertical and oblique pairwise comparisons algorithm to construct multi-criteria decision making with incomplete linguistic preference relations model (InLinPreRa). The use of pairwise comparisons will not produce the inconsistency, even allows every decision maker to choose an explicit criterion or alternative for index unrestrictedly. When there are n criteria, only n ? 1 pairwise comparisons need to be carried out, then one can rest on incomplete linguistic preference relations to obtain the priority value of alternative for the decision maker’s reference. The decision making assessment model that constructed by this study can be extensively applied to every field of decision science and serves as the reference basis for the future research.     

基于方案修订的群决策一致性形成的研究

层次分析法(AHP)是群决策中经常使用的一种方法,利用AHP进行群决策的过程实质上也是决策者个体偏好集结的过程。针对如何将不同形式的偏好信息进行有效集结以形成群决策一致性方案的问题,提出一种通过活用AHP修订决策方案达到决策者群体的一致性偏好最终得以形成的方法,在利用决策者的决策信息进行群组划分的基础上明确各个划分的决策偏好差异,提出了活用AHP进行群决策一致性形成的方向,并形成了有效的动态群决策过程。     

Towards statistical consistency for stochastic constraint programming

In most industrial contexts, decisions are made based on incomplete information. This is due to the fact that decision makers cannot be certain of the future behavior of factors that will affect the outcome resulting from various options under consideration. Stochastic Constraint Satisfaction Problems provide a powerful modeling framework for problems in which one is required to take decisions under uncertainty. In these stochastic problems, the uncertainty is modeled by using discrete random variables to capture uncontrollable factors like the customer demands, the processing times of machines, house prices, etc. These discrete random variables can take on a set of possible different values, each with an associated probability and are useful to model factors that fall outside the control of the decision maker who only knows the probability distribution function of these random variables which can be forecasted, for instance, by looking at the past behavior of such factors. There are controllable variables on which one can decide, named decision variables which allow to model the set of possible choices for the decisions to be made. Finally, such problems comprise chance constraints which express the relationship between random and decision variables that should be satisfied within a satisfaction probability threshold – since finding decisions that will always satisfy the constraints in an uncertain environment is almost impossible.If the random variables’ support set is infinite, the number of scenarios would be infinite. Hence, finding a solution in such cases is impossible in general. In this thesis, within the context of an infinite set of scenarios, we propose a novel notion of statistical consistency. Statistical consistency lifts the notion of consistency of deterministic constraints to infinite chance constraints. The essence of this novel notion of consistency is to be able to make an inference, in the presence of infinite scenarios in an uncertain environment, based on a restricted finite subset of scenarios with a certain confidence level and a threshold error. The confidence level is the probability that characterises the extent to which our inference, based on a subset of scenarios, is correct whereas the threshold error is the error range that we can tolerate while making such an inference. The statistical consistency acknowledges the fact that making a perfect inference in an uncertain environment and with an infinite number of scenarios is impossible. The statistical consistency, thus, with its reliance on a limited number of scenarios, a confidence level, and a threshold error constitutes a valid and an appropriate practical road that one can take in order to tackle infinite chance constraints.We design two novel approaches based on confidence intervals to enforce statistical consistency as well as a novel third approach based on hypothesis testing. We analyze the various methods theoretically as well as experimentally. Our empirical evaluation shows the weaknesses and strengths of each of the three methods in making a correct inference from a restricted subset of scenarios for enforcing statistical consistency. Overall, while the first two methods are able to make a correct inference in most of the cases, the third is a superior, effective, and robust one in all cases.     

An economical cognitive approach for bi-objective optimization using bliss points, visualization, and interaction

When an optimization problem encompasses multiple objectives, it is usually difficult to define a single optimal solution. The decision maker plays an important role when choosing the final single decision. Pareto-based evolutionary multiobjective optimization (EMO) methods are very informative for the decision making process since they provide the decision maker with a set of efficient solutions to choose from. Despite that the set of efficient solutions may not be the global efficient set, we show in this paper that the set can still be informative when used in an interactive session with the decision maker. We use a combination of EMO and single objective optimization methods to guide the decision maker in interactive sessions.     

TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment

A single-valued neutrosophic set is a special case of neutrosophic set. It has been proposed as a generalization of crisp sets, fuzzy sets, and intuitionistic fuzzy sets in order to deal with incomplete information. In this paper, a new approach for multi-attribute group decision-making problems is proposed by extending the technique for order preference by similarity to ideal solution to single-valued neutrosophic environment. Ratings of alternative with respect to each attribute are considered as single-valued neutrosophic set that reflect the decision makers’ opinion based on the provided information. Neutrosophic set characterized by three independent degrees namely truth-membership degree (T), indeterminacy-membership degree (I), and falsity-membership degree (F) is more capable to catch up incomplete information. Single-valued neutrosophic set-based weighted averaging operator is used to aggregate all the individual decision maker’s opinion into one common opinion for rating the importance of criteria and alternatives. Finally, an illustrative example is provided in order to demonstrate its applicability and effectiveness of the proposed approach.     

A comparative analysis of information acquisition mechanisms fordiscrete resource allocation

Discrete resource allocation problems (RAPs) deal with making decisions that result in an optimal deployment of indivisible scarce resources among a group of agents so as to achieve the maximum aggregate utility. One prerequisite for solving the discrete RAP is that the decision maker be cognizant of the individual utility functions for the agents involved. When an agent's preference information is not available, the decision maker needs to gather such information through an inquiry process. The information acquisition process entails its own costs such as communication costs and computation costs. In this paper, three different information inference mechanisms merging, ranking, and entropy - are proposed and compared for the information acquisition process in the discrete RAP. It is found that the merging mechanism results in the least computation costs for the decision maker while the entropy mechanism incurs the least communication costs     

SUPREMAL INFERENCE MACHINE AND MEASURES OF CERTAINTY

Let us assume there exist several expert systems using different inference machines, working in parallel on the decision making problem from the same area and supplying for a given object (patient) probabilities of different diagnoses. The global results'(i.e. number of errors) on some sample data set of patients determine which of die machines is the best one. Is it possible, using the results (i.e. probabilities of diagnoses for the given patient) of the other machines, to improve decision power of the best machine? A method, the supremal inference machine or algorithm, is introduced attempting to combine different inference machines with the help of the random variable “error content in decision” whose density is constructed for different measures of certainty. Experimental results on a case study from the area of rheumatology are given.