电子中介中具有模糊信息的多属性商品交易匹配方法研究

以电子中介中买卖双方单数量同类商品交易为实际应用背景,研究了具有模糊信息的多属性商品交易优化匹配方法。首先,在给出模糊信息数学描述的基础上,从买卖双方视角提出了新的基于改进模糊信息公理的交易匹配度计算方法,由于该方法在计算中充分考虑了模糊信息情形下买卖双方对商品需求的满意程度,因而由其计算得到的交易匹配度更具有合理性和现实意义。接着,以最大化买卖双方交易匹配度为优化目标,建立了电子中介中具有模糊信息的单数量多属性商品交易匹配模型,并通过模型的求解获得最优的匹配结果。实例计算研究表明,本文提出的匹配方法是可行和实用的。     

考虑后悔规避与匹配意愿的直觉模糊双边匹配方法

针对直觉模糊偏好信息的双边匹配问题,提出一种考虑匹配主体后悔规避心理行为和匹配意愿的双边匹配方法。首先,将双边主体的直觉模糊偏好信息转化为效用值;然后,依据后悔理论的思想,通过一方主体将另一方主体进行两两比较计算每个主体的后悔值和欣喜值,进而计算每个主体的总体后悔欣喜值,构建匹配满意度计算规则,建立双边匹配多目标优化模型,通过分析现有匹配意愿系数确定方法的不足,给出一种新的匹配意愿系数确定方法,在此基础上,考虑双边主体的匹配意愿,采用线性加权法将多目标优化模型转化为单目标规划模型进行求解,获得双边匹配结果;最后,通过一个算例验证了提出方法的可行性和有效性。     

Disturbance decoupling and model matching problems for discrete-time systems with time-varying delays

In this paper, the disturbance decoupling problem and the model matching problem for discrete-time linear systems with time-varying delays are considered. Solvability of the above problems is characterized by means of structural necessary and sufficient conditions that can be checked by algorithmic procedures. The basic method used to analyze the considered problems consists in representing the discrete-time linear systems with time-varying delays as switching linear systems, whose properties can be studied by a powerful structural approach. In this way, the considered control problems can be reduced to the corresponding problems for switched linear systems, whose solvability has been recently characterized.     

基于两粒度语言评价信息的双边匹配决策

针对基于两粒度语言评价信息的双边匹配问题,提出了一种了基于二元语义信息处理的决策方法。在该方法中,首先将两粒度语言评价信息转化为两粒度二元语义信息;考虑以每个主体满意度最大为目标,运用广义二元语义加权平均算子构建了多目标优化模型;进一步地,运用二元语义算术平均算子将多目标优化模型转化为双目标优化模型;根据二元语义的自身特点将双目标优化模型转化为单目标优化模型,进而进行求解来得到匹配方案。最后,给出一个算例说明所提供方法的有效性。     

基于区间犹豫模糊信息双向投影技术的双边匹配决策方法

近年来,双边匹配决策问题受到学者们的广泛关注,然而区间犹豫模糊信息双向投影技术的双边匹配决策问题的研究并不多见。因此,针对基于区间犹豫模糊信息下的双边匹配问题,本文提出了一种基于区间犹豫模糊信息双向投影技术的双边匹配决策方法。给出了区间犹豫模糊信息下的双边匹配问题的描述;依据双边主体给出的偏好信息构造区间犹豫模糊矩阵,运用双向投影法对区间犹豫模糊矩阵进行技术处理,构造双向投影矩阵;采用传统Topsis思想构造贴近度矩阵;在此基础上,构建了该双边匹配问题的优化模型,并使用组合满意度法对优化模型进行求解,从而得到了双边主体间的匹配结果;最后,通过算例说明了所提方法的有效性和可行性。     

Generating scenario trees: A parallel integrated simulation-optimization approach

A crucial issue for addressing decision-making problems under uncertainty is the approximate representation of multivariate stochastic processes in the form of scenario tree. This paper proposes a scenario generation approach based on the idea of integrating simulation and optimization techniques. In particular, simulation is used to generate outcomes associated with the nodes of the scenario tree which, in turn, provide the input parameters for an optimization model aimed at determining the scenarios’ probabilities matching some prescribed targets. The approach relies on the moment-matching technique originally proposed in [K. Høyland, S.W. Wallace, Generating scenario trees for multistage decision problems, Manag. Sci. 47 (2001) 295-307] and further refined in [K. Høyland, M. Kaut, S.W. Wallace, A heuristic for moment-matching scenario generation, Comput. Optim. Appl. 24 (2003) 169-185]. By taking advantage of the iterative nature of our approach, a parallel implementation has been designed and extensively tested on financial data. Numerical results show the efficiency of the parallel algorithm and the improvement in accuracy and effectiveness.     

基于分布式序关系的双边匹配决策方法

针对分布式序关系的双边匹配问题,提出了一种基于证据推理的决策方法。首先,以双边匹配满意程度为目标,把双边的各个评价信息作为证据,并通过证据融合求出匹配的满意度;接着,构建基于满意度的决策模型来获得匹配方案。最后, 通过算例说明该方法的应用。     

一种基于偏好序信息的双边匹配方法

针对双方主体给出匹配偏好序信息的双边匹配问题,提出了一种决策分析方法。首先通过构建双边匹配满意度函数,将匹配偏好序信息转化为匹配满意度,然后通过集结双方主体相互间的匹配满意度得到综合匹配满意度,进一步地,将综合匹配满意度视为双边主体之间匹配的权,进而将基于偏好序信息的双边匹配问题转化为完全二分图中的权匹配问题。为了解决双边匹配问题,构建了最大权匹配问题的优化模型,并通过模型的求解确定最优双边匹配结果。在本文最后,通过一个算例说明了提出方法的实用性和有效性。     

基于优势的双边匹配决策方法

传统的双边匹配方法根据主体双方给出的偏好序信息排序, 忽略匹配双方个体间存在的差异, 匹配结果不能很好的满足主体需求, 稳定性较差, 造成资源的错配甚至浪费。本文以人为出发点, 基于对匹配主体特征属性的优势结构识别, 提出新的序值依据, 将定性的不确定匹配标准依重视程度量化, 从而实现对人的多维度测量, 最大化个体差异, 以实现“按需匹配”的高稳定性、高满意度匹配结果。构建基于主体客观评价的优势属性量表; 引入个体综合情况的计算公式; 依托隶属度加权法把多目标优化转变成单目标优化; 运用Hungarain方法获得满意度最高且稳定匹配的指派方案; 最后通过算例证明本方法的科学性和可行性。     

Target Identification Using Dictionary Matching of Generalized Polarization Tensors

The aim of this paper is to provide a fast and efficient procedure for (real-time) target identification in imaging based on matching on a dictionary of precomputed generalized polarization tensors (GPTs). The approach is based on some important properties of the GPTs and new invariants. A new shape representation is given and numerically tested in the presence of measurement noise. The stability and resolution of the proposed identification algorithm is numerically quantified. We compare the proposed GPT-based shape representation with a moment-based one.     

Combining MFS and PGD methods to solve transient heat equation

We propose in this article a numerical algorithm based on the combination of the method of fundamental solutions (MFS) and the proper generalized decomposition technique (PGD) to solve time‐dependent heat equation. The MFS is considered as a truly meshless technique well adapted for a wide range of physical problems and the PGD approach can be considered as a reduction technique based on the separated representation of the variable functions. The proposed study relates to a separation between the spatial and temporal coordinates. To show the effectiveness of the proposed algorithm, several examples are presented and compared to the reference results.     

考虑患者个性化需求的医疗服务匹配决策方法

伴随着我国医疗服务事业的蓬勃发展,提升互联网医疗健康平台中医生和患者双方匹配的效率已经成为医疗服务领域中重要的问题之一。为此,本文针对考虑患者个性化需求的医疗服务匹配决策问题,提出了一种决策分析方法。首先,给出稳定和满意匹配方案的相关定义;在此基础上,根据患者个性化的需求类型,计算患者对医生的满意度;然后,根据医生提出的期望和患者的实际评价信息,计算医生对患者的满意度。进一步地,构建兼具满意且稳定的目标导向的匹配决策模型,通过运用求解模型的变步长算法,获得相应的最优匹配方案;最后,通过实例分析表明了所提出方法的可行性。     

基于优化模型的双边匹配决策方法

针对满意度表达评价信息的双边匹配决策问题,从匹配主体相互满意的视角,综合考虑匹配主体满意度的互补性和一致性定义匹配主体的组合满意度,以匹配的总组合满意度最大为目标建立求解匹配结果的优化模型,进而提出了一种匹配决策方法,并通过实例说明所提方法的可行性和有效性.     

An exact algorithm for the capacitated facility location problems with single sourcing

Facility location problems are often encountered in many areas such as distribution, transportation and telecommunication. We describe a new solution approach for the capacitated facility location problem in which each customer is served by a single facility. An important class of heuristic solution methods for these problems are Lagrangian heuristics which have been shown to produce high quality solutions and at the same time be quite robust. A primal heuristic, based on a repeated matching algorithm which essentially solves a series of matching problems until certain convergence criteria are satisfied, is incorporated into the Lagrangian heuristic. Finally, a branch-and-bound method, based on the Lagrangian heuristic is developed, and compared computationally to the commercial code CPLEX. The computational results indicate that the proposed method is very efficient.     

一种考虑中介交易态度的买卖双边匹配决策方法

电子中介的出现,为商品交易提供新的交易平台。本文以基于电子中介下的商品交易为背景,研究了考虑不同中介交易态度的买卖双边匹配决策问题。首先,给出买卖双边匹配决策问题的描述,然后,根据牟利型、服务型和折中型等3种交易态度中介的特点,以最大化匹配对价值、交易价格和达成度等为目标,分别建立了3种中介下的买卖双边匹配模型。进一步地,通过求解上述模型确定双边买卖匹配决策的结果。最后,通过一个实例分析说明出方法的有效性和可行性。本文进一步完善了买卖双边匹配的决策方法,具有实际应用价值。     

Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation

One of the problems that focus the research in the linguistic fuzzy modeling area is the trade-off between interpretability and accuracy. To deal with this problem, different approaches can be found in the literature. Recently, a new linguistic rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the linguistic 2-tuples representation that allows the lateral displacement of a label considering an unique parameter. This way to work involves a reduction of the search space that eases the derivation of optimal models and therefore, improves the mentioned trade-off.Based on the 2-tuples rule representation, this work proposes a new method to obtain linguistic fuzzy systems by means of an evolutionary learning of the data base a priori (number of labels and lateral displacements) and a simple rule generation method to quickly learn the associated rule base. Since this rule generation method is run from each data base definition generated by the evolutionary algorithm, its selection is an important aspect. In this work, we also propose two new ad hoc data-driven rule generation methods, analyzing the influence of them and other rule generation methods in the proposed learning approach. The developed algorithms will be tested considering two different real-world problems.     

An improved clustering method based on biological visual models

A clustering methodology based on biological visual models that imitates how humans visually cluster data by spatially associating patterns has been recently proposed. The method is based on Cellular Neural Networks and some resolution adjustments. The Cellular Neural Network rebuilds low-density areas while different resolutions find the best clustering option. The algorithm has demonstrated good performance compared to other clustering techniques. However, its main drawbacks correspond to its inability to operate with more than two-dimensional data sets and the computational time required for the resolution adjustment mechanism. This paper proposes a new version of this clustering methodology to solve such flaws. In the new approach, a pre-processing stage is incorporated featuring a Self-Organization Map that maps complex high-dimensional relations into a reduced lattice yet preserving the topological organization of the initial data set. This reduced representation is employed as the two-dimensional data set for further processing. In the new version, the resolution adjustment process is also accelerated through the use of an optimization method that combines the Hill-Climbing and the Random Search techniques. By incorporating such mechanisms rather than evaluating all possible resolutions, the optimization strategy finds the best resolution for a clustering problem by using a limited number of iterations. The proposed approach has been evaluated, considering several two-dimensional and high-dimensional datasets. Experimental evidence exhibits that the proposed algorithm performs the clustering task over complex problems delivering a 46% faster on average than the original method. The approach is also compared to other popular clustering techniques reported in the literature. Computational experiments demonstrate competitive results in comparison to other algorithms in terms of accuracy and robustness.     

考虑匹配意愿的直觉模糊双边匹配决策

本文从匹配意愿的视角研究了基于直觉模糊集信息的双边匹配问题。首先给出了直觉模糊集和双边匹配的概念;接着描述了考虑匹配意愿的直觉模糊双边匹配问题。为求解该问题,先将直觉模糊集矩阵转化为得分矩阵。以每个主体得分最大为目标,在一对一双边匹配约束条件下,建立了双边匹配模型。依据得分矩阵,计算匹配意愿矩阵;依据匹配意愿矩阵,将双边匹配模型转化为单目标优化模型;通过求解该模型获得“最佳”双边匹配。最后,通过一个人岗匹配实例说明了所提双边匹配决策的可行性和有效性。     

Average-optimal string matching

The exact string matching problem is to find the occurrences of a pattern of length m from a text of length n symbols. We develop a novel and unorthodox filtering technique for this problem. Our method is based on transforming the problem into multiple matching of carefully chosen pattern subsequences. While this is seemingly more difficult than the original problem, we show that the idea leads to very simple algorithms that are optimal on average. We then show how our basic method can be used to solve multiple string matching as well as several approximate matching problems in average optimal time. The general method can be applied to many existing string matching algorithms. Our experimental results show that the algorithms perform very well in practice.     

An efficient interval computing technique for bound-constrained uncertain optimization problems

In this article, a competent interval-oriented approach is proposed to solve bound-constrained uncertain optimization problems. This new class of problems is considered here as an extension of the classical bound-constrained optimization problems in an inexact environment. The proposed technique is nothing but an imitation of the well-known interval analysis-based branch-and-bound optimization approach. Efficiency of this technique is strongly dependent on division, bounding, selection/rejection and termination criteria. The technique involves a multisection division criterion of the accepted/proposed search region. Then, we have employed the interval-ranking definitions with respect to the pessimistic decision makers’ point of view given by Mahato and Bhunia [Interval-arithmetic-oriented interval computing technique for global optimization, Appl. Math. Res. Express 2006 (2006), pp. 1–19] to compare the interval-valued objectives calculated in each subregion and also to select the subregion containing the best interval objective value. The process is continued until the interval width for each variable in the accepted subregion is negligible and ultimately the global or close-to-global interval-valued optimal solution is obtained. The proposed technique has been evaluated numerically using a wide set of newly introduced univariate/multivariate test problems. Finally, to compare the computational results obtained by the proposed method, the graphical representation for some test problems is given.