基于随机Petri网的遗传算法性能分析

本文采用随机Petri网方法,对遗传算法进行初步的性能分析。首先建立了遗传算法的Petri网模型,引入实施速率将其转化为SPN,同构为马尔科夫链,求出各个状态的稳定概率,得出算法各个步骤的利用率。试验结果验证了该方法的可行性。     

基于随机Petri网的零售企业数字化转型要素与演化情景仿真

运用扎根理论与案例研究方法，在提炼出影响零售企业数字化转型要素基础上，遵循“目标-行为-结果”逻辑思路，采用“位势-过程-路径”动态能力三维分析框架，提出其内在演化系统，构建零售企业数字化转型的随机Petri网模型与等价马尔科夫链模型，利用情景仿真方法对实例对象的均衡状态变动规律及其发展路径进行剖析。     

基于随机Petri网的突发事件信息演化模型

突发事件爆发后通常会引发事件信息的演化现象，而信息演化可能导致的负面舆情危机严重影响社会生活秩序，如何准确把握突发事件信息的演化规律是社会发展新形势下公共事务风险治理的重要内容。首先采用案例分析和结构化描述方法提取突发事件信息演化现象的内外部属性，并提出其演化系统。进而，构建该系统的随机Petri网模型及其同构的马尔可夫链。最后，通过情景仿真研究系统的均衡状态变动规律及其调控问题。研究过程中，提出突发事件信息演化现象的结构化描述、演化系统及不同演化状态，得出系统均衡状态变动规律及其调控机制。在此基础上，指出未来应考虑带有碰撞和耦合机制的多种信息演化规律及其调控问题。     

工作流协同系统性能评价研究

利用广义随机Petri网系统性能分析的优势,在组织间工作流模型基础上引入时间因素,并利用Petri网对局部工作流之间不同通信关系进行描述,最终构建了时间组织间工作流模型,为工作流协同系统性能评价问题提出了解决思路.     

重大传染病疫情演化情境下应急物资配置决策建模分析:以新冠肺炎疫情为例

基于对新冠肺炎疫情时空分布的分析,从时段、关键事件、传播动力学、空间分布、感染规模、信息特征、医疗资源等7个维度构建了重大传染病疫情演化的5种情境,提出了各种情境下需要解决的5个关键应急物资配置决策问题。综合考虑应急物资配置的空间、信息、物资、供应、需求和网络等特性,分析了每一个决策问题进行建模优化的关键因素。基于这些关键因素构建了一个多周期贝叶斯序贯决策模型,给出了求解步骤和解析解,并结合武汉疫情进行了算例分析,验证了模型的有效性。在重大传染病疫情演化情境下,综合考虑这些关键因素,应用贝叶斯决策分析进行应急物资配置决策建模有利于建立更加符合实际的决策模型,减少决策损失。     

震后应急医疗救援流程效率评价研究

为了最大程度减少地震灾害造成的人员伤亡，实施快速有效的应急医疗救援，在资源有限情景下，迫切需要提高应急医疗救援效率。通过案例分析方法提出了震后应急医疗救援的一般流程，构建了应急医疗救援流程的模糊随机Petri网模型，根据模糊随机Petri网与马尔科夫链的同构关系，得到系统状态的稳态概率表达式，据此分析震后应急医疗救援流程中的关键环节。在此基础上，考虑医疗资源投入的数量与救援工作效率之间的关系，引入时效性评估函数对关键环节的实施效率进行评价，通过理论推导证明同一资源配比存在最优值。以"汶川地震"为例，通过动态和静态分析，得到各状态下稳态概率变化情况，明确了震后应急医疗救援流程的关键环节。以救援过程中资源的投入量作为自变量，通过算例仿真得出医疗资源确定情况下关键环节的最优资源配比。由此对震后应急医疗救援过程提出相应对策与建议，可以为地震灾害应急医疗救援工作部署提供决策支持，促进灾后医疗救援工作的有序进行，实现应急医疗救援效率的提升。     

基于Petri网的电子商务建模分析

针对电子商务系统中事件是随机并行发生的特点,提出了利用Petri网理论来化解事件冲突的观点,并对电子商务系统进行Petri网建模,利用矩阵方程对模型进行分析研究,以提升电子商务系统的整体运作效率.     

应对突发公共卫生事件的途径

近年来,"非典"、禽流感、手足口病、甲型H1N1流感,一连串传染病疫情,把人们的目光一次次地聚焦到了"公共卫生"这四个字上,公共卫生安全成了全社会高度关注的敏感问题。突发性公共卫生事件,无论是重大传染病疫情、群体性不明原因疾病、重大食物和职业中     

基于扩展广义随机时间Petri网的流程银行建模及等价性能分析

对业务流程进行建模和性能分析是流程银行研究的重要课题.通过对广义随机时间Petri网进行扩展,将其用于对流程银行的业务流程建模.同时,基于活动执行时间为正态分布的假设,给出了4种流程元模式的等价性能分析方法.在此基础上,进一步把活动执行时间扩展到任意分布的情况,给出了通用的等价性能分析方法.     

公共危机治理中的社会信任修复研究——以重大动物疫情公共卫生事件为例

本文针对重大动物疫情公共危机事件中社会失信问题,构建了监督机构、企业和消费者群体三方演化博弈模型,并以4次调查"黄浦江浮猪"事件的数据为基础进行实证研究。研究认为,危机事件风险变化影响了监督机构、企业、消费者的行为决策,从而导致短期内社会信任明显降低。     

带有马尔可夫竞争因子的多产品报童模型

在竞争的环境下考虑多产品报童问题。为了刻画多产品间的竞争关系，本文利用马尔可夫链构建多产品的需求转移模型，并根据模型的转移概率矩阵提出马氏竞争因子和综合竞争力等核心概念，进而结合卷积公式和雅可比行列式，将竞争因子的概念纳入经典报童模型的框架，构建了带有马氏竞争因子的多产品报童模型，同时在该模型的理论基础上给出了新的决策机制。模型的数据实验表明：竞争因子对多产品库存系统的最优订购策略具有重要的影响，关于产品的综合竞争力为单调递增函数；将竞争因子纳入多产品库存系统的决策机制，可进一步拓展报童模型在实际应用领域中的适应性以及提高库存系统的优化与控制的效用。     

Markov and renewal models for total manpower system

This study compares the predictive utility of three stochastic models for both total manpower system and cohort personnel movement. The models are all discrete time versions, including a first order Markov chain, a Markov chain with duration of stay (semi-Markov) and a vacancy model having both renewal and Markov properties. The analysis covers a continuous 20 year period: 1950–1970 for a state police (U.S.A.) internal labor market. The simple Markov chain model is inadequate for long term cohort forecasts, but reasonably adequate for long term organizational forecasts. The semi-Markov model outperforms the simple Markov model for cohorts, but is surprisingly less accurate for the total organization. The heuristic information it portrays for the cohort is, however, quite informative. The best model for intermediate (5 year) and long term (10 year) forecasts in both cohort and organizational tests is the renewal type vacancy model. This finding is viewed as particularly important both in terms of empirical performance, which we expect can be improved due to the initial simplifying assumptions used, and in terms of further theoretical explication of the underlying causal process since internal staff flows are conceptualized as contingent on the opportunities available.     

考虑多种诱导信息下公众满意度的舆情危机响应决策方法

针对当前研究较少考虑到多种诱导信息情境下舆情危机响应决策问题的现状,首先在介绍研究理论和方法的基础上,对该决策问题进行了描述;然后,通过分析诱导信息的属性结构及其量化方式,运用前景理论从属性层面,提出了舆情危机特定情景和响应方案下公众对多种诱导信息发展态势满意度的计算方法;进一步地,考虑到方案对危机情景演变的影响,运用累积前景理论从公众满意度干预视角,提出了方案筛选的风险决策方法。最后,以一类重大传染病疫情场景中舆情危机响应决策问题为例,介绍该方法的潜在应用和说明其有效性。     

Market Impact on IT Security Spending

Traditionally, IT security investment decisions are made in isolation. However, as firms that compete for customers in an industry are closely interlinked, a macro perspective is needed in analyzing these decisions. We utilize the notions of direct‐ and cross‐risk elasticity to describe the customer response to adverse IT security events in the firm and competitor, respectively, thus allowing us to analyze optimal security investment decisions. Examining both symmetric and asymmetric duopoly cases using a continuous‐time Markov chain (CTMC) model, we demonstrate that optimal IT security spending, expected firm profits and willingness of firms to cooperate on security improvements are highly dependent on the nature of customer response to adverse events. We also examine the investment problem when security attacks on different firms are correlated.     

A Bayesian Evidence Synthesis for Estimating Campylobacteriosis Prevalence

Stakeholders making decisions in public health and world trade need improved estimations of the burden‐of‐illness of foodborne infectious diseases. In this article, we propose a Bayesian meta‐analysis or more precisely a Bayesian evidence synthesis to assess the burden‐of‐illness of campylobacteriosis in France. Using this case study, we investigate campylobacteriosis prevalence, as well as the probabilities of different events that guide the disease pathway, by (i) employing a Bayesian approach on French and foreign human studies (from active surveillance systems, laboratory surveys, physician surveys, epidemiological surveys, and so on) through the chain of events that occur during an episode of illness and (ii) including expert knowledge about this chain of events. We split the target population using an exhaustive and exclusive partition based on health status and the level of disease investigation. We assume an approximate multinomial model over this population partition. Thereby, each observed data set related to the partition brings information on the parameters of the multinomial model, improving burden‐of‐illness parameter estimates that can be deduced from the parameters of the basic multinomial model. This multinomial model serves as a core model to perform a Bayesian evidence synthesis. Expert knowledge is introduced by way of pseudo‐data. The result is a global estimation of the burden‐of‐illness parameters with their accompanying uncertainty.     

Alternative marketing systems for the apparel wool textile supply chain: filling the communication vacuum

Analysis of secondary data and information gathered from interviews with downstream wool supply chain members suggests that problems exist with the sourcing of raw wool for the apparel textile industry. Specific problems relate to ‘hard’ attributes, such as contaminated fibres and fibre diameter, as well as ‘soft’ attributes, such as origin of the wool and the nature of wool production systems. These problems may arise due to inadequate communication of quality attributes between chain members. The authors argue that more effective communication in the apparel wool supply chain requires the removal of ‘functional silos’, where supply chain members fail to look outside (upstream and/or downstream) their specific sectoral interests, and the continued dominance of the auction as the primary marketing system. The authors conclude that the apparel wool industry needs to co-operate to compete in dynamic, global markets increasingly dominated by synthetic fibres, in which vertically co-ordinated supply chains are the norm, auctions non-existent and communication is seen as a strength, rather than a weakness.     

Inventory cost saving benefits from the standardization of computer modular instruments

This article investigates the savings in inventory costs from the standardization of two computer modular instrument interface standards: NIM, an analog standard and CAMAC, a digital standard, at US nuclear laboratory installations. The major benefit from the standardization effort springs from the fact that the modular instruments are interchangeable, providing opportunities for reducing inventory costs. To that effect, the safety stock levels of instrument modules required for computer system configurations were estimated by an iterative procedure derived from a continuous Markov chain process with a one-to-one replacement policy. Since on the average, there are only two instruments of the same type per CAMAC or NIM crate, the opportunities for reducing inventory costs are a function of the number of crates and thus appear present only in medium and large systems. Large amounts of benefits of $206m (in 1981 dollars) were estimated from this successful standardization effort.     

基于归因理论的顾客对供应链其他成员服务失误的反应研究

基于归因理论视角探讨了顾客对供应链其他成员服务失误的反应。发现顾客倾向于将供应链其他成员的运营系统失误归因于核心服务提供者,而将员工自主性行为失误归因于供应链其他成员。对核心服务提供者的失误责任归因及可控性归因程度越高,顾客满意度和再惠顾意愿就越低;失误稳定性归因程度越高,顾客满意度和再惠顾意愿越高。     

An Application of Markov Chain Analysis to the Game of Squash*

If the score in a squash game is tied late in the game, one player has a choice of how many additional points (from a prespecified set of possibilities) are to be played to determine the winner. This paper constructs a Markov chain model of the situation and solves for the optimal strategy. Expressions for the optimal strategy are obtained with a symbolic algebra computer package. Results are given for both international and American scoring systems. The model and analysis are very suitable for educational purposes. The resulting Markov chain is small enough that it can be easily presented in a classroom setting, yet the model is sufficiently complex that algebraic manipulation is nearly hopeless. The final results illustrate the power of the combination of mathematical and computer modeling applied to a problem of practical interest.