基于大数据的贷款客户信息修复与智能决策系统设计

为解决逾期高阶阶段出现的客户失联、资产情况无法评估、催收策略选择等问题，结合大数据技术构建贷款客户信息修复与智能决策系统。系统主要包括以下三个模块。数据集成模块：通过集成异构、碎片化的社交数据，形成结构化的文本数据集；数据挖掘模块：对文本数据集中的联系方式、地址、资产等信息进行识别和抽取，并基于词频证据对客户的联系方式、地址、资产等信息进行推理与判别；智能决策模块：基于if-then规则对贷款客户各特征属性下的催收策略分别打分，并利用多属性决策模型选择合适的催收策略。研究为贷款客户信息修复与智能决策系统的构建提供了全套的技术解决方案，具有一定的理论意义与实践意义。     

基于DCOM的动态运输调度多智能体系统研究*

针对物流运输调度中的客户需求动态性和随机性问题的解决,设计了一种基于DCOM的动态运输调度多智能体系统。在多智能体系统中,设计了包括预规划智能体、实时监控智能体、决策智能体和车辆智能体四类智能体。智能体以DCOM式组件形式实现,智能体之间的通信和协调由决策智能体集中执行。通过一个实例验证了系统算法的有效性,同时也为动态运输调度问题的解决提供了一个可参考的思路。     

报业广告智能决策系统的研究与应用

针对报业广告决策的特点,运用数据仓库技术和决策支持系统的思想开发出一套适应面广、灵活性高的智能决策支持系统。基于多层次决策应用的需要提出了虚拟集市的概念和一种改进的三层数据集市结构,并利用星型图数据建模技术将其成功应用到实际系统中;采用基于数据集市和联机分析处理的集成化智能决策支持系统模型,详细论述了系统的预测和客户分析功能模块并提出了基于层次分析法的客户忠诚度定量模型。该系统已成功应用到实际中,对报社广告部的决策提供了有力的支持,取得了良好的效果。     

基于双深度Q网络的智能决策系统研究

目前智能决策系统中的经典算法智能化程度较低,而更为先进的强化学习算法应用于复杂决策任务又会导致存储上的维度灾难问题。针对该问题,提出了一种基于双深度Q网络的智能决策算法,改进了目标Q值计算方法,并将动作选择和策略评估分开进行,从而获得更加稳定有效的策略。智能体对输入状态进行训练,输出一个较优的动作来驱动智能体行为,包括环境感知、动作感知及任务协同等,继而在复杂度较高的决策环境中顺利完成给定任务。基于Unity3D游戏引擎开发了虚拟智能对抗演练的验证系统,对演练实时状态和智能体训练结果进行可视化,验证了双深度Q网络模型的正确性和稳定性,有效解决了强化学习算法存在的灾难问题。该智能决策算法有望在策略游戏、对抗演练、任务方案评估等领域发挥作用。     

华为服务税务呼叫中心

此次展出了智能排队机及相关网络产品一揽子解决方案。华为 INtess 呼叫中心系统集程控交换技术、计算机技术、Internet 技术、网络技术和数据库技术于一体,通过搭建呼叫中心平台实现税务系统中咨询、宣传、投诉举报受理、业务查询及预受理、决策支持等多项功能,从而提供完整的税务呼叫中心解决方案。作为整个税务呼叫系统的核心接入设备,智能排队机以其运行的稳定性、业务的灵活性、     

.NET环境下SAGE系统智能客户端的研究与实现

本文以智能客户端应用程序模型为依托,结合实际案例——房屋出租管理系统SAGE（Storage Administrator Graphical Environment）系统,分析了智能客户端应用程序设计和实现过程中必须解决的关键技术难题,提出了完整的智能客户斌应用程序设计方案。最后基于.NET环境实现了SAGE系统智能客户端应用程序。     

基于模糊偏序关系评估决策的智能信息融合

针对信息融合系统应综合考虑获取信息所要求的资源、计算复杂度和时间所需要的最小成本的问题,提出了基于模糊偏序关系评估决策的智能信息融合系统,利用模糊偏序关系的排序方法,来对传感器进行评估决策和智能选择,并与证据理论相组合,应用于目标识别,仿真结果表明了该方法的有效性。     

基于排队论的呼叫中心ACD技术研究

本文首先阐述了ACD的排队理论;其次对先到先服务、优先级服务的排队策略,基于负载均衡、座席技能级别、客户信息和经验的路由算法进行了详细地分析;最后提出可根据呼叫到达时间、主叫号码、DNIS、用户可接受的等待时间、客户等级多项参数进行线性加权确定优先级的排队算法策略,根据系统规模、服务效率、客户信息等来综合地确定路由分配方法,真正实现合理的排队和智能的路由分配。     

基于FPGA的智能呼叫系统的设计

研究了智能呼叫系统,能实现64人呼叫,在有人呼叫时,启动语言对话系统,实现呼叫者和值班管理员对话,显示第一或优先呼叫者编码,并有光声提示,同时还能存储多人呼叫信息。将系统分成若干部分．主要包括呼叫信号编码和译码电路、呼叫存储电路、语音放大电路、控制电路、模拟电子开关电路、模拟选择／分配电路等部分,,数字部分功能用Ahera公司的FPGA器件EPF10K10TC144—3实现。     

基于VOIP技术构建保险公司新型客户服务中心系统

该文以中国太平洋保险公司客户服务系统成功应用为背景,分析了保险公司客户服务中心所面临的主要问题和关键因素,在充分考虑相关政策法规、保险信息化现状、保险服务流程等因素的基础上,采用网络多媒体技术为保险公司新型客户服务中心的建设提出解决方案,采用多点接入、多座席中心的建设模式,集语音、呼叫处理、计算机网络、数据库、短信、电子邮件等技术于一体,打造标准、灵活、自动、智能的保险客户服务中心。     

客户信用等级评价系统的研究与应用

主要介绍供电局客户信用等级评价系统的应用,在客户关系管理基础上采用J2EE的体系结构,以ORACLE 10.2做数据库,通过BI数据分析工具MicroStrategy8.1实现OLAP联机事务分析功能,为业务操作员人员提供简单方便的Web应用。该系统实现了对电力营销数据挖掘分析,可跟踪用电客户缴费情况,构建客户信用等级评估体系,形成客户信用预警机制,采取适宜的电费催收策略,有效规避客户欠费风险。全面提高供电企业决策水平、管理质量和工作效率。     

Integrating simulation modelling and multi criteria decision making for customer focused scheduling in job shops

Today, customer centricity is an important strategy in business-to-business markets and manufacturing companies need decision support systems that provide adequate information for customer centric applications. This study proposes an integrated decision support system that combines simulation modelling and multi-criteria decision making. More specifically, job shop lot streaming problem is dealt with, and it is aimed to determine the best dispatching rules to schedule batches on machines. To this aim, three renowned performance-oriented criteria; (i) mean flow time, (ii) percentage of tardy orders, (iii) makespan and one customer-oriented criterion; (iv) mean percentage deviation from the customer expectations are considered. Effect of different classical and customer-oriented dispatching rules on these performance criteria are investigated. The performance criteria are weighted using analytical hierarchy process by considering the level of bottleneck resource utilization and customer importance weights. The results reveal that customer-oriented dispatching rules provide better outcomes in case of high level of bottleneck resource utilization and high fluctuation amongst the customer importance weights.     

AN INTEGRATED FUZZY APPROACH FOR DETERMINING ENGINEERING CHARACTERISTICS IN CONCRETE INDUSTRY

This paper deals with the modeling of conceptual knowledge to capture the major customer requirements effectively and to transform these requirements systematically into the relevant design requirements. Quality Function Deployment (QFD) is a well-known planning and problem-solving tool for translating customer needs (CNs) into the engineering characteristics (ECs) and can be employed for this modeling. In this study, an integrated methodology is presented to rank ECs for implementing QFD in a fuzzy environment. The proposed methodology uses fuzzy weighted average method as a fuzzy group decision making approach to fuse multiple preference rankings for determining the weights of the customer needs. It adopts a fuzzy Analytic Network Process (ANP) approach which enables the consideration of inner dependencies in a cluster as well as the interdependencies between the clusters to determine the importance of ECs. The proposed approach is illustrated through a case study in ready-mixed concrete industry.     

Calling communities analysis and identification using machine learning techniques

The analysis of social communities related logs has recently received considerable attention for its importance in shedding light on social concerns by identifying different groups, and hence helps in resolving issues like predicting terrorist groups. In the customer analysis domain, identifying calling communities can be used for determining a particular customer’s value according to the general pattern behavior of the community that the customer belongs to; this helps the effective targeted marketing design, which is significantly important for increasing profitability. In telecommunication industry, machine learning techniques have been applied to the Call Detail Record (CDR) for predicting customer behavior such as churn prediction. In this paper, we pursue identifying the calling communities and demonstrate how cluster analysis can be used to effectively identify communities using information derived from the CDR data. We use the information extracted from the cluster analysis to identify customer calling patterns. Customers calling patterns are then given to a classification algorithm to generate a classifier model for predicting the calling communities of a customer. We apply different machine learning techniques to build classifier models and compare them in terms of classification accuracy and computational performance. The reported test results demonstrate the applicability and effectiveness of the proposed approach.     

Using BPNN and DEMATEL to modify importance–performance analysis model – A study of the computer industry

The importance–performance analysis (IPA) model has been widely used as the primary tool for customer satisfaction management. IPA is a 2-D matrix analysis based on the importance and performance of the organization from the customer perception of quality. The firm’s customer satisfaction management strategy is formulated according to the IPA analysis results. However, both conventional and modified IPA models have important hidden assumptions: (1) assumptions regarding the importance of quality characteristics and performance; (2) the assumption that performance and satisfaction have a linear relationship; (3) that quality characteristics are mutually independent variables, with no causal relationship. Under these assumptions, if the quality characteristics cannot meet the above-mentioned assumptions, the conventional and modified IPA models will not accurately analyze the importance and priority ranking for improvement, leading to wrongful decision making. This study puts forth a new decision making and analysis methodology that will, on one hand, exploit the back-propagation neural network (BPNN) to establish quality characteristics and the hidden important integral satisfaction assumptions. The decision making trial and evaluation laboratory (DEMATEL) is used to calculate the causal relationship and extent of mutual influence among the qualities to adjust the importance of the quality characteristics and identify the core Order-Winners and Qualifiers problems. The proposed method modifies the quality importance and improves the IPA model ranking and also resolves difficult practical problems with fewer resources. This study illustrates using Taiwan industrial computer, working in conjunction with IPA models established with BPNN and DEMATEL to observe its application and effect.     

A decision support system assessing the trust level in supply chains based on information sharing dimensions

Assessing customer trust in suppliers with regards to its influencing factors is an important open issue in supply chain management literature. In this paper, a customer trust index is designed as the trust level arising from the information sharing degree and quality, related to the information shared by a supplier with his customer. The customer trust level is evaluated using a fuzzy decision support system integrating information sharing dimensions. The core is a rule-based system designed using the results of questionnaires and interviews with supply chain experts. Several tests were generated in order to analyze the impact of the different information sharing attributes on the customer trust index. The developed approach is then applied to a real supply chain from the textile industry. Results show large differences of weight and impact between the different information-related factors that build the customer trust index. It is also shown that the proposed system has an important role in ensuring the objectivity of the trust assessment process and in helping decision makers evaluate their business partners.     

A decision support system for cooperative transportation planning: Design,implementation, and performance assessment

In this paper, we describe a decision support system for cooperative transportation planning in the German food industry where several manufacturing companies share their fleets to reduce transportation costs. Besides using vehicles of their fleets, there are different outsourcing options offered by logistics service providers, but these are much more expensive. The decision-making kernel of the decision support system is implemented as a multi-agent-system (MAS). The kernel provides a distributed hierarchical algorithm for cooperative transportation planning and an on-line data layer that contains all the information for decision making. We sketch the distributed hierarchical transportation planning algorithm and identity the required software agents. The MAS interacts via web services with a commercial tour planning system that persistently stores the resulting tour plans, orders, and customer data. Moreover, the tour planning system is used to offer graphical user interfaces to interact with the users. The data layer is updated by order and customer data from the ERP systems of the different manufacturing companies. We describe the architecture and the implementation of the MAS and the overall coupling framework. Furthermore, we discuss the simulation-based performance assessment of the resulting decision support system when the system is applied in a rolling horizon setting and present some computational results. The results demonstrate that the MAS approach is appropriate for the cooperative transportation planning domain.     

A decision support system for product design in concurrent engineering

Compared with the traditional sequential design method, concurrent engineering is a systematic approach to integrate concurrent design of products and their related processes. One of the key factors to successfully implement concurrent engineering is information technology. In order to design a product and its manufacturing process simultaneously, information on product features, manufacturing requirements, and customer demands must be processed while the design is concurrently going on. There is an increased understanding of the importance of the correct decisions being made at the conceptual design and development stages that involve many complex evaluation and decision-making tasks. In order to promote the efficiency in concurrent product development, appropriate evaluation and decision tools need to be provided. In this paper, the characteristics of fuzzy, multi-stage evaluation and decision making in concurrent product development process are analyzed and a decision support system for product design in concurrent engineering is presented. An example is given to illustrate the application of the system.     

Visualization method for customer targeting using customer map

An important issue in customer-oriented marketing area is target selection which enables the determination of potential customers. In this paper, we suggest a novel customer targeting method: customer map for a service industry. The customer map is the visualization method for customer targeting. It depicts value distribution across customer needs and customer characteristics. To develop the customer map, we integrate numerous customer data from various data sources, perform data analyses using data mining techniques, and finally visualize the information derived by the former analyses. The customer map helps decision makers to build customer-oriented strategy under the unified goal of customer targeting. It also affords to monitor and perceive real time state and the change of customer value distribution based on their information without preconception. We apply the customer map to a credit card company, build web-based prototype system for the customer map and acquire managerial implications from this study.