Optimal imperfect preventive maintenance policies for a used system

In some practical situations, it may be more economical to work a used system than do a new one. From this viewpoint, this article considers three basic preventive maintenance (PM) policies for a used system: the system with initial variable damage Y ₀ begins to operate at time 0, and suffers damage due to shocks. It fails when the total damage exceeds a failure level K and corrective maintenance is made immediately. To prevent such failure, it undergoes PM at a planned time T, a shock number N and a damage level k, but maintenances are imperfect. However, failure rate of a used system maybe higher than that of a new one, so some maintenance is applied to the policies at each shock in the extended models. Using the theory of cumulative processes, expected cost rate models are obtained, optimal policies which minimise them are derived analytically and discussed numerically.     

Integrated production and preventive maintenance scheduling for a single machine with failure uncertainty

This paper addresses the problem of finding a robust and stable schedule for a single machine with availability constraints. The machine suffers unexpected breakdowns and follows the Weibull failure function. A joint model for integrating run-based preventive maintenance (PM) into the production scheduling problem is proposed, in which the sequence of jobs, the PM times and the planned completion times of jobs are proactively determined simultaneously. Aiming at optimizing the bi-objective of system robustness and stability, a genetic algorithm based on the properties of the optimal schedule is proposed. The experimental results demonstrate that the proposed algorithm is efficient and effective under practical problem sizes. In addition, the impact of degree of uncertainty on the performance and the tradeoff between robustness and stability are explored in detail.     

An economic production quantity model for deteriorating items with preventive maintenance policy and random machine breakdown

In recent years, many researches on economic production quantity (EPQ) models with machine breakdown and preventive maintenance have been developed, but few of them have developed integrated models for deteriorating items. In this study, we develop EPQ models for deteriorating items with preventive maintenance, random machine breakdown and immediate corrective action. Corrective and preventive maintenance times are assumed to be stochastic and the unfulfilled demands are lost sales. Two EPQ models of uniform distribution and exponential distribution of corrective and maintenance times are developed. An example and sensitivity analysis is given to illustrate the models. For the exponential distribution model, it is shown that the corrective time parameter is one of the most sensitive parameters to the optimal total cost.     

A preventive maintenance policy for a standby system subject to internal failures and external shocks with loss of units

In many situations, serious damage and considerable financial losses are caused by non-repairable failures of a system. Redundant systems and maintenance policies are commonly employed to improve reliability. This paper is focused on the modelling of a complex cold standby system by analysing the effectiveness and costs of preventive maintenance, always in an algorithmic form. The online unit of the system is subject to wear failures and external shocks. The online unit can go through an indeterminate number of degradation levels before failure. This one is observed when inspections occur. Inspections are performed at random intervals, and when one takes place, the unit is taken to the preventive maintenance facility if it is necessary. The preventive maintenance time and cost is different depending on the degradation level observed. If only one unit is performing, a minimal maintenance policy is adopted in order to optimise system behaviour. Reliability measures such as the conditional probability of failure are worked out in a well-structured and algebraic form in transient and stationary regimes by using algorithmic methods. The stationary distribution is calculated using matrix analytic methods, and rewards are included in the model. An optimisation example shows the versatility of the model presented.     

Intelligent decision-making model in preventive maintenance of asphalt pavement based on PSO-GRU neural network

The milage of asphalt pavement growth explosively around the world in the past decades resulted in a tremendous maintenance workload. Preventive maintenance (PM) is an effective strategy in saving budget, keeping the pavement in good condition, and extending pavement life. A particle swarm optimization (PSO) algorithm enhanced gated recurrent unit (GRU) neural network is developed in this research to predict five pavement performance parameters. The model is trained based on a dataset containing seven-year distress measurement data in 100-m intervals, traffic load data, climatic records, and maintenance records of a chosen highway in China. The random forest (RF) algorithm is used to analyze the influence of the factors on pavement performances for different lanes. The result shows the PSO-GRU model could increase the prediction accuracy by 21% on average compared with traditional ANN and 17% on average compared with the AdaBoost model. The validation case study shows a significant consistency between the predicted pavement quality index and the whole-year measurement data with a 0.67 coefficient of determination. This study demonstrates the potential of using the PSO-GRU neural network to provide the most effective treatment at a given location on a highway.     

A pattern recognition and data analysis method for maintenance management

This article presents a pattern recognition method based on grouping by linear relationship a set of faults. The majority of faults can be detected, but only a few experiments can be identified. The algorithm called Principal Component Analysis (PCA) is employed together with the statistical parameters of the signals for detecting and identifying the faults. PCA technique is utilised for modifying dataset reducing the coordinate system, which must be correlated, by linear transformation, into a smaller set of uncorrelated variables called ‘principal components’. The signals analysed were the current and force signals in normal-to-reverse and reverse-to-normal directions of the system.     

Developer social networks in software engineering:construction,analysis,and applications

With the increasing popularity of Internet, more and more developers are collaborating together for software development. During the collaboration, a lot of information related to software development, including communication and coordination information of developers, can be recorded in software repositories. The information can be employed to construct Developer Social Networks （DSNs） for facilitating tasks in software engineering. In this paper, we survey recent advances of DSNs and examine three fundamental steps of DSNs, namely construction, analysis, and applications. We summarize the state-of-the-art methods in the three steps and investigate the relationships among them. Furthermore, we discuss the main issues and point out the future opportunities in the study of DSNs.     

A generalised maintenance policy with age-dependent minimal repair cost for a system subject to shocks under periodic overhaul

A system is subject to shocks that arrive according to a non-homogeneous Poisson process. As shocks occur, the system has two types of failures: type 1 failure (minor failure) is removed by a minimal repair, whereas type 2 failure (catastrophic failure) is removed by overhaul or replacement. The cost of minimal repair depends on age. A system is overhauled when the occurrence of a type 2 failure or at age T, whichever occurs first. At the N-th overhaul, the system is replaced rather than overhauled. A maintenance policy for determining optimal number of overhauls and optimal interval between overhauls which incorporate minimal repairs, overhauls and replacement is proposed. Under such a policy, an approach which using the concept of virtual age is adopted. It is shown that there exists a unique optimal policy which minimises the expected cost rate under certain conditions. Various cases are considered.     

基于构件软件的可靠性分析技术—FTA

快速、高效是软件开发追逐的目标,集中体现软件复用思想的基于构件软件技术继面向对象技术后成为现今软件工程实践和研究的热点;软件所扮演的角色日趋关键,高效开发的同时更需要高可靠性的软件。论文所关注的就是针对开发设计阶段的基于构件软件,采用FTA技术,考虑等概率条件下的软件运行剖面,进行面向软件运行的可靠性分析,试图为软件的设计和测试提供更具有实际意义的反馈和指导,进而增强软件的可靠性。文章还介绍了基于这样的技术针对小型的基于构件软件进行的实证分析。     

基于模型的构件软件修改影响分析

基于构件的软件构建方法目前被广泛使用在软件开发中,用于减少软件开发的工程成本和加快软件开发进度.面向构件的系统主要由第三方提供的可重用构件或者内建的可重用构件组成,因此,系统的质量好坏和维护的难易程度依赖于构件的品质.一个软件修改会给其他构件甚至整个系统带来影响,而修改影响分析是控制和消除这类影响的有效手段.然而,现有的研究很少涉及构件软件的修改影响分析,尤其缺少对系统层面的修改影响分析研究.提出了一种基于模型的系统化修改影响分析方法,该方法的基本思路是:首先提出构件及系统层面的修改影响分析模型,然后根据分析模型分别从构件和系统两个层面对构件软件修改前后的版本进行修改识别,并且利用“防火墙”方法进行影响分析.理论分析和实验结果表明,该方法是可行的,也是有效的.     

Multi-faceted quality and defect measurement for web software and source contents

In this paper, we examine external failures and internal faults traceable to web software and source contents. We develop related defect and quality measurements based on different perspectives of customers, users, information or service hosts, maintainers, developers, integrators, and managers. These measurements can help web information and service providers with their quality assessment and improvement activities to meet the quality expectations of their customers and users. The different usages of our measurement framework by different stakeholders of web sites and web applications are also outlined and discussed. The data sources include existing web server logs and statistics reports, defect repositories from web application development and maintenance activities, and source files. We applied our approach to four diverse websites: one educational website, one open source software project website, one online catalog showroom for a small company, and one e-Commerce website for a large company. The results demonstrated the viability and effectiveness of our approach.     

MSR4SM: Using topic models to effectively mining software repositories for software maintenance tasks

ContextMining software repositories has emerged as a research direction over the past decade, achieving substantial success in both research and practice to support various software maintenance tasks. Software repositories include bug repository, communication archives, source control repository, etc. When using these repositories to support software maintenance, inclusion of irrelevant information in each repository can lead to decreased effectiveness or even wrong results.ObjectiveThis article aims at selecting the relevant information from each of the repositories to improve effectiveness of software maintenance tasks.MethodFor a maintenance task at hand, maintainers need to implement the maintenance request on the current system. In this article, we propose an approach, MSR4SM, to extract the relevant information from each software repository based on the maintenance request and the current system. That is, if the information in a software repository is relevant to either the maintenance request or the current system, this information should be included to perform the current maintenance task. MSR4SM uses the topic model to extract the topics from these software repositories. Then, relevant information in each software repository is extracted based on the topics.ResultsMSR4SM is evaluated for two software maintenance tasks, feature location and change impact analysis, which are based on four subject systems, namely jEdit, ArgoUML, Rhino and KOffice. The empirical results show that the effectiveness of traditional software repositories based maintenance tasks can be greatly improved by MSR4SM.ConclusionsThere is a lot of irrelevant information in software repositories. Before we use them to implement a maintenance task at hand, we need to preprocess them. Then, the effectiveness of the software maintenance tasks can be improved.     

SemPI: A semantic framework for the collaborative construction and maintenance of a shared dictionary of performance indicators

Collaboration at strategic level entails the sharing of Performance Indicators (PIs) to measure the achievement of common objectives and evaluate performances. PIs are synthetic measures calculated starting from transactional data. Given their compound nature, it is difficult to achieve an agreement on their definitions and heterogeneities arise that make sharing and exchange a difficult task. Semantic techniques can help to address these challenges by providing a common layer of formal definitions and automatic reasoning tools to maintain its consistency. In this paper, we present a novel semantic framework for representing Performance Indicators that supports the construction and maintenance of a minimal and consistent dictionary. The distinctive feature of the approach is the logical representation of formulas defining PIs, allowing to make algebraic relationships among indicators explicit, and to reason over these relationships to derive PI identity and equivalence and to enforce the overall consistency of the dictionary. We also present a web application implementing the framework for collaborative construction and maintenance of the dictionary. We provide experimental evidence of the efficiency and effectiveness of the approach on synthetic and real data.     

Ergonomics risk assessment among maintenance operators in a Tunisian railway company: A case study

Railway truck maintenance plays a crucial role for rail transport. It aims at ensuring the availability of the equipment and the safety of the operations. However, maintenance activities often involve uncommon work and nonroutine tasks and it is obvious that maintenance operators are more threatened than other employees to be exposed to various hazards in their workplace. In Tunisia, the prevalence of occupational accidents and injuries among maintenance workers is not well documented. The target of this cross‐sectional study is to assess ergonomics risks associated with maintenance activities in a railway company. Data collection was carried out through a self‐administrated questionnaire and via direct observations. The Body Parts Symptom Survey (BPSS) was used to identify the discomfort level of the workers. More than 100 maintenance operators working in nine different workshops were involved in this study. From BPSS results, this paper proposed a new method to calculate the discomfort index related to each body part in order to calculate the global discomfort index (GDI) for each workshop. Results showed that the most hazardous workshop was rail‐car maintenance workshop (GDI = 43.06). Four main tasks were assessed with normalized score. Final scores were found to be extremely high. To remedy the raised problems, new adjustable workstations were proposed, which showed a considerable improvement on workers’ postures.     

Relative importance of evaluation criteria for enterprise systems: a conjoint study

Abstract.  While a large body of research exists on the development and implementation of software, organizations are increasingly acquiring enterprise software packages [e.g. enterprise resource planning (ERP) systems] instead of custom developing their own software applications. To be competitive in the marketplace, software package development firms must manage the three-pronged trade-off between cost, quality and functionality. Surprisingly, prior research has made little attempt to investigate the characteristics of packaged software that influence management information system (MIS) managers' likelihood of recommending purchase. As a result, both the criteria by which MIS managers evaluate prospective packaged systems and the attributes that lead to commercially competitive ERP software products are poorly understood. This paper examines this understudied issue through a conjoint study. We focus on ERP systems, which are among the largest and most complex packaged systems that are purchased by organizations. In a conjoint study, 1008 evaluation decisions based on hypothetical ERP software package profiles were completed by managers in 126 organizations. The study represents the first empirical investigation of the relative importance that managers ascribe to various factors that are believed to be important in evaluating packaged software. The results provide important insights for both organizations that acquire such systems and those that develop them. The results show that functionality, reliability, cost, ease of use and ease of customization are judged to be important criteria, while ease of implementation and vendor reputation were not found to be significant. Functionality and reliability were found to be the most heavily weighted factors. We conclude the paper with a detailed discussion of the results and their implications for software acquisition and development practice.     

IBFET: Index-based features extraction technique for scalable code clone detection at file level granularity

Many techniques have been developed over the years to detect code clones in different software systems to maintain security measures. These techniques often require the source code to compare the subject system against a very large data set of big code. This paper presents index-based features extraction technique (IBFET) to detect code clones at a very large-scale level to billions of LOC at file level granularity. We performed preprocessing, indexing, and clone detection for more than 324 billion of LOC using a Hadoop distributed environment, which is quite faster and more efficient as compared to existing distributed indexing and clone detection techniques; meanwhile, it detects all three types of clones efficiently. The MapReduce rule of divide and conquer is used for a count and retrieve the similar features between different systems. We evaluated the execution time, scalability, precision, and recall of IBFET by using a well-known clone detection data set IJaDataset and BigCloneBench; furthermore, we compared the results with other state-of-the-art tools. Our approach is faster, flexible, scalable, and provides accurate results with high authenticity and can be implemented at a large-scale level.     

Using the analytic network process (ANP) in a SWOT analysis - A case study for a textile firm

Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis does not provide an analytical means to determine the importance of the identified factors or the ability to assess decision alternatives according to these factors. Although the analysis successfully pinpoints the factors, individual factors are usually described briefly and very generally. For this reason, SWOT analysis possesses deficiencies in the measurement and evaluation steps. Although the analytic hierarchy process (AHP) technique removes these deficiencies, it does not allow for measurement of the possible dependencies among the factors. The AHP method assumes that the factors presented in the hierarchical structure are independent; however, this assumption may be inappropriate in light of certain internal and external environmental effects. Therefore, it is necessary to employ a form of SWOT analysis that measures and takes into account the possible dependency among the factors. This paper demonstrates a process for quantitative SWOT analysis that can be performed even when there is dependence among strategic factors. The proposed algorithm uses the analytic network process (ANP), which allows measurement of the dependency among the strategic factors, as well as AHP, which is based on the independence between the factors. Dependency among the SWOT factors is observed to effect the strategic and sub-factor weights, as well as to change the strategy priorities.     

Prominent feature extraction for review analysis: an empirical study

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.     

The analytic network process for managing inter-enterprise collaboration: A case study in a collaborative enterprise network

In the last years, collaboration among enterprises has gained attention in the business environment as a means to remain competitive. Enterprises that are collaborating look for improving their performance but, in real assessments, they often do not establish efficient frameworks to structure and manage the enterprise association/inter-enterprise performance. In addition, there are many factors that act as barriers to effective collaboration and have to be also properly managed as they impact on the inter-enterprise performance. This paper provides a methodology based on the analytic network process (ANP) to identify and measure, under an integrated approach, both factors and inter-enterprise performance considering their reciprocal impact. With this innovative approach, enterprises will obtain significant information for the decision-making process regarding which are the factors and inter-enterprise performance elements that generate a higher impact and therefore have a high priority within the specific collaborative relationship. Thus, enterprises can focus their efforts on improving those most important factors and performance elements, and consequently, enhancing their competitiveness.