全渠道拥抱消费者王朝

随着SoLoMo消费群的兴起，权利正在转移，全渠道零售将掀起新的零售革命。     

连锁零售供应链多级库存协同决策研究

针对连锁零售供应链多级库存资源的动态优化配置问题,提出了在上层对库存策略和下层对物流分配方案协同寻优的多级库存双层规划模型。借鉴细粒度模型遗传算法的遗传操作具有局部性的特点,模拟微观群体交互作用的局部性,基于细粒度模型遗传算法的Agent群体行为优化算法和基于复杂适应系统涌现机理的协同决策机制,进行连锁零售供应链多级库存协同决策研究。通过算例实验对模型的有效性进行了验证。仿真实验结果表明,通过连锁零售供应链微观个体Agent的群体行为优化,从系统工程的角度,实现了连锁零售供应链多级库存的动态资源优化配置和信息共享,降低了多级库存管理与运营的总成本。     

基于客户行为分析的电子渠道协同平台设计

针对目前电子渠道协同的现状，以及充分结合客户行为的相关理论，结合运营商的实际情况。详细介绍了基于客户行为分析的电子渠道协同平台的设计，通过协同原则的分析，建立起协同平台模型。并根据模型提出协同的应用功能和模块功能。通过该协同平台的设计，可以方便的实现满足协同模型的协同应用的实施。     

全渠道零售模式下企业供应链变革

何谓全渠道零售全渠道零售（Omni-Channel retailing）,就是企业为了满足消费者任何时候、任何地点、任何方式购买的需求,采取实体渠道、电子商务渠道和移动电子商务渠道整合的方式销售商品或服务,提供给顾客无差别的购买体验.产品/服务全面向数字化/移动互联化转移、多平台整合运营、粉丝经济和会员经济为核心、整个渠道扁平化的高效协同,这些都是全渠道零售的新特征.在该模式下,也对传统的供应链管理方法提出了新的挑战.     

数据挖掘技术在辅助决策系统中的应用

销售管理与辅助决策系统是以多年的销售数据为研究对象,采用关联规则挖掘和序列规则挖掘的方法,从中分析、挖掘和提取全面、综合、宏观的辅助决策信息,并能预测客户的购买状况,为客户和管理者提供可靠的决策信息,从而达到推荐自己产品的目的.     

基于关联规则的社会性网络行为研究

随着Web和Internet的迅猛发展,人们对资讯需求的深化,用户需求的细分将成为未来发展的趋势,同一类别用户的行为(即社会性行为)存在着特定的关系和规则的。本文以一个音乐共享平台来分析用户在线收听歌曲的社会性网络行为,对模型MusicMiner实现细节进行了描述,从数据挖掘的角度出发,应用关联规则的AprioriTid算法对大量的网络行为数据进行挖掘,得到了一些有用的关联规则。     

具有公平关切的零售商对双渠道供应链决策影响分析

针对市场需求信息不对称的双渠道供应链,考虑零售商具有公平关切心理,研究零售商的公平关切行为对供应链各成员决策及效用的影响.通过定义制造商判断市场需求规模的阈值,以获取零售商订单中所传递的市场需求规模信息,并得到零售商的最优订货量和制造商的最优直销量.研究发现,阈值随零售商公平关切程度的增加呈现递减趋势,且最终趋于常数. 数值实验结果表明,零售商的公平关切行为有利于增加自身的效用,但会损害制造商的利润.     

基于数据挖掘技术的虚拟社区成员行为研究

虚拟社区的研究对于社区的建设、管理和发展都具有重要意义。然而目前对虚拟社区和社区中的成员行为的定量研究还不多见，本文抽样采集了某一典型虚拟社区中的成员文章数据，在进行预处理中，利用关联规则的数据挖掘技术对其进行了挖掘，从而获取了虚拟社区中一些潜在的行为规律，并给出这些行为规律的相应描述，其研究结果具有一定的意义，对虚拟社区的建设、发展和管理起到预见和指导作用。     

制造商与零售商应对产品评论智能协同决策机制研究综述

产品评论作为供应链中产品信息共享的一种新的途径正在变得日益流行与重要,供应链中制造商与零售商正面临考虑产品评论条件下如何利用智能群推荐的协同决策。首先分析了制造商与零售商应对产品评论智能协同决策机制起源于供应链协同决策、群推荐系统及产品评论;然后对现存的制造商与零售商应对产品评论智能协同决策机制进行概述,包括了理论、策略及实证方面的成果,并分析这些机制之间的关系;最后对制造商与零售商应对产品评论智能协同决策机制研究文献进行分析归纳,提出理论、策略及实证方面研究问题,并指出将来研究内容与方向。     

数据挖掘技术在案件侦察决策中的应用

侦察决策是侦察者通过对案件进行分析后作出的侦察选择或者决定。在案件侦察过程中,采用数据挖掘技术开采海量的侦察数据,从中发现有价值的信息,是刻画案情和决策侦察行为的重要环节。因此,论文提出了一个基于数据挖掘技术的侦察决策辅助支持系统,探讨了该系统的核心功能。在数据挖掘方面,主要采用数据聚类和关联规则技术,并且通过使用案件侦破后修正规则库的手段,使该系统对规则具有一定的自我学习的智能功能。解决了海量的侦察数据与有价值的数据之间的矛盾,挖掘出可能的有价值的犯罪过程和犯罪信息,使侦察决策的形成更具有科学性和现实性。     

Agent-based modeling of consumer decision making process based on power distance and personality

Simulating consumer decision making processes involves different disciplines such as: sociology, social psychology, marketing, and computer science. In this paper, we propose an agent-based conceptual and computational model of consumer decision-making based on culture, personality and human needs. It serves as a model for individual behavior in models that investigate system-level resulting behavior. Theoretical concepts operationalized in the model are the Power Distance dimension of Hofstede’s model of national culture; Extroversion, Agreeableness and Openness of Costa and McCrae’s five-factor model of personality, and social status and social responsibility needs. These factors are used to formulate the utility function, process and update the agent state, need recognition and action estimation modules of the consumer decision process. The model was validated against data on culture, personality, wealth and car purchasing from eleven European countries. It produces believable results for the differences of consumer purchasing across eleven European countries.     

考虑决策者心理行为的灰色多属性群体决策方法

针对决策者在实际决策过程中表现出参照依赖和损失规避的现象,提出一种考虑决策者心理行为的灰色多属性群体决策方法.首先,利用"奖优罚劣"变换算子对原始决策信息进行规范化处理,以充分反映"奖优罚劣"思想;其次,依据决策群体信息,计算两两方案关于属性相比较时收益和损失的优势度,整合得到备选方案的全局优势度;再次,根据极大熵准则...     

Collaborative decision making algorithm for selection of optimal wire saw in photovoltaic wafer manufacture

The yield of the wafer slicing process has the greatest impact on manufacturing costs in the fabrication of photovoltaic (PV) cells. Hence, it is critical to identify the correct type of wire saw for this process. This paper employs the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) to construct a collaborative decision model for predicting the yield of a wire saw. The evaluation criteria for establishing the model are derived on the basis of a literature review and the opinions of experts with experience in PV wafer manufacturing. The evaluation weights are determined by the AHP and the optimal machine is identified by the TOPSIS. Finally, process capability indices are presented to demonstrate and verify the feasibility and effectiveness of the proposed method.     

Naturalistic decision making

OBJECTIVE: This article describes the origins and contributions of the naturalistic decision making (NDM) research approach. BACKGROUND: NDM research emerged in the 1980s to study how people make decisions in real-world settings. Method: The findings and methods used by NDM researchers are presented along with their implications. RESULTS: The NDM framework emphasizes the role of experience in enabling people to rapidly categorize situations to make effective decisions. CONCLUSION: The NDM focus on field settings and its interest in complex conditions provide insights for human factors practitioners about ways to improve performance. APPLICATION: The NDM approach has been used to improve performance through revisions of military doctrine, training that is focused on decision requirements, and the development of information technologies to support decision making and related cognitive functions.     

Systematic decision process for intelligent decision making

In this paper, Systematic decision process (SDP) for solving Multiple Criteria Decision Making problems with application for manufacturing location selection is introduced. SDP is a comprehensive approach which is based on eliciting strength of preferences for assessing additive utility functions. SDP consists of three steps: I. assessing weights, II. assessing qualitative criteria, and III. ranking alternatives using the assessed additive utility function. Strengths of preferences can be expressed by using either qualitative or numerical ratings. If the decision maker is inconsistent in his/her responses, such inconsistencies are identified by the method. It is shown that the method has advantages in terms of simplicity and accuracy compared to existing methods such as Analytical Hierarchy Process. Furthermore, a quadratic optimization method for assessing weights of additive utility function by use of pair comparison of actual alternatives is developed. Computational experiments are provided.     

Pareto multi-criteria decision making

The paper concerns design governed by multiple objective criteria, which are conflicting in the sense of competing for common resources to achieve variously different performance objectives (financial, functional, environmental, esthetical, etc.). A multi-criteria decision making (MCDM) strategy is proposed that employs a tradeoff-analysis technique to identify compromise designs for which the competing criteria are mutually satisfied in a Pareto-optimal sense. The MCDM strategy is initially developed for the case of design governed by n = 2 objective criteria. It is then extended to design governed by n > 2 objective criteria, by introducing the concept of primary and aggregate criteria. It is proved that, from among the theoretically infinite number of feasible designs forming the Pareto front for a design problem governed by n independent objective criteria, there exists a unique Pareto-compromise design that represents a mutually agreeable tradeoff between all n criteria. This remarkable result is illustrated for a flexural plate design governed by n = 2 criteria, a bridge maintenance-intervention protocol design governed by n = 3 criteria, and a media centre envelop design governed by n = 11 criteria.     

Multiknowledge for decision making

The representation of knowledge has an important effect on automated decision-making. In this paper, vector spaces are used to describe a condition space and a decision space, and knowledge is represented by a mapping from the condition space to the decision space. Many such mappings can be obtained from a training set. A set of mappings, which are created from multiple reducts in the training set, is defined as multiknowledge. In order to get a good reduct and find multiple reducts, the WADF (worst-attribute-drop-first) algorithm is developed through analysis of the properties of decision systems using rough set theory. An approach that combines multiknowledge and the naïve Bayes classifier is applied to make decisions for unseen instances or for instances with missing attribute values. Benchmark data sets from the UCI Machine Learning Repository are used to test the algorithms. The experimental results are encouraging; the prediction accuracy for unseen instances by using the algorithms is higher than by using other approaches based on a single body of knowledge.     

Interaction effects of consumers' product class knowledge and agent search strategy on consumer decision making in electronic commerce

This paper investigates the interaction effects between the search strategy of software agents and consumers' product class knowledge in the context of consumers seeking to purchase cars on the Internet. The research design used was a 2/spl times/4, between-groups, completely randomized, two-factor, factorial design. The independent variables that were manipulated were product class knowledge (HIGH KNOWLEDGE, LOW KNOWLEDGE) and agent search strategy [elimination by aspects (EBA STRATEGY), weighted average method (WAD STRATEGY), profile building (PROFILE STRATEGY), simple hypertext (HYPERTEXT STRATEGY)]. The dependent variables that were measured were satisfaction with the decision process (SATISFACTION), confidence in the decision (CONFIDENCE), trust in the agent's recommendations (TRUST), propensity to purchase (PURCHASE), perceived cost savings (SAVINGS), and cognitive decision effort (EFFORT). Significant differences were found in the affective reactions of the subjects toward the agent/application depending on the level of product class knowledge possessed by the subjects. Subjects with high product class knowledge had more positive affective reactions toward agents/applications that used the WAD and EBA strategies as compared to the PROFILE strategy. Subjects with low product class knowledge had more positive affective reactions to agents/applications that used the PROFILE strategy as compared to the EBA and WAD strategies.