统计代写|决策与风险作业代写decision and risk代考|A Modified Risk Prioritization Approach

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损失大小的概率分布,在某个规定的时间段,如一年。这就是我认为大多数人在谈论某物的 “风险 “时的真正含义。

statistics-lab™ 为您的留学生涯保驾护航 在代写决策与风险decision and risk方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写决策与风险decision and risk方面经验极为丰富,各种代写决策与风险decision and risk相关的作业也就用不着说。

我们提供的决策与风险decision and risk及其相关学科的代写,服务范围广, 其中包括但不限于:

  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等楖率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (Generalized) Linear Models 广义线性模型
  • Statistical Machine Learning 统计机器学习
  • Longitudinal Data Analysis 纵向数据分析
  • Foundations of Data Science 数据科学基础
统计代写|决策与风险作业代写decision and risk代考|A Modified Risk Prioritization Approach

统计代写|决策与风险作业代写decision and risk代考|Using Best–Worst Method

Production is one of the most critical factors in increasing societies’ life quality and ensuring society’s continuity. The manufacturing industry takes a large share in the world economy (Cheung et al. 2017). Globalization and the rapid change of business dynamics threaten the sustainability of manufacturers. Manufacturers have to improve their production performance to compete with other companies continually (Kang and Subramaniam 2018; Zhou and ThaiN 2016).

Plastics are relatively inexpensive, strong, and highly corrosion-resistant materials with heat and hot insulation properties (Fuentes-Huerta et al. 2018). Plastic injection, one of the most frequently used methods in the production of plastic products, can be defined as the process of injecting plastic heated to a specific temperature into a mold under a certain pressure (Gökler and Boran 2020; Karasu and Salum 2018; Sadeghi 2000). This method is a very popular production method due to its high productivity, low surface roughness, and relatively low cost (Park and Dang 2017).
Production performance is affected by the uncertainty and difficulty of controlling many parameters, such as machine failures and production errors. Machine failures cause production to stop and increase unexpected costs of the business. The production of defective parts causes an increase in direct and indirect costs due to the enterprise’s internal or external low quality (Pan et al. 2010). Many methods have been proposed in recent years to reduce uncertainties and analyze failures in enterprises. One of these methods is FMEA (Bhattacharjee et al. 2020). FMEA was first proposed for the aviation industry in the 1960 s. FMEA is used extensively to identify, measure, and eliminate possible errors in systems and processes. FMEA is widely used, especially in the automotive, aviation, railway, and nuclear industries, due to its easy use and effective results ( $\mathrm{Li}$ et al. 2020; Wang et al. 2018). In the FMEA method, the risk assessment of each failure mode is made by evaluating the parameters with respect to severity (S), occurrence (O), and Detection (D). RPN is obtained by multiplying these parameters. The higher the RPN value, the higher the risk is considered; thus, it should be considered risk mitigation. Although RPN is an effective way to assess risks in practice, this assessment has several drawbacks (Zandi et al. 2020; Wang et al. 2018). It has been criticized by many authors (Gul et al. 2020; Başhan et al. 2020; Mandal and Maiti 2014; Yang et al. 2008). Different combinations of different risk parameters can come together to reach the same RPN level (Liu et al. 2011; Boran and Gökler 2020). Prioritizing failure modes in FMEA with respect to RPN is a process that requires multi-criteria decision-making (MCDM) analysis (Braglia et al. 2003). MCDM is an advantageous approach that can structure the risk analysis process by separating it into stages and enumerate risk factors by considering their importance. Therefore, especially in recent years, MCDM methods have been used in FMEA to avoid the disadvantage of traditional RPN calculation (Liu et al. 2019). Many studies integrate MCDM with FMEA in order to avoid the limits of classical FMEA. A detailed review was presented by (Liu et al. 2019).
It is aimed to evaluate alternatives among many criteria in MCDM methods. The evaluation is made by one or more decision-makers (DM), and the preferences of DM are revealed. Alternatives are ranked, graded, or selected (Mohammadi and Rezaei 2020 ). In the literature, there are many methods such as analytic hierarchy process (AHP) (Ak and Gul 2019; Gul 2018), analytic network process (ANP) (Khan et al. 2020; Matin et al. 2020), multi-attribute rating technique (SMART) (Fitriani et al. 2020 ; Siregar et al. 2017) that determine the weight of decision criteria based on the preference of DM. A pairwise comparison-based MCDM, called BWM in recent years, was proposed by Rezaei (2015). BWM is becoming widespread day by day because it requires less data, can make more consistent comparisons, and gives more consistent results (Mi et al. 2019).

统计代写|决策与风险作业代写decision and risk代考|Literature Review

FMEA has taken its place in the literature as a systematic method widely used in analyzing the modes and effects of failures that occur in processes, systems, or product/service of a production/service system. There are some drawbacks in calculating the RPN, formulated as a combination of the three-parameter structure in the classical FMEA. Numerous studies have been proposed in the literature to overcome these drawbacks. New and original approaches that use multi-criteria decision analysis-based methods and their integration with the concepts such as fuzzy set theory, gray theory, soft set theory, and neutrosophic set theory have developed FMEA (Liu et al. 2019, 2013). The drawbacks of the RPN logic that exist in classical FMEA, revealed in the literature, can be listed as follows (Başhan et al. 2020; Qin et al. 2020; Bhattacharjee et al. 2020; Wang et al. 2020; Rezaee et al. 2020; Baykasoğlu and Gölcük 2020; Fattahi et al. 2020; Lo et al. 2020; Gul et al. 2020; Di Bona et al. 2018; Ozdemir et al. 2017; Liu et al. 2019, 2013; Bozdag et al. 2015; Park et al. 2018; Liu 2016):

  • Apart from three parameters (S, O, and D), additional parameters that impact risk prioritization have not been fully considered (Liu et al. 2019; Di Bona et al. 2018). Therefore, parameters such as economic loss (e.g., percentage of the total annual budget fixed by the company for occupational health and safety measures), prevention, sensitivity to non-usage of personal protective equipment, sensitivity to non-implementation of reactive and proactive care, and the effectiveness of prevention measures and strategies must be functions of risk in an FMEA study (Seiti et al. 2020; Du et al. 2016; Lo et al. 2019).
  • Weights of three parameters are not considered in RPN calculation in classical FMEA (Park et al. 2018; Liu et al. 2013; Huang et al. 2017). To overcome this drawback and provide a weighted assessment formula, some multi-criteria methods, including pairwise comparison, assess the decision criteria (e.g., AHP, BWM) and can be used.
  • Different $S, O$, and $D$ ratings may result in different meanings in the same RPN. However, risk priorities are definitely different (Huang et al. 2017; Catelani et al. 2018; Du et al. 2016; Safari et al. 2016).$\mathrm{S}, \mathrm{O}$, and D parameters are not easy to study precisely because of their subjective evaluation on a scale of $1-10$. Using language terms in fuzzy numbers can better guide FMEA (Zhang et al. 2020; Mete 2019; Ozdemir et al. 2017; Zhao et al. 2017; Loet al. 2019; Kutlu \& Ekmekçioğlu 2012). More deficiencies can be found in Liu et al. (2013) and Liu et al. (2019). Both studies include two important literature reviews of FMEA-based studies.

统计代写|决策与风险作业代写decision and risk代考|MCDM for Risk Assessment

This section introduces the importance of MCDM methods for the risk assessment problem and a flow of the process of injecting MCDM into classical risk analysis techniques. MCDM is an operations research concept that includes many methods for selecting the best alternative, prioritizing, and classifying alternatives as a result of a systematic and mathematical series of steps. As with other decision problems, MCDM is looking for solutions to many problems related to risk assessment and management. The decision-making procedure for risk assessment requires considering a range of hazards or types of hazards based on different risk parameters. For this purpose, MCDM methods have been suggested in recent years as a powerful tool to assist decision-makers in prioritizing risks and to reduce risks to an acceptable level MCDM-based risk analysis applications are increasing day by day. Risk assessment and management includes many elements with different goals and criteria. The main feature of MCDM methods is flexibility over the judgments of the decisionmaker/makers. These methods aim to reach the ideal decision by assigning performance scores and weights. Figure $3.1$ demonstrates the flow of the process of injecting MCDM into a usual risk assessment procedure. Here, “risk parameter” can refer to the elements of a classical risk analysis tool. As an example, in a Fine-Kinney procedure, these are probability, exposure, and consequence. In FMEA, severity, occurrence, and detection are the core parameters. Other components of this process include hazard list (with their associated risk descriptions), MCDM method for risk parameter weighting (e.g., AHP, ANP, BWM, DEMATEL), MCDM method for risk prioritization (e.g., TOPSIS, VIKOR, WASPAS, GRA, COPRAS, MOORA), and decision-maker/expert.

统计代写|决策与风险作业代写decision and risk代考|A Modified Risk Prioritization Approach


统计代写|决策与风险作业代写decision and risk代考|Using Best–Worst Method

生产是提高社会生活质量和确保社会连续性的最关键因素之一。制造业在世界经济中占有很大份额(Cheung et al. 2017)。全球化和商业动态的快速变化威胁着制造商的可持续性。制造商必须提高生产绩效才能不断与其他公司竞争(Kang 和 Subramaniam 2018;Zhou 和 ThaiN 2016)。

塑料是相对便宜、坚固且高度耐腐蚀的材料,具有隔热和隔热性能(Fuentes-Huerta 等人,2018 年)。塑料注射是塑料制品生产中最常用的方法之一,可定义为在一定压力下将加热到特定温度的塑料注射到模具中的过程(Gökler 和 Boran 2020;Karasu 和 Salum 2018;Sadeghi 2000)。这种方法是一种非常流行的生产方法,因为它具有高生产率、低表面粗糙度和相对较低的成本(Park and Dang 2017)。
生产性能受到许多参数的不确定性和控制难度的影响,例如机器故障和生产错误。机器故障会导致生产停止并增加业务的意外成本。由于企业内部或外部质量低下,缺陷零件的生产导致直接和间接成本的增加(Pan et al. 2010)。近年来已经提出了许多方法来减少企业的不确定性和分析失败。其中一种方法是 FMEA (Bhattacharjee et al. 2020)。FMEA 于 1960 年代首次为航空业提出。FMEA 广泛用于识别、测量和消除系统和过程中可能出现的错误。FMEA应用广泛,特别是在汽车、航空、铁路、核工业,Li等。2020;王等人。2018)。在 FMEA 方法中,通过评估与严重性 (S)、发生率 (O) 和检测率 (D) 相关的参数来评估每种故障模式的风险。RPN 是通过将这些参数相乘而获得的。RPN值越高,考虑的风险越高;因此,应考虑降低风险。尽管 RPN 是一种在实践中评估风险的有效方法,但这种评估有几个缺点(Zandi et al. 2020; Wang et al. 2018)。它受到了许多作者的批评(Gul et al. 2020; Başhan et al. 2020; Mandal and Maiti 2014; Yang et al. 2008)。不同风险参数的不同组合可以共同达到相同的 RPN 水平(Liu et al. 2011; Boran and Gökler 2020)。在 FMEA 中针对 RPN 对故障模式进行优先级排序是一个需要多标准决策 (MCDM) 分析的过程 (Braglia et al. 2003)。MCDM 是一种有利的方法,它可以通过将风险分析过程分为多个阶段来构建风险分析过程,并通过考虑其重要性来列举风险因素。因此,特别是近年来,MCDM方法被用于FMEA中,以避免传统RPN计算的缺点(Liu et al. 2019)。许多研究将 MCDM 与 FMEA 相结合,以避免经典 FMEA 的局限性。(Liu et al. 2019) 进行了详细的审查。MCDM 方法已在 FMEA 中使用,以避免传统 RPN 计算的缺点(Liu et al. 2019)。许多研究将 MCDM 与 FMEA 相结合,以避免经典 FMEA 的局限性。(Liu et al. 2019) 进行了详细的审查。MCDM 方法已在 FMEA 中使用,以避免传统 RPN 计算的缺点(Liu et al. 2019)。许多研究将 MCDM 与 FMEA 相结合,以避免经典 FMEA 的局限性。(Liu et al. 2019) 进行了详细的审查。
它旨在评估 MCDM 方法中许多标准中的替代方案。评估由一个或多个决策者 (DM) 进行,并揭示 DM 的偏好。备选方案被排名、分级或选择(Mohammadi 和 Rezaei 2020)。在文献中,有很多方法,如层次分析法(AHP)(Ak and Gul 2019;Gul 2018)、网络分析法(ANP)(Khan et al. 2020; Matin et al. 2020)、多属性评级技术 (SMART) (Fitriani et al. 2020 ; Siregar et al. 2017) 根据 DM 的偏好确定决策标准的权重。Rezaei (2015) 提出了一种基于成对比较的 MCDM,近年来称为 BWM。BWM 正日益普及,因为它需要的数据更少,可以进行更一致的比较,并提供更一致的结果(Mi et al. 2019)。

统计代写|决策与风险作业代写decision and risk代考|Literature Review

FMEA 作为一种系统方法已在文献中占有一席之地,广泛用于分析生产/服务系统的过程、系统或产品/服务中发生的故障的模式和影响。计算 RPN 有一些缺点,它是经典 FMEA 中三参数结构的组合。文献中提出了许多研究来克服这些缺点。使用基于多准则决策分析的新方法及其与模糊集理论、灰色理论、软集理论和中智集理论等概念的集成开发了 FMEA(Liu 等人,2019 年,2013 年)。文献中揭示的经典 FMEA 中存在的 RPN 逻辑的缺点可以列举如下(Başhan et al. 2020; Qin et al. 2020; Bhattacharjee et al. 2020; 王等人。2020;雷扎伊等人。2020;Baykasoğlu 和 Gölcük 2020;法塔希等人。2020;罗等人。2020;古尔等人。2020;迪博纳等人。2018; 奥兹德米尔等人。2017;刘等人。2019, 2013; 博兹达格等人。2015;公园等人。2018; 2016 年 7 月):

  • 除了三个参数(S、O 和 D)外,尚未充分考虑影响风险优先级的其他参数(Liu 等人,2019;Di Bona 等人,2018)。因此,经济损失(例如,公司为职业健康和安全措施确定的年度总预算的百分比)、预防、对不使用个人防护设备的敏感性、对不实施反应性和主动性护理的敏感性等参数,并且预防措施和策略的有效性必须是 FMEA 研究中风险的函数(Seiti 等人 2020;Du 等人 2016;Lo 等人 2019)。
  • 经典 FMEA 的 RPN 计算中不考虑三个参数的权重(Park 等人 2018;Liu 等人 2013;Huang 等人 2017)。为了克服这个缺点并提供加权评估公式,可以使用一些多标准方法,包括成对比较,评估决策标准(例如,AHP,BWM)并且可以使用。
  • 不同的小号,这, 和D评级可能会导致同一 RPN 中的不同含义。然而,风险优先级肯定是不同的(Huang et al. 2017; Catelani et al. 2018; Du et al. 2016; Safari et al. 2016)。小号,这, 和 D 参数不容易精确地研究,因为它们的主观评价范围为1−10. 在模糊数中使用语言术语可以更好地指导 FMEA(Zhang et al. 2020; Mete 2019; Ozdemir et al. 2017; Zhao et al. 2017; Loet al. 2019; Kutlu \& Ekmekçioğlu 2012)。在 Liu 等人中可以发现更多的不足。(2013)和刘等人。(2019)。这两项研究都包括基于 FMEA 的研究的两个重要文献综述。

统计代写|决策与风险作业代写decision and risk代考|MCDM for Risk Assessment

本节介绍 MCDM 方法对风险评估问题的重要性以及将 MCDM 注入经典风险分析技术的流程。MCDM 是一种运筹学概念,包括许多用于选择最佳替代方案、确定优先级和对替代方案进行分类的方法,这些方法是系统和数学系列步骤的结果。与其他决策问题一样,MCDM 正在寻找与风险评估和管理相关的许多问题的解决方案。风险评估的决策程序需要根据不同的风险参数考虑一系列危害或危害类型。以此目的,近年来,MCDM 方法被认为是一种强大的工具,可以帮助决策者确定风险的优先级并将风险降低到可接受的水平。基于 MCDM 的风险分析应用日益增多。风险评估和管理包括许多具有不同目标和标准的要素。MCDM 方法的主要特点是决策者/制定者判断的灵活性。这些方法旨在通过分配性能分数和权重来达到理想的决策。数字3.1演示了将 MCDM 注入到通常的风险评估程序中的流程。这里,“风险参数”可以指经典风险分析工具的要素。例如,在 Fine-Kinney 程序中,这些是概率、暴露和后果。在 FMEA 中,严重性、发生和检测是核心参数。该过程的其他组成部分包括危害列表(及其相关的风险描述)、风险参数加权的 MCDM 方法(例如,AHP、ANP、BWM、DEMATEL)、风险优先级排序的 MCDM 方法(例如,TOPSIS、VIKOR、WASPAS、GRA、 COPRAS、MOORA)和决策者/专家。

统计代写|决策与风险作业代写decision and risk代考 请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考


在概率论概念中,随机过程随机变量的集合。 若一随机系统的样本点是随机函数,则称此函数为样本函数,这一随机系统全部样本函数的集合是一个随机过程。 实际应用中,样本函数的一般定义在时间域或者空间域。 随机过程的实例如股票和汇率的波动、语音信号、视频信号、体温的变化,随机运动如布朗运动、随机徘徊等等。


贝叶斯统计概念及数据分析表示使用概率陈述回答有关未知参数的研究问题以及统计范式。后验分布包括关于参数的先验分布,和基于观测数据提供关于参数的信息似然模型。根据选择的先验分布和似然模型,后验分布可以解析或近似,例如,马尔科夫链蒙特卡罗 (MCMC) 方法之一。贝叶斯统计概念及数据分析使用后验分布来形成模型参数的各种摘要,包括点估计,如后验平均值、中位数、百分位数和称为可信区间的区间估计。此外,所有关于模型参数的统计检验都可以表示为基于估计后验分布的概率报表。





随着AI的大潮到来,Machine Learning逐渐成为一个新的学习热点。同时与传统CS相比,Machine Learning在其他领域也有着广泛的应用,因此这门学科成为不仅折磨CS专业同学的“小恶魔”,也是折磨生物、化学、统计等其他学科留学生的“大魔王”。学习Machine learning的一大绊脚石在于使用语言众多,跨学科范围广,所以学习起来尤其困难。但是不管你在学习Machine Learning时遇到任何难题,StudyGate专业导师团队都能为你轻松解决。


基础数据: $N$ 个样本, $P$ 个变量数的单样本,组成的横列的数据表
变量定性: 分类和顺序;变量定量:数值
数学公式的角度分为: 因变量与自变量


随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。


多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。


MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中,其中问题和解决方案以熟悉的数学符号表示。典型用途包括:数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发,包括图形用户界面构建MATLAB 是一个交互式系统,其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题,尤其是那些具有矩阵和向量公式的问题,而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问,这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展,得到了许多用户的投入。在大学环境中,它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域,MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要,工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数(M 文件)的综合集合,可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。



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