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

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。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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|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|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。

## 广义线性模型代考

statistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

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。