基于概率犹豫模糊信息的三边单向非循环稳定匹配

本文针对单向非循环偏好下的三边匹配问题,基于概率犹豫模糊偏好信息,提出了一种稳定匹配算法。首先,针对三边单向非循环匹配问题,给出了概率犹豫模糊偏好元及其相对期望得分、相对偏差的定义,建立了对主体偏好排序的三级排序法;然后,基于三边主体偏好序,以匹配基数最大化及稳定匹配为目标,建立了三边单向非循环匹配的数学模型;进一步地,提出了阈值约束条件下的两阶段搜索优选算法,并对算法输出匹配的稳定性进行了证明;最后,通过一个实例验证本文所提算法的可行性和有效性。     

一种使用在线评论信息的商品购买决策分析方法

在线评论信息对消费者的商品购买决策影响非常显著,如何使用数据体量较大的在线评论信息来进行有针对性的商品购买决策分析是近年来尤为需要关注的一个新研究内容。本文提出一种使用在线评论信息的商品购买决策分析方法。首先,通过在线评论信息的挖掘来确定了消费者所重点关注的关于候选商品的商品特征及其权重;然后,通过消费者情感的分析来构建了关于各候选商品的商品购买决策矩阵;在此基础上,通过给出的一种基于随机TOPSIS的方案排序方法来进行了各候选商品的排序。最后,依据携程网提供的关于三家客栈的在线评论信息进行了数据实验,从而说明了本文提出方法的实用性与可行性。     

基于双因素理论的消费者满意度动态监测模型研究

为了提出大数据时代的消费者满意度动态监测方法,帮助企业实时了解消费者满意度动态及影响因素,从京东抓取乳制品评论数据,基于双因素理论和文本挖掘方法进行消费者满意度监测,并分析其影响因素.建立了激励因素和保健因素词库,构建了消费者满意度动态监测模型并绘制动态监测图,分析了满意度波动的原因.乳制品评论数据的实证表明,该模型能够有效监测消费者满意度,并分析波动的影响因素.     

改进蚁群算法优化周期性车辆路径问题

周期性车辆路径问题(PVRP)是标准车辆路径问题(VRP)的扩展,PVRP将配送期由单一配送期延伸到T(T＞1)期,因此,PVRP需要优化每个配送期的顾客组合和配送路径。由于PVRP是一个内嵌VRP的问题,其比标准VRP问题更加复杂,难于求解。本文采用蚁群算法对PVRP进行求解,并提出采用两种改进措施——多维信息素的运用和基于扫描法的局部优化方法来提高算法的性能。最后,通过9个经典PVRP算例对该算法进行了数据实验,结果表明本文提出的改进蚁群算法求解PVRP问题是可行有效的,同时也表明两种改进措施可以显著提高算法的性能。     

基于用户在线评论的旅游景点推荐算法研究

随着互联网的快速发展,推荐系统的研究和应用朝着多方向、多领域发展,传统的推荐算法已经不再满足某些特定领域准确率的要求.考虑到用户在线评论信息可以获得用户对产品偏好信息及偏好程度,文章提出一种基于用户在线评论的旅游景点推荐算法.首先,该算法用爬虫软件和Jieba分词对用户旅游景点的在线评论信息进行获取和预处理.其次,利用情感强度分析法确定每条评论相对景点各属性的评价标度.再次,依据处理后的在线评论信息计算用户对景点各属性的权重.最后,利用TOPSIS排序方法实现对用户旅游景点的推荐.实验表明,该算法可以有效的确定用户对景点的偏好程度,有效提升旅游景点推荐的准确性.     

基于Voronoi划分的同城即时配送优化策略研究

近年来经济社会发展及新零售业强势崛起使得平台或商家对大规模即时配送需求日益增加,在求解大规模车辆路径问题时仅使用启发式算法或其融合算法已无法满足实际需求。本文针对基于分众级的同城即时配送模式及现阶段存在的问题,确定了基于Voronoi划分算法的即时配送分区方法和对基础蚁群算法的三个改进策略;并以全程配送产生的总成本最少为目标函数,构建了带用户需求软时间窗的车辆路径问题数学模型;最后选取客户、车辆以及门店共计一百二十个真实地理位置数据,验证了本文提出的求解策略的有效性,并分析最终结果。结果显示,①使用Voronoi分区-改进蚁群算法的两阶段方法求解大规模车辆路径问题能显著减少配送总成本,同时提升客户满意度;②在多门店的条件假设下,采用改进蚁群算法求解得到的超时时间比基础蚁群算法少36%,配送总成本低17%。     

考虑共同客户的协同配送优化方法研究

本文针对一些客户仅需要一个配送中心提供配送服务，而某些客户需要多个配送中心提供配送服务(需要多个配送中心提供服务的客户就是企业的共同客户)的情形，提出了一类具有多配送中心、有时间窗限制的车辆路径问题，建立了相应的数学模型。基于“先分类，后求解”的思想，本文设计了两阶段启发式算法：第一阶段提出基于客户聚类的启发式算法，形成聚类信息，将多中心问题转化成单中心问题；第二阶段通过改进的蚁群算法对每个配送中心的情况进行求解。最后，通过算例对该模型的可行性和有效性进行了验证，结果表明与非协同配送方式相比，在配送距离、降低配送成本、提高客户满意度等方面均有明显改进。     

改进的布谷鸟算法求解物流配送中心选址问题

针对基本布谷鸟算法求解物流配送中心选址问题时存在搜索精度低、易陷入局部最优值的缺陷,提出一种改进的布谷鸟算法.算法采用基于寄生巢适应度值排序的自适应方法改进基本布谷鸟算法的惯性权重,以平衡算法的全局开发能力和局部探索能力;利用NEH领域搜索以提高算法的搜索精度和收敛速度;引入停止阻止策略对全局最优寄生巢位置进行变异避免算法陷入局部最优值、增加种群的多样性.通过实验仿真表明,改进的布谷鸟算法在求解物流配送中心选址问题上要优与基本布谷鸟算法以及其它智群算法,是一种有效的算法.     

基于偏好关系的制造工艺资源评价与选择的模糊决策方法

制造工艺资源的评价和选择是实现资源优化配置的关键技术.本文分析了协同工艺设计中制造工艺资源选择决策研究状况,应用模糊数和模糊决策理论,建立基于偏好关系的制造工艺资源模糊评价模型,研究一种基于偏好关系的隶属度函数求解算法和基于偏序关系的多方案制造工艺资源排序决策方法,最后给出一个算例验证本文提出的资源评价方法的合理性和有效性.     

模糊置换流水车间调度的多目标模型

为了求解同时考虑模糊加工时间和模糊交货期的多目标置换流水车间调度问题,提出一种模糊多目标调度模型。针对目标之一的最大化满意度,考虑决策者偏好,建立基于悲观准则的偏好满意度模型,并在此基础上,兼顾考虑可信度,对满意度模型进行改进;针对Pareto最优解的选取,引入模糊集理论和概率论,运用面积补偿法将最大模糊完工时间去模糊化,便于可行解之间进行比较。最后,采用随机系列算例以及典型算例进行优化计算,计算结果验证了模型的有效性。     

基于网络客户评论的产品口碑排序方法

电子商务时代,产品的网络口碑已成为消费者做购买决策的重要参考依据。本文利用客户评论信息,依据产品的评分及产品之间的对比投票数据,提出了一个新的产品口碑排序方法。首先利用产品两两间的对比投票计算各自的相对口碑,再采用贝叶斯平均方法修正原始客户评分,然后将二者结合得到产品的总口碑,进而对产品的网络口碑进行排序。实验数据采自第三方点评网站中的产品对比投票数据,实验结果表明本文提出的产品口碑排序方法具有较高的支持度,且与产品销量排序的相关性也很高。     

New algorithms for product positioning

Two new algorithms are proposed for the problem of positioning a new product in attribute space in order to attract the maximum number of consumers. Each consumer is assumed to choose the existing or new product closest to his ideal point according to the Euclidean norm. The first algorithm is based on finding a finite number of intersection points of indifference surfaces. The second algorithm proceeds by considering sets of balls bounded by indifference surfaces and finding points belonging to the largest weighted number of them. Problems with up to 500 consumers groups, 40 existing products and 20 attributes are solved exactly.     

消费者预防在线销售掺假行为的博弈模型及最优策略

建立了描述消费者与带掺假行为的在线零售商之间相互博弈的双层规划模型,其中消费者为领导者,在线零售商为随从者.消费者预防在线销售掺假行为的两种策略是进行商品品质检查和采用延期付款,在线零售商则依据消费者的预防策略决定是否销售掺假商品.根据消费者和在线零售商的可能采用的策略,对模型分四种情形展开分析与讨论,并分别在不同情形下得到了消费者与在线零售商的最优决策.结果表明,消费者延期付款的最优时间和进行商品品质检查能有效遏制在线零售商掺假行为.     

Particle swarm with equilibrium strategy of selection for multi-objective optimization

A new ranking scheme based on equilibrium strategy of selection is proposed for multi-objective particle swarm optimization (MOPSO), and the preference ordering is used to identify the “best compromise” in the ranking stage. This scheme increases the selective pressure, especially when the number of objectives is very large. The proposed algorithm has been compared with other multi-objective evolutionary algorithms (MOEAs). The experimental results indicate that our algorithm produces better convergence performance.     

Ensemble learning for the partial label ranking problem

The partial label ranking problem is a general interpretation of the preference learning scenario known as the label ranking problem, the goal of which is to learn preference classifiers able to predict a complete ranking with ties over the finite set of labels of the class variable. In this paper, we use unsupervised discretization techniques (equal-frequency and equal-width binning) to heuristically select the threshold for the numerical features in the algorithms based on induction of decision trees (partial label ranking trees algorithm). Moreover, we adapt the most well-known averaging (bootstrap aggregating and random forests) and boosting (adaptive boosting) ensemble methods to the partial label ranking problem, in order to improve the robustness of the built classifiers. We compare the proposed methods with the nearest neighbors-based algorithm (instance based partial label ranking) over the standard benchmark datasets, showing that our versions of the ensemble methods are superior in terms of accuracy. Furthermore, they are affordable in terms of computational efficiency.     

面向电子商务的应用——基于多Agen t的消费者需求代理系统

首先根据电子商务环境下消费者需求的特点,在对多A gen t技术论述的基础上,建立了基于多A gen t的消费者需求代理系统.接着对系统具体工作流程进行了深入分析:先针对不同消费者建立相应的用户模型,以便为不同消费者提供效用最大化的产品或产品服务的组合,然后采用协商型A gen t,完成网上产品或服务的交易.最后提出了基于遗传算法的协商谈判策略,以提高消费者需求代理系统的协商能力.     

A new stochastic ultrametric tree unfolding methodology for assessing competitive market structure and deriving market segments

We present a new methodology for simultaneously assessing competitive market structure and deriving market segments. A hierarchical or ultrametric tree representation is estimated in a maximum likelihood framework from collected paired-comparison choice data. The derived tree portrays both brands and consumers/households/segments as terminal nodes, where the ‘closer’ a brand is to a particular consumer/household/segment in the tree, the higher the predicted probability of that consumer/household/segment choosing that particular brand. This paper initially presents an introduction to the problem of market structure assessment. We review the extensive marketing literature on market structure and survey several competing methodologies. The proposed stochastic ultrametric tree unfolding methodology is technically described and several program options are indicated. An illustration of the proposed methodology is presented with respect to paired comparison choice data collected from a convenience sample involving the over-the-counter analgesics market. Finally, several areas for future research are identified.     

Distance-based and ad hoc consensus models in ordinal preference ranking

This paper examines the problem of aggregating ordinal preferences on a set of alternatives into a consensus. This problem has been the subject of study for more than two centuries and many procedures have been developed to create a compromise or consensus.We examine a variety of structures for preference specification, and in each case review the related models for deriving a consensus. Two classes of consensus models are discussed, namely ad hoc methods, evolving primarily from parliamentary settings over the past 200 years, and distance or axiomatic-based methods. We demonstrate the levels of complexity of the various distance-based models by presenting the related mathematical programming formulations for them. We also present conditions for equivalence, that is, for yielding the same consensus ranking for some of the methods. Finally, we discuss various extensions of the basic ordinal ranking structures, paying specific attention to partial ranking, voting member weighted consensus, ranking with intensity of preference, and rank correlation methods, as alternative approaches to deriving a consensus. Suggestions for future research directions are given.     

Multicriteria analysis in agricultural marketing: The case of French olive oil market

Agribusiness industries face a stiff competition originating mainly from the EU trade barrier’s removal and the rapidly changing marketing environment of the single European market. Therefore, certain need has been identified towards the development and proper utilization of updated market research tools and methodologies in the field of agricultural marketing. The aim of this paper is to show the usefulness of multicriteria approach in analyzing consumer’s preference data and its ability to support new product development processes by agricultural firms. The paper outlines first the philosophy of agricultural marketing by emphasizing on the features, which differentiate it from general marketing. Several methodological issues in agricultural marketing are then presented through a state-of-the art survey. Then, the paper develops a consumer-based methodology to support product development decisions where the key-role is played to determine the preference model which explains a single consumer’s ranking; a decision support system summarizes the analysis on the whole set of interviewed consumers to prescribe the `ideal' profile of a new product and to simulate its penetration strategy into the market. Results from the application of the methodology to a survey data base coming from the Paris olive oil market are presented. Finally, the paper concludes with some recommendations about marketing practice in agribusiness.