Interval articulation of superiority and precise elicitation of priorities

This paper develops an approach to obtaining precise (point) estimates of priorities of objects, in particular, coefficients of importance of criteria. It is assumed that the judgements regarding the preference superiority of one object over another are given in the forms of intervals, that is by indicating the lower and upper bounds. The suggested approach is suitable both in the case of consistent and inconsistent preferences. In the former case, the available intervals are processed by contracting them to the maximum degree, in the latter, by extending them to the minimum degree. The degrees of contraction or extension are regarded as the indexes of indeterminacy or inconsistency of preferences, and treated as multiple objective functions. A method of transformation of such a multicriterial problem to a sequence of several linear programming problems is suggested.     

On some algorithms for limiting average Markov decision processes

We consider limiting average Markov decision processes (MDP) with finite state and action spaces. We propose some algorithms to determine optimal strategies for deterministic and general MDPs. These algorithms are based on graph theory and the construction of levels in some aggregated MDP.     

一种用于区域公路交通网络综合评价的方法研究

交通运输是国民经济中最重要的基础产业之一,它是支持工业化和市场经济发展的基本条件.利用数据包络分析的有关理论,给出了用于公路交通网络整体水平评价、公路网建设方案有效性评价以及有效路网预测的方法,同时,还探讨了模型的含义和有关性质.     

Sample-path optimality and variance-maximization for Markov decision processes

This paper studies both the average sample-path reward (ASPR) criterion and the limiting average variance criterion for denumerable discrete-time Markov decision processes. The rewards may have neither upper nor lower bounds. We give sufficient conditions on the system’s primitive data and under which we prove the existence of ASPR-optimal stationary policies and variance optimal policies. Our conditions are weaker than those in the previous literature. Moreover, our results are illustrated by a controlled queueing system. Research partially supported by the Natural Science Foundation of Guangdong Province (Grant No: 06025063) and the Natural Science Foundation of China (Grant No: 10626021).     

Decision making in stock trading: An application of PROMETHEE

The key issue for decision making in stock trading is selection of the right stock at the right time. In order to select the superior stocks (alternatives) for investment, a finite number of alternatives have to be ranked considering several and sometimes conflicting criteria. Therefore, we are faced with a special multicriteria decision-making problem. The purpose of this paper is to develop a decision-making model for selecting superior stocks in stock exchange and a model is provided in order to structure this problem. The proposed model is structured around two pillars: Industry evaluation and Company evaluation. The preference ranking organization method for enrichment evaluation (PROMETHEE) has been used for solving the problem. The model has been applied at Tehran Stock Exchange (TSE) as a real case and a survey from the experts in order to determine the effective criteria for industry evaluation and company evaluation has been conducted.     

基于多属性群决策的软件可靠性模型选择方案

基于模型评价准则的属性值和专家经验的偏好信息进行软件可靠性增长模型的选择,可归结为多属性群决策问题.模型评价准则的主观专家偏好可利用层次分析方法将其量化得到其主观排序权重;基于失效数据的模型评价准则的计算数据可利用熵权法获得客观排序权重.综合两种权重可得到既能反映专家经验又能综合实际软件可靠性测试数据的模型评价方案.实例分析表明该方案具有合理性和可行性.     

赋模糊权的拟强连通有向网络中的最佳树形图

本文在赋模糊数为弧权的拟强连通有向网络中，建立了根据模糊决策来求解最佳树形图的网络模型，并给出了这一模型的有效算法。     

一种基于模糊线性规划的主客观组合赋权方法

研究属性权重未知情况下的多属性评价问题。对决策矩阵进行规范化，将各被评价方案的综合评价值记为含未知属性权重的表达式，决策者对各评价方案的主观判断以区间数形式表示，并导出区间数判断矩阵的权重。根据含未知属性权重的被评价方案的综合评价值与决策者主观判断的模糊包含关系，提出采用模糊线性规划模型求解属性权重，并研究模型的应用范围。     

Learning dynamic prices in electronic retail markets with customer segmentation

In this paper, we use reinforcement learning (RL) techniques to determine dynamic prices in an electronic monopolistic retail market. The market that we consider consists of two natural segments of customers, captives and shoppers. Captives are mature, loyal buyers whereas the shoppers are more price sensitive and are attracted by sales promotions and volume discounts. The seller is the learning agent in the system and uses RL to learn from the environment. Under (reasonable) assumptions about the arrival process of customers, inventory replenishment policy, and replenishment lead time distribution, the system becomes a Markov decision process thus enabling the use of a wide spectrum of learning algorithms. In this paper, we use the Q-learning algorithm for RL to arrive at optimal dynamic prices that optimize the seller’s performance metric (either long term discounted profit or long run average profit per unit time). Our model and methodology can also be used to compute optimal reorder quantity and optimal reorder point for the inventory policy followed by the seller and to compute the optimal volume discounts to be offered to the shoppers.     

梯级水库群防洪系统多目标决策的灰色优选

将传统的优化技术与新发展起来的灰关联决策理论有机地结合起来,针对洪水调度的特点,提出了一个切实可行的梯级水库群洪水调度方案决策的灰色优选模型.最后,以乌江流域4个梯级电站的洪水调度方案优选进行了说明.