Maintenance Strategies Design and Assessment Using a Periodic Complexity Approach

People become more dependent on various devices, which do deteriorate over time and their operation becomes more complex. This leads to higher unexpected failure chance, which causes inconvenience, cost, time, and even lives. Therefore, an efficient maintenance strategy that reduces complexity should be established to ensure the system performs economically as designed without interruption. In the current research, a comprehensive novel approach is developed for designing and evaluating maintenance strategies that effectively reduce complexity in a cost efficient way with maximum availability and quality. A proper maintenance strategy application needs a rigorous failure definition. A new complexity based mathematical definition of failure is introduced that is able to model all failure types. A complexity-based metric, complication rate , is introduced to measure functionality degradation and gradual failure. Maintenance reduces the system complexity by system resetting via introducing periodicity. A metric for measuring the amount of periodicity introduced by maintenance strategy is developed. Developing efficient maintenance strategies that improve system performance criteria, requires developing the mathematical relationships between maintenance and quality, availability, and cost. The first relation relating the product quality to maintenance policy is developed using the virtual age concept. The aging intensity function is then deployed to develop the relation between maintenance and availability. The relation between maintenance and cost is formulated by investigating the maintenance effect on each cost element. The final step in maintenance policy design is finding the optimum periodicity level. Two approaches are investigated; weighted sum integrated with AHP and a comfort zones approach. Comfort zones is a new developed physical programming based optimization heuristic that captures designer preferences and limitations without substantial efforts in tweaking or calculating weights. A mining truck case study is presented to explain the application of the developed maintenance design approach and compare its results to the traditional reward renewal theory. It is shown that the developed approach is more capable of designing a maintenance policy that reduces complexity and simultaneously improves some other performance measures. This research explains that considering complexity reduction in maintenance policy design improves system functionality, and it can be achieved by simple industrially applicable approach

Optimizing maintenance plans of offshore wind farms by calculating the likelihood of future turbine failures

Although offshore wind power shows promising energy potentials, high cost of operating and maintaining offshore wind farms concerns investors. Different maintenance strategies are applied by wind farm operators to overcome this drawback. A mixed integer optimization model is developed to find the optimal maintenance plan for an offshore wind farm. The proposed model include probabilistic failure times, multiple components per wind turbine, route decisions and imperfect maintenance. That is, aspects usually studied individually in the literature. Maintenance actions are scheduled based on the calculated likelihood of future turbine failures. Results from numerical experiments show that applying an imperfect preventive maintenance strategy, as opposed to a preventive replacement strategy, is preferable in most scenarios. An additional heuristic algorithm is presented. Close to optimal solutions with optimality gaps between 1% and 3% prove that the heuristic algorithm yields good solutions.Masteroppgave i energiENERGI399MAMN-ENER

Time Management Experiences Among Adult Learners in an Online Undergraduate Degree Program

The purpose of this case study was to discover the time management experiences of adult learners in an online undergraduate degree program at a mid-sized, Midwestern private university. The theory guiding this study was Tourangbam’s (2011) time equity theory as it emphasized the connection between time management and both productivity and life satisfaction. Data was collected through online (Zoom) interviews, a virtual (Zoom) focus group, and photovoice (Wang & Burris, 1992) exercise in which participants took photographs that represented their priorities, obligations, and time-wasters, which were the three categories of time management identified in time equity theory (Tourangbam, 2011). Data was analyzed qualitatively, allowing emergent categories to form from the raw data. Data from the three types of sources were synthesized, and multi-layered member-checking was used to increase the dependability of the study. The results of this study demonstrated common experiences, behaviors, and understandings of priorities, obligations, and time-wasters among the participants. The participants consistently used time-management strategies identified in existing literature (Macan et al., 1990), as well as self-regulation behaviors (Bandura, 1991). However, participants did not make the distinction between priorities and obligations described by Tourangbam (2011) and used these terms interchangeably. All participants identified social media as a time-waster and admitted to struggling to minimize time-wasters. Participants described their places of study as typically chaotic, high-traffic areas with frequent interruptions, but also identified supportive family as a primary factor in their academic success

Subheap-Augmented Garbage Collection

Automated memory management avoids the tedium and danger of manual techniques. However, as no programmer input is required, no widely available interface exists to permit principled control over sometimes unacceptable performance costs. This dissertation explores the idea that performance-oriented languages should give programmers greater control over where and when the garbage collector (GC) expends effort. We describe an interface and implementation to expose heap partitioning and collection decisions without compromising type safety. We show that our interface allows the programmer to encode a form of reference counting using Hayes\u27 notion of key objects. Preliminary experimental data suggests that our proposed mechanism can avoid high overheads suffered by tracing collectors in some scenarios, especially with tight heaps. However, for other applications, the costs of applying subheaps---in human effort and runtime overheads---remain daunting

Improving railroad freight car reliability using a new opportunistic maintenance heuristic and other information system improvements

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1991.Includes bibliographical references (leaves 283-289).by Patrick Little.Sc.D

Integration of maintenance optimization in process design and operation under uncertainty.

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Supporting Mobile Distributed Services

With sensors becoming increasingly ubiquitous, there is a tremendous potential for services which can take advantage of the data collected by these sensors, from the important -- such as detecting medical emergencies and imminent natural disasters -- to the mundane -- such as waiting times experienced by diners at restaurants. This information can then be used to offer useful services. For example, a busy professional could find a restaurant to go to for a quick lunch based on information available from smartphones of people already there having lunch, waiting to be seated, or even heading there; a government could conduct a census in real-time, or “sense” public opinion. I refer to such services as mobile distributed services. The barriers to offering mobile distributed services continue to be prohibitive for most: not only must these services be implemented, but they would also inevitably compete for resources on people's devices. This is in part because such services are poorly understood, and consequently, there is limited language support for programming them. In this thesis, I address practical challenges related to three important problems in mobile distributed services. In addition, I present my efforts towards a formal model for representing mobile distributed services. First, I address the challenge of enhancing the programmability of mobile distributed services. This thesis presents a set of core mechanisms underlying mobile distributed services. I interpret and implement these mechanisms for the domain of crowd-sourced services. A distributed runtime middleware, CSSWare, has been developed to simplify the burden of initiating and managing crowd-sourced services. CSSWare provides a set of domain-specific programming constructs for launching a new service. Service designers may launch novel services over CSSWare by simply plugging in small pieces of service specific code. Particularly, new services can be prototyped in fewer than 100 lines of code. This ease of programming promises to democratize the building of such services. Second, I address the challenge of efficiently supporting the sensing needs of mobile distributed services, and more generally sensor-based applications. I developed ShareSens, an approach to opportunistically merge sensing requirements of independent applications. When multiple applications make sensing requests, instead of serving each request independently, ShareSens opportunistically merges the requests, achieving significant power and energy savings. Custom filters are then used to extract the data required by each application. Third, I address the problem of programming the sensing requirements of mobile distributed services. In particular, ModeSens is presented to allow multi-modal sensing requirements of a service to be programmed separately from its function. Programmers can specify the modes in which a service can be, the sensing needs of each mode, and the sensed events which trigger mode transition. ModeSens then monitors for mode transition events, and dynamically adjusts the sensing frequencies to match the current mode's requirements. Separating the mode change logic from an application's functional logic leads to more modular code. In addition, I present MobDisS (Mobile Distributed Services), an early model for representing mobile distributed services, allowing them to be carefully studied. Services can be built by composing simpler services. I present the syntax and operational semantics of MobDisS. Although this work can be evaluated along multiple dimensions, my primary goal is to enhance programmability of mobile distributed services. This is illustrated by providing the actual code required for creating two realistic services using CSSWare. Each service demonstrates different facets of the middleware, ranging from the use of different sensors to the use of different facilities provided by CSSWare. Furthermore, experimental results are presented to demonstrate scalability, performance and data-contributor side energy efficiency of CSSWare and ShareSens. Finally, a set of experimental evaluation is carried out to measure the performance and energy costs of using ModeSens

Profits with Purpose: An Economic Justification of Conscious Capitalism

For years, mainstream economic theory has assumed that the only legitimate purpose of business is to maximize profits. In Conscious Capitalism: Liberating the Heroic Spirit of Business, authors John Mackey and Raj Sisodia reject this assumption, arguing that many firms have a genuinely motivated higher purpose. In addition to having a purpose beyond profit maximization, the conscious business model proposed by the authors calls for maximizing value for all stakeholders (employees, customers, suppliers, society, the environment, and investors), instead of for investors exclusively. However, the authors cite a number of examples of practices of conscious businesses that are justified in the economics literature for the exclusive goal of profit maximization, and indexes of firms with qualities similar to those of conscious firms have been shown to outperform the broader market, sometimes significantly. Despite the business model’s rejection of profit maximization as the sole function of businesses, do the goals of a conscious business suggest a strategy that paradoxically leads to profit maximization? This paper explores the potential for the conscious capitalism business model to be justified from a profit maximization standpoint through a broad exploration of the economics literature on various common practices of conscious businesses. Additionally, a case study examines two discount retailers: Walmart has a reputation for having troubled relationships with its stakeholders, while Costco is frequently applauded for its generosity to its workers and the loyalty it engenders in its customers. Based on theoretical and empirical evidence from a broad range of fields including labor economics, management economics, sociology, and economic psychology, and on Costco’s advantages in areas including employee turnover, customer loyalty, reputation, and community relations, it is reasonable to conclude that adherence to a conscious business model is a mechanism for profit maximization

Preventive maintenance and replacement scheduling : models and algorithms.

Preventive maintenance is a broad term that encompasses a set of activities aimed at improving the overall reliability and availability of a system. Preventive maintenance involves a basic trade-off between the costs of conducting maintenance/replacement activities and the cost savings achieved by reducing the overall rate of occurrence of system failures. Designers of preventive maintenance schedules must weigh these individual costs in an attempt to minimize the overall cost of system operation. They may also be interested in maximizing the system reliability, subject to some sort of budget constraint. In this dissertation, we present a complete discussion about the problem definition and review the literature. We develop new nonlinear mixed-integer optimization models, solve them by standard nonlinear optimization algorithms, and analyze their computational results. In addition, we extend the optimization models by considering engineering economy features and reformulate them as a multi-objective optimization model. We optimize this model by generational and steady state genetic algorithms as well as by a simulated annealing algorithm and demonstrate the computational results obtained by implementation of these algorithms. We perform a sensitivity analysis on the parameters of the optimization models and present a comparison between exact and metaheuristic algorithms in terms of computational efficiency and accuracy. Finally, we present a new mathematical function to model age reduction and improvement factor parameter used in optimization models. In addition, we develop a practical procedure to estimate the effect of maintenance activity on failure rate and effective age of multi component systems