Pub Date : 2016-09-10DOI: 10.1080/0740817X.2016.1204488
Weihong Hu, Mariel S. Lavieri, A. Toriello, Xiang Liu
ABSTRACT Analysts predict impending shortages in the health care workforce, yet wages for health care workers already account for over half of U.S. health expenditures. It is thus increasingly important to adequately plan to meet health workforce demand at reasonable cost. Using infinite linear programming methodology, we propose an infinite-horizon model for health workforce planning in a large health system for a single worker class; e.g., nurses. We give a series of common-sense conditions that any system of this kind should satisfy and use them to prove the optimality of a natural lookahead policy. We then use real-world data to examine how such policies perform in more complex systems; in particular, our experiments show that a natural extension of the lookahead policy performs well when incorporating stochastic demand growth.
{"title":"Strategic health workforce planning","authors":"Weihong Hu, Mariel S. Lavieri, A. Toriello, Xiang Liu","doi":"10.1080/0740817X.2016.1204488","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1204488","url":null,"abstract":"ABSTRACT Analysts predict impending shortages in the health care workforce, yet wages for health care workers already account for over half of U.S. health expenditures. It is thus increasingly important to adequately plan to meet health workforce demand at reasonable cost. Using infinite linear programming methodology, we propose an infinite-horizon model for health workforce planning in a large health system for a single worker class; e.g., nurses. We give a series of common-sense conditions that any system of this kind should satisfy and use them to prove the optimality of a natural lookahead policy. We then use real-world data to examine how such policies perform in more complex systems; in particular, our experiments show that a natural extension of the lookahead policy performs well when incorporating stochastic demand growth.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"1127 - 1138"},"PeriodicalIF":0.0,"publicationDate":"2016-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1204488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59755832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-09-09DOI: 10.1080/0740817X.2016.1200201
Chenxu Li, Yu An, Dachuan Chen, Qiulin Lin, Nian Si
ABSTRACT Closed-form likelihood expansion is an important method for econometric assessment of continuous-time models driven by stochastic differential equations based on discretely sampled data. However, practical applications for sophisticated models usually involve significant computational efforts in calculating high-order expansion terms in order to obtain the desirable level of accuracy. We provide new and efficient algorithms for symbolically implementing the closed-form expansion of the transition density. First, combinatorial analysis leads to an alternative expression of the closed-form formula for assembling expansion terms from that currently available in the literature. Second, as the most challenging task and central building block for constructing the expansions, a novel analytical formula for calculating the conditional expectation of iterated Stratonovich integrals is proposed and a new algorithm for converting the conditional expectation of the multiplication of iterated Stratonovich integrals to a linear combination of conditional expectation of iterated Stratonovich integrals is developed. In addition to a procedure for creating expansions for a nonaffine exponential Ornstein–Uhlenbeck stochastic volatility model, we illustrate the computational performance of our method.
{"title":"Efficient computation of the likelihood expansions for diffusion models","authors":"Chenxu Li, Yu An, Dachuan Chen, Qiulin Lin, Nian Si","doi":"10.1080/0740817X.2016.1200201","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1200201","url":null,"abstract":"ABSTRACT Closed-form likelihood expansion is an important method for econometric assessment of continuous-time models driven by stochastic differential equations based on discretely sampled data. However, practical applications for sophisticated models usually involve significant computational efforts in calculating high-order expansion terms in order to obtain the desirable level of accuracy. We provide new and efficient algorithms for symbolically implementing the closed-form expansion of the transition density. First, combinatorial analysis leads to an alternative expression of the closed-form formula for assembling expansion terms from that currently available in the literature. Second, as the most challenging task and central building block for constructing the expansions, a novel analytical formula for calculating the conditional expectation of iterated Stratonovich integrals is proposed and a new algorithm for converting the conditional expectation of the multiplication of iterated Stratonovich integrals to a linear combination of conditional expectation of iterated Stratonovich integrals is developed. In addition to a procedure for creating expansions for a nonaffine exponential Ornstein–Uhlenbeck stochastic volatility model, we illustrate the computational performance of our method.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"1156 - 1171"},"PeriodicalIF":0.0,"publicationDate":"2016-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1200201","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59755720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-11DOI: 10.1080/0740817X.2016.1189626
S. Frank, Steffen Rebennack
ABSTRACT The set of optimization problems in electric power systems engineering known collectively as Optimal Power Flow (OPF) is one of the most practically important and well-researched subfields of constrained nonlinear optimization. OPF has enjoyed a rich history of research, innovation, and publication since its debut five decades ago. Nevertheless, entry into OPF research is a daunting task for the uninitiated—both due to the sheer volume of literature and because OPF's ubiquity within the electric power systems community has led authors to assume a great deal of prior knowledge that readers unfamiliar with electric power systems may not possess. This article provides an introduction to OPF from an operations research perspective; it describes a complete and concise basis of knowledge for beginning OPF research. The discussion is tailored for the operations researcher who has experience with nonlinear optimization but little knowledge of electrical engineering. Topics covered include power systems modeling, the power flow equations, typical OPF formulations, and common OPF extensions.
{"title":"An introduction to optimal power flow: Theory, formulation, and examples","authors":"S. Frank, Steffen Rebennack","doi":"10.1080/0740817X.2016.1189626","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1189626","url":null,"abstract":"ABSTRACT The set of optimization problems in electric power systems engineering known collectively as Optimal Power Flow (OPF) is one of the most practically important and well-researched subfields of constrained nonlinear optimization. OPF has enjoyed a rich history of research, innovation, and publication since its debut five decades ago. Nevertheless, entry into OPF research is a daunting task for the uninitiated—both due to the sheer volume of literature and because OPF's ubiquity within the electric power systems community has led authors to assume a great deal of prior knowledge that readers unfamiliar with electric power systems may not possess. This article provides an introduction to OPF from an operations research perspective; it describes a complete and concise basis of knowledge for beginning OPF research. The discussion is tailored for the operations researcher who has experience with nonlinear optimization but little knowledge of electrical engineering. Topics covered include power systems modeling, the power flow equations, typical OPF formulations, and common OPF extensions.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"1172 - 1197"},"PeriodicalIF":0.0,"publicationDate":"2016-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1189626","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59755515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT Failure Mode and Effect Analysis (FMEA) is a reliability analysis technique that plays a prominent role in improving the reliability and safety of systems, products, and/or services. Although commonly used in quality improvement efforts, the conventional Risk Priority Number (RPN) method has been heavily criticized in the literature for its various limitations, such as in failure mode evaluations, risk factor weights, and RPN computation. In this article, we describe the application of an ELECTRE (ELimination Et Choix Traduisant la REalité)-based outranking approach for FMEA within the interval two-tuple linguistic environment. Considering different types of FMEA team members' assessment information, we employ a hybrid averaging operator to construct the group assessment matrix and use a modified ELECTRE method to analyze the group interval two-tuple linguistic data. Furthermore, the new risk-ranking model deals with the subjective and objective weights of risk factors concurrently, considering the degree of importance that each concept has in the risk analysis. The practicality and applicability of the proposed methodology are demonstrated by applying it to a risk evaluation problem of proton beam radiotherapy, and a comparative study is conducted to validate the effectiveness of the new FMEA approach.
失效模式与影响分析(FMEA)是一种可靠性分析技术，在提高系统、产品和/或服务的可靠性和安全性方面发挥着重要作用。尽管通常用于质量改进工作，传统的风险优先级数(RPN)方法在文献中因其各种局限性而受到严厉批评，例如在失效模式评估，风险因素权重和RPN计算方面。在本文中，我们描述了在区间二元语言环境中基于ELECTRE (ELimination Et Choix Traduisant la realit)的FMEA超越排序方法的应用。考虑到不同类型FMEA团队成员的评价信息，采用混合平均算子构建群体评价矩阵，并采用改进的ELECTRE方法对群体区间二元组语言数据进行分析。此外，新的风险排序模型同时处理风险因素的主观和客观权重，考虑每个概念在风险分析中的重要程度。将该方法应用于质子束放射治疗的风险评估问题，验证了该方法的实用性和适用性，并进行了对比研究，验证了该方法的有效性。
{"title":"An integrated failure mode and effect analysis approach for accurate risk assessment under uncertainty","authors":"Hu-chen Liu, Jian-Xin You, Shouming Chen, Yi-Zeng Chen","doi":"10.1080/0740817X.2016.1172742","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1172742","url":null,"abstract":"ABSTRACT Failure Mode and Effect Analysis (FMEA) is a reliability analysis technique that plays a prominent role in improving the reliability and safety of systems, products, and/or services. Although commonly used in quality improvement efforts, the conventional Risk Priority Number (RPN) method has been heavily criticized in the literature for its various limitations, such as in failure mode evaluations, risk factor weights, and RPN computation. In this article, we describe the application of an ELECTRE (ELimination Et Choix Traduisant la REalité)-based outranking approach for FMEA within the interval two-tuple linguistic environment. Considering different types of FMEA team members' assessment information, we employ a hybrid averaging operator to construct the group assessment matrix and use a modified ELECTRE method to analyze the group interval two-tuple linguistic data. Furthermore, the new risk-ranking model deals with the subjective and objective weights of risk factors concurrently, considering the degree of importance that each concept has in the risk analysis. The practicality and applicability of the proposed methodology are demonstrated by applying it to a risk evaluation problem of proton beam radiotherapy, and a comparative study is conducted to validate the effectiveness of the new FMEA approach.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"1027 - 1042"},"PeriodicalIF":0.0,"publicationDate":"2016-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1172742","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59755655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-06DOI: 10.1080/0740817X.2016.1146421
O. Vicil, P. Jackson
ABSTRACT In this article we develop a procedure for estimating service levels (fill rates) and for optimizing stock and threshold levels in a two-demand-class model managed based on a lot-for-lot replenishment policy and a static threshold allocation policy. We assume that the priority demand classes exhibit mutually independent, stationary, Poisson demand processes and non-zero order lead times that are independent and identically distributed. A key feature of the optimization routine is that it requires computation of the stationary distribution only once. There are two approaches extant in the literature for estimating the stationary distribution of the stock level process: a so-called single-cycle approach and an embedded Markov chain approach. Both approaches rely on constant lead times. We propose a third approach based on a Continuous-Time Markov Chain (CTMC) approach, solving it exactly for the case of exponentially distributed lead times. We prove that if the independence assumption of the embedded Markov chain approach is true, then the CTMC approach is exact for general lead time distributions as well. We evaluate all three approaches for a spectrum of lead time distributions and conclude that, although the independence assumption does not hold, both the CTMC and embedded Markov chain approaches perform well, dominating the single-cycle approach. The advantages of the CTMC approach are that it is several orders of magnitude less computationally complex than the embedded Markov chain approach and it can be extended in a straightforward fashion to three demand classes.
{"title":"Computationally efficient optimization of stock pooling and allocation levels for two-demand-classes under general lead time distributions","authors":"O. Vicil, P. Jackson","doi":"10.1080/0740817X.2016.1146421","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1146421","url":null,"abstract":"ABSTRACT In this article we develop a procedure for estimating service levels (fill rates) and for optimizing stock and threshold levels in a two-demand-class model managed based on a lot-for-lot replenishment policy and a static threshold allocation policy. We assume that the priority demand classes exhibit mutually independent, stationary, Poisson demand processes and non-zero order lead times that are independent and identically distributed. A key feature of the optimization routine is that it requires computation of the stationary distribution only once. There are two approaches extant in the literature for estimating the stationary distribution of the stock level process: a so-called single-cycle approach and an embedded Markov chain approach. Both approaches rely on constant lead times. We propose a third approach based on a Continuous-Time Markov Chain (CTMC) approach, solving it exactly for the case of exponentially distributed lead times. We prove that if the independence assumption of the embedded Markov chain approach is true, then the CTMC approach is exact for general lead time distributions as well. We evaluate all three approaches for a spectrum of lead time distributions and conclude that, although the independence assumption does not hold, both the CTMC and embedded Markov chain approaches perform well, dominating the single-cycle approach. The advantages of the CTMC approach are that it is several orders of magnitude less computationally complex than the embedded Markov chain approach and it can be extended in a straightforward fashion to three demand classes.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"955 - 974"},"PeriodicalIF":0.0,"publicationDate":"2016-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1146421","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59754342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-21DOI: 10.1080/0740817X.2016.1198064
B. Lunday, M. Robbins
ABSTRACT This research improves upon the monopsonist vaccine formulary design problem in the literature by incorporating several modeling enhancements and applying different methodologies to efficiently obtain solutions and derive insights. Our multi-objective formulation seeks to minimize the overall price to immunize a cohort of children, maximize the net profit shared among pediatric vaccine manufacturers, and minimize the average number of injections per child among the prescribed formularies. Accounting for Centers for Disease Control and Prevention (CDC) guidelines, we restrict vaccines utilized against a given disease within a given formulary to those produced by a single manufacturer. We also account for a circumstance in which one manufacturer's vaccine has a greater relative efficacy. For the resulting nonconvex mixed-integer nonlinear program, we bound the second and third objectives using optimal formulary designs for current public sector prices and utilize the ϵ -constraint method to solve an instance representative of contemporary immunization schedule requirements. Augmenting our formulation with symmetry reduction constraints to reduce the required computational effort, we identify a set of non-inferior solutions. Of practical interest to the CDC, our model enables the design of a pricing and purchasing policy, creating a sustainable and stable capital investment environment for the provision of pediatric vaccines.
{"title":"Informing pediatric vaccine procurement policy via the pediatric formulary design, pricing, and production problem","authors":"B. Lunday, M. Robbins","doi":"10.1080/0740817X.2016.1198064","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1198064","url":null,"abstract":"ABSTRACT This research improves upon the monopsonist vaccine formulary design problem in the literature by incorporating several modeling enhancements and applying different methodologies to efficiently obtain solutions and derive insights. Our multi-objective formulation seeks to minimize the overall price to immunize a cohort of children, maximize the net profit shared among pediatric vaccine manufacturers, and minimize the average number of injections per child among the prescribed formularies. Accounting for Centers for Disease Control and Prevention (CDC) guidelines, we restrict vaccines utilized against a given disease within a given formulary to those produced by a single manufacturer. We also account for a circumstance in which one manufacturer's vaccine has a greater relative efficacy. For the resulting nonconvex mixed-integer nonlinear program, we bound the second and third objectives using optimal formulary designs for current public sector prices and utilize the ϵ -constraint method to solve an instance representative of contemporary immunization schedule requirements. Augmenting our formulation with symmetry reduction constraints to reduce the required computational effort, we identify a set of non-inferior solutions. Of practical interest to the CDC, our model enables the design of a pricing and purchasing policy, creating a sustainable and stable capital investment environment for the provision of pediatric vaccines.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"1112 - 1126"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1198064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59755714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-17DOI: 10.1080/0740817X.2016.1200202
F. Kiermaier, M. Frey, J. Bard
ABSTRACT Companies in the service industry frequently depend on cyclic rosters to schedule their workforce. Such rosters offer a high degree of fairness and long-term predictability of days on and off, but they can hinder an organization’s ability to respond to changing demand. Motivated by the need for improving cyclic planning at an airport ground handling company, this article introduces the idea of flexible cyclic rostering as a means of accommodating limited weekly adjustments of employee schedules. The problem is first formulated as a multi-stage stochastic program; however, this turned out to be computationally intractable. To find solutions, two approximations were developed that involved reductions to a two-stage problem. In the computational study, the flexible and traditional cyclic rosters derived from these approximations are compared and metrics associated with the value of stochastic information are reported. In the testing, we considered seven different perturbations of the demand curve that incorporate the types of uncertainty that are common throughout the service industry. To the best of our knowledge, this is the first analysis of cyclic rostering that applies stochastic optimization. The results show that a reduction in undercoverage of more than 10% on average can be achieved with minimal computational effort. It was also observed that the new approach can overcome most of the limitations of traditional cyclic rostering while still providing most of its advantages.
{"title":"Flexible cyclic rostering in the service industry","authors":"F. Kiermaier, M. Frey, J. Bard","doi":"10.1080/0740817X.2016.1200202","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1200202","url":null,"abstract":"ABSTRACT Companies in the service industry frequently depend on cyclic rosters to schedule their workforce. Such rosters offer a high degree of fairness and long-term predictability of days on and off, but they can hinder an organization’s ability to respond to changing demand. Motivated by the need for improving cyclic planning at an airport ground handling company, this article introduces the idea of flexible cyclic rostering as a means of accommodating limited weekly adjustments of employee schedules. The problem is first formulated as a multi-stage stochastic program; however, this turned out to be computationally intractable. To find solutions, two approximations were developed that involved reductions to a two-stage problem. In the computational study, the flexible and traditional cyclic rosters derived from these approximations are compared and metrics associated with the value of stochastic information are reported. In the testing, we considered seven different perturbations of the demand curve that incorporate the types of uncertainty that are common throughout the service industry. To the best of our knowledge, this is the first analysis of cyclic rostering that applies stochastic optimization. The results show that a reduction in undercoverage of more than 10% on average can be achieved with minimal computational effort. It was also observed that the new approach can overcome most of the limitations of traditional cyclic rostering while still providing most of its advantages.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"1139 - 1155"},"PeriodicalIF":0.0,"publicationDate":"2016-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1200202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59755775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-16DOI: 10.1080/0740817X.2016.1190480
Yan Li, Yi Zhang, N. Kong, M. Lawley
ABSTRACT We study the problem of capacity planning for long-term care services, which is important not only for the elderly and disabled who cannot adequately care for themselves but also for long-term care providers and health policymakers. Patients with long-term care needs usually have to transfer between different settings such as nursing homes and home- and community-based services. We model patient flows among these settings using an open migration network and formulate the planning of the capacity needed to provide long-term care with a newsvendor-type model. We explore the structural properties of the model and identify the most influential factors, such as the penalty cost for capacity shortage and transition rates between different care settings, in making capacity decisions. With the model developed, capacity decisions for long-term care service networks can be made more systematically with full consideration of different patient flow patterns and budget constraints. The research will be especially useful to long-term care policymakers in a state or nationwide given the worsening shortage of care providers and the escalating long-term care needs resulting from population aging.
{"title":"Capacity planning for long-term care networks","authors":"Yan Li, Yi Zhang, N. Kong, M. Lawley","doi":"10.1080/0740817X.2016.1190480","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1190480","url":null,"abstract":"ABSTRACT We study the problem of capacity planning for long-term care services, which is important not only for the elderly and disabled who cannot adequately care for themselves but also for long-term care providers and health policymakers. Patients with long-term care needs usually have to transfer between different settings such as nursing homes and home- and community-based services. We model patient flows among these settings using an open migration network and formulate the planning of the capacity needed to provide long-term care with a newsvendor-type model. We explore the structural properties of the model and identify the most influential factors, such as the penalty cost for capacity shortage and transition rates between different care settings, in making capacity decisions. With the model developed, capacity decisions for long-term care service networks can be made more systematically with full consideration of different patient flow patterns and budget constraints. The research will be especially useful to long-term care policymakers in a state or nationwide given the worsening shortage of care providers and the escalating long-term care needs resulting from population aging.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"1098 - 1111"},"PeriodicalIF":0.0,"publicationDate":"2016-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1190480","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59755699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-07DOI: 10.1080/0740817X.2016.1190481
Yan-Hui Lin, Yanfu Li, E. Zio
ABSTRACT System failures can be induced either by internal degradation mechanisms or by external causes. In this article, we consider the reliability of systems experiencing both degradation and random shock processes. The dependencies between degradation processes and random shocks and those among the degradation processes are explicitly modeled. The degradation processes of system components are modeled using Multi-State Models (MSMs) and Physics-Based Models (PBMs). The piecewise-deterministic Markov process modeling framework is employed to combine MSMs and PBMs and to incorporate degradation and random shocks dependencies. The Monte Carlo simulation and finite-volume methods are used to compute the system reliability. A subsystem of a residual heat removal system in a nuclear power plant is considered as an illustrative case.
{"title":"Reliability assessment of systems subject to dependent degradation processes and random shocks","authors":"Yan-Hui Lin, Yanfu Li, E. Zio","doi":"10.1080/0740817X.2016.1190481","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1190481","url":null,"abstract":"ABSTRACT System failures can be induced either by internal degradation mechanisms or by external causes. In this article, we consider the reliability of systems experiencing both degradation and random shock processes. The dependencies between degradation processes and random shocks and those among the degradation processes are explicitly modeled. The degradation processes of system components are modeled using Multi-State Models (MSMs) and Physics-Based Models (PBMs). The piecewise-deterministic Markov process modeling framework is employed to combine MSMs and PBMs and to incorporate degradation and random shocks dependencies. The Monte Carlo simulation and finite-volume methods are used to compute the system reliability. A subsystem of a residual heat removal system in a nuclear power plant is considered as an illustrative case.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"1072 - 1085"},"PeriodicalIF":0.0,"publicationDate":"2016-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1190481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59755706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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