基于着色Petri网的跨组织工作流建模研究

全球经济化一体化的发展使得现代企业需要通过跨组织协作来增强竞争力。本文提出一种基于着色Petri网的跨组织工作流协同建模方法,针对厂家线上直销这个典型的跨组织业务流程进行工作流建模,并通过可达图对所建立的模型进行了合理性验证。应用结果表明基于着色Petri建模可以对复杂的跨组织业务流程进行有效的描述,而且大大降低了模型的复杂性,使模型更加简约清晰。     

工作流过程建模方法及其智能化研究

针对传统的工作流过程建模在模型的描述方面缺乏柔性、全面性、智能化的问题,从思想方法到应用效果上分析比较了适合过程建模的主要工具,以此为基础,将它们有效结合,形成了一种新的建模方法：基于角色的Petri网建模,对该方法的模型和步骤进行了定义构造和应用设计。并进一步研究了使用智能技术构建过程模型的方法。新的建模方法能解决过程描述的刚性等不足,保证流程的完整和角色的有效交互,提高了模型对环境的适应性。     

工作流过程建模方法及其智能化研究

针对传统的工作流过程建模在模型的描述方面缺乏柔性、全面性、智能化的问题,从思想方法到应用效果上分析比较了适合过程建模的主要工具,以此为基础,将它们有效结合,形成了一种新的建模方法:基于角色的Petri网建模,对该方法的模型和步骤进行了定义构造和应用设计.并进一步研究了使用智能技术构建过程模型的方法.新的建模方法能解决过程描述的刚性等不足,保证流程的完整和角色的有效交互,提高了模型对环境的适应性.     

工作流网的资源正确性检查方法

工作流模型在运行过程中不仅要保证结构正确,还要保证资源使用正确,因此在建模阶段保证资源模型正确很重要。本文以工作流网为工作流建模方法,然后对其扩展,加入了资源的使用定义,并给出了关于资源正确性使用的一些基本定义与相关概念,在此基础上对其资源正确性使用给出了判定方法,最后讨论了关于资源使用冲突的解决方法。     

基于价值网模型的企业技术学习研究

本文基于价值网模型,对价值网内和价值网间企业的技术学习进行了研究。并将其与传统价值链和产业集群的技术学习方式进行了比较分析,论述了基于价值网的企业技术学习优势。     

基于petri网的网上教学的动态建模研究

文章针对网上教学的并行性、并发性、动态性的特点，提出了用Petri网对网上教学进行动态建模，并用矩阵方程对模型进行了分析研究。     

基于工作流管理系统的项目管理探讨

文章从工作流的概念及工作流管理系统的作用与组成出发,设计了一个由流程模型定义工具、业务建模工具,流程运行引擎服务与流程运行WEB组成的项目管理系统。     

工作流网的结构正确性验证

本文利用工作流网作为形式化建模工具,然后在此基础上研究了工作流网的结构正确性,其分析方法主要是利用了工作流网模型结构正确性与短回路网的活性和有界性的等价关系,然后结合Petri网的验证技术对自由选择网的活性和有界性进行分析,从而得到自由选择网的正确性,从而验证工作流网模型的结构正确性。     

基于Petri网高校教学管理流程建模方法——以学生成绩申诉流程为例

高校教学管理是各项管理工作的中心环节.提高教学管理水平,必须充分认识教学管理工作的内在规律,深刻理解教学管理工作的根本任务、目的与基本方法.教学管理的合理性决定了管理的有效性,同时高校教学管理业务流程的复杂性决定了必须有一套严谨的、精密的建模及验证方法.在不否定教学管理的行政管理属性和管理人员经验能力的前提下,管理流程在数学模型的基础上提出,通过分析验证后最终还原到具体的工作中.本文以学生成绩申诉处理流程为例,描述了如何建立基于Perri网流程模型,为模型的验证和优化打下基础.     

基于Multi-agent的工作流管理系统研究

传统工作流管理系统缺乏足够的自适应性、智能性、灵活性等处理能力,本文采用多智能体技术,将工作流管理系统中的各组成部分构造为智能体,提出和构造了一个具有高可塑性和高自主性的多智能体工作流管理系统的模型。     

流程挖掘研究

企业信息系统，例如企业资源计划（ERP），客户关系管理（CRM），供应链管理（SCM），Web服务等等都支持企业流程．但是它们都不能跟踪并改进流程。流程挖掘的目的就是从日志数据中抽取信息构建商业流程执行时的模型，从而能跟踪改进流程。本文简单介绍了流程挖掘涉及到的一系列方面，包括流程挖掘的定义、框架、研究难点。最后从九个方面对四种流程挖掘工具EMiT，Little Thumnb，InWoLvE，Process Miner进行了比较。     

基于工作过程的高职院校《平面设计》课程学习情境设计探讨

文章以对基于工作过程的《平面设计》课程学习情境进行分析,论述了《平面设计》课程学习情境设计的重要性及具体的学习情境设计,并提出了实施建议和今后任务,充分展现教学过程的实践性、开放性和职业性,努力实现理论和实践的有机结合。     

基于随机Petri网的IEEE 802.11DCF协议建模

本文首先提出了增强广义随机Petri网,增加了标识相关弧多样性和实施函数等特性,扩充了广义随机Petri网的模型功能,并给出了EGSPN的性能分析方法,建立了基于随机Petri网的802.11协议模型,并以RTS/CTS访问方式下协议性能计算及结果分析为例验证了该方法的有效性。     

Object detection based on visual memory: a feature learning and feature imagination process

ABSTRACT

Visual memory plays an important role for the human’s visual system to detect objects. The features of an object stored in the visual memory have much lower dimensions than the features contained within an image. We simulate the visual memory as a feature learning and feature imagination (FLFI) process to build an object detection algorithm. The method is constructed by a bottom-up feature learning and a top-down feature imagination. The proposed object detection method is tested using publicly available benchmark data sets, and the result indicates that it is fast and more robust.     

A service governance mechanism based on process mining for cloud-based applications

ABSTRACT

With the widely use of cloud computing in enterprise information systems, service governance becomes critical for an enterprise to adapt to a changing business environment. In order to provide a dynamic strategy for continuous service governance, in this paper a comprehensive service analysis solution is proposed to address both design time and run-time requirements for cloud applications. First, a process-centric Semantic Scene Model is composed to combines static business requirements with dynamic execution information. Then service analysis is conducted referring both to the business process mining results from event logs and the Semantic Scene Model given by business model designers. Service governance strategies are generated from the service analysis results and are performed to qualitatively classify the Quality of Service (QoS) of the services. Finally the approach is verified with a transportation logistics service application in a cloud platform. The result shows that our method can get continuous service governance through combining business processes with run-time service analysis.     

Portfolio management with robustness in both prediction and decision: A mixture model based learning approach

We develop in this paper a novel portfolio selection framework with a feature of double robustness in both return distribution modeling and portfolio optimization. While predicting the future return distributions always represents the most compelling challenge in investment, any underlying distribution can be always well approximated by utilizing a mixture distribution, if we are able to ensure that the component list of a mixture distribution includes all possible distributions corresponding to the scenario analysis of potential market modes. Adopting a mixture distribution enables us to (1) reduce the problem of distribution prediction to a parameter estimation problem in which the mixture weights of a mixture distribution are estimated under a Bayesian learning scheme and the corresponding credible regions of the mixture weights are obtained as well and (2) harmonize information from different channels, such as historical data, market implied information and investors׳ subjective views. We further formulate a robust mean-CVaR portfolio selection problem to deal with the inherent uncertainty in predicting the future return distributions. By employing the duality theory, we show that the robust portfolio selection problem via learning with a mixture model can be reformulated as a linear program or a second-order cone program, which can be effectively solved in polynomial time. We present the results of simulation analyses and primary empirical tests to illustrate a significance of the proposed approach and demonstrate its pros and cons.     

港口集装箱码头集卡优化调度研究

文中结合港口集装箱码头集卡调度的特点,提出现代集卡调度系统,通过两种调度模式的比较总结现代集卡调度系统的优点。并提出了集装箱码头物流系统有色Petri网建模的基本构架和一种新的遗传算法调度编码方式。     

A systematic mapping study of process mining

This study systematically assesses the process mining scenario from 2005 to 2014. The analysis of 705 papers evidenced ‘discovery’ (71%) as the main type of process mining addressed and ‘categorical prediction’ (25%) as the main mining task solved. The most applied traditional technique is the ‘graph structure-based’ ones (38%). Specifically concerning computational intelligence and machine learning techniques, we concluded that little relevance has been given to them. The most applied are ‘evolutionary computation’ (9%) and ‘decision tree’ (6%), respectively. Process mining challenges, such as balancing among robustness, simplicity, accuracy and generalization, could benefit from a larger use of such techniques.     

Workflow process modelling and resource allocation based on polychromatic sets theory

In workflow management, studying the relationship between workflow process activities and resource allocation is one of the interesting research topics. Polychromatic sets theory (PST) is a relatively new mathematical theory which is especially suitable for treating such problems. Based on PST, this paper proposes a framework of workflow process modelling and resource allocation. As the theoretical foundation, polychromatic sets (PS), polychromatic graph (PG) and isolation operation (IO) of the PS are introduced. Special net structure (SNS), a special PG with colourless nodes and concolourous edges, is also introduced, and a new workflow process model and its verification algorithm are presented. Furthermore, a workflow resource model based on the entity of PS is developed. Based on IO of PS, the allocation mechanism that considers workflow process and workflow resource is proposed. Finally, a case study is provided to demonstrate the effectiveness of the above-mentioned theory and method.     

Heuristics,learning and the business angel investment decision-making process

ABSTRACT

This paper extends the literature on the investment decision-making of business angels.

Using insights from the emerging body of research on entrepreneurial learning processes, particularly the use of heuristics and the nature of learning from meagre experience, we explore whether angels learn from experience, how they learn and what they learn. These issues are addressed using verbal protocol analysis, a methodology for examining decision-making in real time, with three groups of business angels with differing levels of investment experience, and with follow-up debriefing interviews with these angels. This reveals some differences in the speed of decision making and the emphasis given to various investment criteria. There is some evidence for the use of heuristics in the decision making process, and for the critical role played by vicarious learning from the experience of others. Learning in the individual angel decision making process is a social as well as an individual phenomenon.