A fuzzy model to evaluate the suitability of installing an enterprise resource planning system

The use of enterprise resource planning (ERP) as a foundation for the integration of the complete range of business processes and functions, is clearly useful and economically profitable in most very large organizations which manage a great deal of data in their information systems. However, the decision of installing an ERP system in all the companies is not always so clear, it will depend on the size, future profits and other features of the company. Therefore, different parameters (features, aspects) will be evaluated to make a decision about the suitability of the ERP system. These parameters might have different nature or the knowledge about them could be vague or imprecise. Thus, this implies that it would be suitable that the evaluation process can manage heterogeneous information. In this paper we shall present a fuzzy evaluation model to evaluate the suitability of an ERP system based on a multi-expert decision-making (ME-DM) process that is able to deal with heterogeneous information.     

Job-shop排序问题的遗传强化学习算法

针对Job-shop排序问题的复杂性,提出一种遗传强化学习算法对其求解。通过引入多个随机变量,把Job-shop排序问题转换成多阶段决策问题,通过仿真手段构建作业排序问题模型环境,求取系统性能指标并保证解的可行性。设计一个多智能体Q-Learning算法和遗传算法相结合的算法用于解决Job-shop排序问题。仿真优化实验结果验证了该算法的有效性。     

基于模糊熵和证据推理的语言?? 数多准则决策方法

定义了语言??数及其模糊熵, 提出了基于模糊熵和证据推理的多准则决策方法, 以解决准则权系数信息不完全确定的语言??数多准则决策问题. 所提方法通过建立基于语言??数模糊熵的线性规划模型来得到准则的最优权系数, 利用证据推理算法确定方案的综合准则值, 进而得出最优方案. 最后通过实例验证了所提出方法的有效性和可行性.

     

多传感器基于动态时空特性软硬协作决策的目标检测研究

针对多传感器系统中目标检测时检测精度与效率的矛盾问题,研究了一种具有协同合作决策功能的目标检测算法及其传感器设计方案。首先将多传感器系统依据时域、空域和频域给出三维定义描述,分析了时空动态特性,其次依据二元检验和目标优化检测分析了软决策和硬决策在目标检测时变化规律,建立了一种基于协同合作的目标检测决策机制,最后给出了基于动态时空特性软硬协作决策的目标检测及其传感器头结构设计方案。实验表明,所提目标检测方案在检测精度、检测效率、资源利用率和覆盖率等方面具有明显优势。     

Variability in decision-making and critical cue use by different road users at rail level crossings

Collisions at rail level crossings (RLXs) are typically high-severity and high-cost, often involving serious injuries, fatalities and major disruptions to the transport network. Most research examining behaviour at RLXs has focused exclusively on drivers and consequently there is little knowledge on how other road users make decisions at RLXs. We collected drivers’, motorcyclists’, bicyclists’ and pedestrians’ self-reported daily experiences at RLXs for two weeks, focusing on behaviour, decision-making and information use in the presence of a train and/or activated RLX signals. Both information use and behaviour differed between road users. Visual information (e.g. flashing lights) was more influential for motorists, whereas pedestrians and cyclists relied more on auditory information (e.g. bells). Pedestrians were also more likely to violate active RLX warnings and/or cross before an approaching train. These results emphasise the importance of adopting holistic RLX design approaches that support cognition and behaviour across for all road users.

Practitioner Summary: This study explores how information use and decision-making at rail level crossings (RLXs) differ between road user groups, using a two-week self-report study. Most users make safe decisions, but pedestrians are most likely to violate RLX warnings. Information use (visual vs. auditory) also differs substantially between road user groups.     

The EU data protection reform and the challenges of big data: remaining uncertainties and ways forward

As the first broad reform of the EU data protection legislation is being achieved, and notwithstanding EU institutions’ confident discourse, scepticism remains about the reform’s ability to safeguard the fundamental right to data protection in the face of evolving data processing techniques underlying so-called big data. Yet, one might wonder whether the cause for this difficulty should be ascribed mainly to technological progresses that the law finds it hard to deal with or rather to the policy choices embedded in the legal reform itself. Indeed, a new data protection enforcement model is being adopted, which relies heavily on risk assessment and management by the data controllers themselves. Likewise, data protection authorities see their supervisory role significantly weakened. These developments and the underlying rationality are discussed. Given the limitations of the risk-based approach as currently devised, we suggest that it be reappraised in consideration of risk regulatory experiences in other domains.     

基于多Agent的医保欺诈检测系统的研究

针对医疗保险中屡屡出现的违规和欺诈行为,提出了一种基于多Agent的医疗保险欺诈检测系统。在系统中,通过利用Agent的智能性和多Agent的协同性对欺诈行为的检测做出决策。在决策过程中,各个Agent独立运行同时又互相合作,通过黑板机制进行互相的通信和交流,实现对欺诈行为的实时监控。文中阐述了该系统的总体结构、组成部分、工作机制以及实现的关键技术,并在JADE平台基础上进行了实证。通过实证,证明了系统具有灵活性、跨平台性和安全性等优点。     

云计算背景下企业IT 决策机制框架

云计算的不同应用水平会在不同程度影响企业的IT决策,相应的IT决策机制也会随之发生变化．本文深入分析了云计算的三层服务模式对IT决策的影响,由此提出了基于云计算背景的IT决策机制框架．并通过国外成功实施云计算的企业案例分析并验证本文得出的IT决策机制框架．本文不仅对以往的IT决策机制框架的基础上加入了IT服务模式维度,同时也为云计算在企业中的应用提供了制度框架．     

案例推理变权值引擎模型及权值计算方法

在案例推理（CBR）案例检索匹配中,不同案例通常由不同的特征构成。而传统的CBR引擎模型大多采用固定权值模式,导致系统在匹配精度方面的性能很低。为了解决这一问题,提出一种CBR变权值引擎模型,在其特征权值计算模块引入人机互动机制,基于群决策法计算主观权值,提出依据专家个体和群体决策差异的主观权值调整方法;基于相似粗糙集法计算客观权值。最后设计了一种综合权值调整算法,通过计算主观权值和客观权值间的距离,判断两者的偏离程度,从而推导出权值调整系数,得到最终的权值调整结果。通过网络攻击案例进行的算例分析和仿真实验验证了上述方法的正确性和优越性。     

一种基于案例推理的报价决策模型

在建筑行业的投标报价过程中,如何确定对最终投标报价决策影响较大的几项因素以及如何确定这些因素与最终投标报价决策之间的关系是一个棘手的问题。为此提出了基于案例推理的报价决策模型,并研究了该模型的可行性,使报价过程规则的自动提取成为可能。