Dissertations / Theses on the topic 'Optimised schedule'

This research examines the drying of hardwood timber with particular reference to seasoning blackbutt (Eucalyptus pilularis) in a solar kiln. The aims of this research were to develop an optimised drying schedule for drying blackbutt and to develop and validate a mathematical model for a solar kiln. In the first stage of this study, the cross-grain physical and mechanical properties were determined for blackbutt timber so that an optimised schedule (based on drying within a limiting strain envelope) can be developed using model predictive control techniques for drying 43 mm thick (green) blackbutt timber boards in solar kilns. This optimised schedule has been developed and tested in the laboratory. The drying time was 10% shorter for this schedule than the original schedule, compared with an expected reduction in drying time of 14% (relative to the original schedule). Overall the quality was slightly better and the drying time was shorter for the optimised schedule compared with the original schedule. A complete solar kiln model has been developed and validated based on comparisons between the predicted and the measured internal air temperatures, relative humidities and timber moisture contents. The maximum difference between the actual and predicted moisture contents was 0.05 kg/kg. The agreement between the predicted and measured temperatures of the internal air is reasonable, and both the predictions and measurements have a similar cyclical pattern. The generally good agreement between the model prediction of the final moisture content and its measurement may be due to the careful measurement of the boundary conditions such as the solar energy input. The key uncertainties were identified as the heat exchanger output, the measurement of the initial moisture content, the amount of accumulation of condensate on the floor, and the estimation of sky temperature. The significant uncertainty (18%) in the estimation of the initial moisture content is a key reason for the mismatch between the model prediction and the measurements. In terms of operating variables, the energy release rate from the heat exchanger had the greatest effect on the simulated performance, followed by the water spray and venting rates. The simulation suggested that a material with a lower transmissivity to thermal radiation may effectively lower radiation losses, improving the kiln performance, so such materials for glazing is a high priority.

This research examines the drying of hardwood timber with particular reference to seasoning blackbutt (Eucalyptus pilularis) in a solar kiln. The aims of this research were to develop an optimised drying schedule for drying blackbutt and to develop and validate a mathematical model for a solar kiln. In the first stage of this study, the cross-grain physical and mechanical properties were determined for blackbutt timber so that an optimised schedule (based on drying within a limiting strain envelope) can be developed using model predictive control techniques for drying 43 mm thick (green) blackbutt timber boards in solar kilns. This optimised schedule has been developed and tested in the laboratory. The drying time was 10% shorter for this schedule than the original schedule, compared with an expected reduction in drying time of 14% (relative to the original schedule). Overall the quality was slightly better and the drying time was shorter for the optimised schedule compared with the original schedule. A complete solar kiln model has been developed and validated based on comparisons between the predicted and the measured internal air temperatures, relative humidities and timber moisture contents. The maximum difference between the actual and predicted moisture contents was 0.05 kg/kg. The agreement between the predicted and measured temperatures of the internal air is reasonable, and both the predictions and measurements have a similar cyclical pattern. The generally good agreement between the model prediction of the final moisture content and its measurement may be due to the careful measurement of the boundary conditions such as the solar energy input. The key uncertainties were identified as the heat exchanger output, the measurement of the initial moisture content, the amount of accumulation of condensate on the floor, and the estimation of sky temperature. The significant uncertainty (18%) in the estimation of the initial moisture content is a key reason for the mismatch between the model prediction and the measurements. In terms of operating variables, the energy release rate from the heat exchanger had the greatest effect on the simulated performance, followed by the water spray and venting rates. The simulation suggested that a material with a lower transmissivity to thermal radiation may effectively lower radiation losses, improving the kiln performance, so such materials for glazing is a high priority.

The motivation of this project is found on the past trend of growing greenhouse gases emissions and, also growing, energy use over the world that still remains. This trend overlaps with a more recent increase in the awareness regarding the effects of human activities towards the Earth ecosystems. Thus, the upgrade of the already-in-use systems is necessary to move towards greener and more modern technologies that permit continue with the economic growth while building more sustainable societies. Thereby, the research focuses on the improvement of the ventilation system of a warehouse building and a cooking school located in the same plot, in an industrial area in Gävle, Sweden. The current system conditions, even consisting in some cases in recirculating air handling units, doesn’t permit the utilisation of the waste heat by bringing it back to the system. The strategy used during the project follows a case study scheme: looking the system, understanding it in a complete way and designing the proper solution that fulfils the requirements. The study was approached as an energy audit: with several meetings with the company, collecting airflows data with the thermo-anemometer device, sketching the required building drawings and designing the optimal solution for the company. Finally, the project resulted in the selection of the proper air handling unit, equipped with a heat recovery system, and the design of its ventilation duct system that permit a heat energy savings derived of the heat demand used to heat the makeup air of about 67 %. Furthermore, the occupancy study helped design the new scheduling for the ventilation periods that reduce the electricity demand of the ventilation system by 30 %. Thus, was obtained a significant energy use reduction that results in a sizeable energy cost saving.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
Includes bibliographical references (p. 173-177).
by David Shoemaker.
Ph.D.

The purpose of this investigation is to see how economic inequality, stereotypes, and unemployment affect the well-being of people who identify as middle class in central West Virginia. Questions include whether living in a place with high poverty rates, regardless of income, negatively affects a person’s attitude and well-being, and if middle class people are victims of “guilt-by-association” for living in a lower income county. The results of this study may help organizations like the ARC include data on well-being and life satisfaction alongside their economic reports. Moreover, the public may begin to view West Virginia differently, fueling tourism and overall economic growth. Relevant scholarship for this project includes: The Road to Poverty (Billings, Blee), Stigma (Goffman), Glass House (Alexander), Those Who Work, Those Who Don’t (Sherman), Women, Power, and Dissent… (Anglin), Man’s Search for Meaning (Frankl), and The Human Quest for Meaning (Wong). I am conducting phenomenological interviews of twelve people in six distressed counties and also administering the Life Attitudes Scale to determine their level of well-being. I will then use an ethnomethodological approach to analyze the lived experiences of these West Virginians. Specifically, I wish to understand how they confront the forces of Othering and dehumanization imposed on them by both cultural outsiders and regional organizations. In doing so, this study may prove that the social reality and moral framework constructed by the people who live in this area is far closer to the truth than any kind of statistical analysis.

Cette thèse étudie le problème de planification de vol et de la maintenancedes avions militaires. D’abord, nous étudions la complexité de ce problème d’optimisation.Puis, nous proposons un modèle de programmation linéaire en nombres entiers (PLNE) pourle résoudre. Nous construisons un générateur d’instances et une heuristique pour générer dessolutions initiales. Ensuite, nous appliquons l’Apprentissage Automatique pour améliorer laperformance des modèles PLNE en utilisant des coupes valides générées à partir des conditionsinitiales et des coupes apprises à partir de la prédiction des caractéristiques de solutionsoptimales. Ces coupes sont appliquées à un nouveau modèle PLNE. Le résultat est une réductiondu temps de résolution avec peu de pertes d’optimalité et de faisabilité par rapport auxméthodes matheuristiques alternatives. Finalement, nous présentons une nouvelle matheuristiquepour résoudre efficacement des grandes instances. La méthode utilise une descente àvoisinage variable qui combine la programmation dynamique (DP) et l’horizon glissant. LaDP exploite une représentation en graphe de l’espace des solutions de chaque avion. Le résultatest des solutions rapides et presque optimales, et un passage à l’échelle efficace pourdes instances de très grande taille
This thesis studies the long term Military Flight and Maintenance Planningproblem. First, we evaluate the complexity of this optimisation problem. Then we propose aMixed Integer Programming (MIP) model to solve it. We develop an instance generator anda heuristic to generate initial solutions. Furthermore, we apply Machine Learning to improvethe performance of the MIP model by using valid cuts generated on the basis of initialconditions and learned cuts based on the prediction of characteristics of optimal solutions.These cuts are applied to a new MIP model. This results in reductions in the solutiontime with little losses in optimality and feasibility in comparison to alternative matheuristicmethods. Finally, we present a new matheuristic to efficiently solve large instances. Themethod employs a Variable Neighborhood Descent that combines Dynamic Programming(DP) and Rolling Horizon neighborhoods. The DP is applied to a graph representation of thesolution space for a single aircraft. This results in fast good quality solutions and an efficientscaling for very large instances

Improved utilization of scarce resources such as health care personnel is necessary to address well-known problem of long waiting times within the health care. Implementing mathematically modeled scheduling in the operating theatre has the potential to result in more efficient allocation of resources and financial gains. Despite the promising results, the adoption rate of such models is low. This thesis examines the impact of a mixed-integer linear programming model using an overlapping strategy. We perform a computational experiment where both sequential and parallel schedules are produced with real surgery data from an orthopedic department at a Swedish university hospital. The generated schedules are compared against each other in measurements of cost productivity. Statistical analysis shows that there is a statistical significant difference between the two schedules, favoring the optimized schedule. The results further suggest that three operating rooms and four surgery teams is the most optimal combination of the 18 combinations analyzed, where operating rooms and surgery teams varies between 1-4 and 1-6, respectively.

Analytics frameworks were initially created to run on bare-metal hardware so they contain scheduling mechanisms to optimise the distribution of the cpu load and data allocation. Generally, the scheduler is part of the analytics framework resource manager. There are different resources managers used in the market and the open-source community that can serve for different analytics frameworks. For example, Spark is initially built with Mesos. Hadoop is now using YARN. Spark is also available as a YARN application. On the other hand, cloud environments (Like OpenStack) contain theirs own mechanisms of distributing resources between users and services. While analytics applications are increasingly being migrated to the cloud, the scheduling decisions for running an analytic job is still done in isolation between the different scheduler layers (Cloud/Infrastructure vs analytics resource manager). This can seriously impact performance of analytics or other services running jointly in the same infrastructure as well as limit load-balancing, and autoscaling capabilities. This master thesis identifies what are the scheduling decisions that should be taken at the different layers (Infrastructure, Platform and Software) as well as the required metrics from the environment when mul-tiple schedulers are used in order to get the best performance and maximise the resource utilisation.

In previous studies it was determined that the fiber diameter distribution in a peripheral nerve could be estimated by a simulation technique known as group delay. These results could be further improved using a combinatorial optimization algorithm called simulated annealing. This paper explores the structure and behavior of simulated annealing for the application of optimizing the group delay estimated fiber diameter distribution. Specifically, a set of parameters known as the cooling schedule is investigated to determine its effectiveness in the optimization process. Simulated annealing is a technique for finding the global minimum (or maximum) of a cost function which may have many local minima. The set of parameters which comprise the cooling schedule dictate the rate at which simulated annealing reaches its final solution. Converging too quickly can result in sub-optimal solutions while taking too long to determine a solution can result in an unnecessarily large computational effort that would be impractical in a real-world setting. The goal of this study is to minimize the computational effort of simulated annealing without sacrificing its effectiveness at minimizing the cost function. The cost function for this application is an error value computed as the difference in the maximum compound evoked potentials between an empirically-determined template distribution of fiber diameters and an optimized set of fiber diameters. The resulting information will be useful when developing the group delay estimation and subsequent simulated annealing optimization in an experimental laboratory setting.

This thesis examined a range of potential reasons as to why people differ in how well the fear associated with a memory can be reduced. These reasons included the strength of the initial fear memory and individual differences such as the level of female hormones, strength of stress reaction, type of emotion regulation used, optimistic and neurotic personalities, and mood. The thesis revealed that stronger initial fear memories did not prevent the effective treatment of a fearful memory. It was also found that pessimism and hormonal contraception use prevent the effective reduction of the fear associated with memories.

碩士
中華技術學院
飛機系統工程研究所在職專班
97
The small scaled aviation industry in Taiwan, R.O.C, resulted in a limitation of funds and resources available for the airline operators. The aircraft maintenance cost however is almost account for 15%~20% of an airlines company cost or even more. Each general aviation company in Taiwan owns a small number of fleets and therefore has few aircraft maintenance persons relatively. Nevertheless, the lack of maintenance persons and shift patterns will cause a delay to a scheduled inspection and loss in invisible operation cost. The aircraft maintenance always plays a very important role in the aviation industry. A proper Maintenance Program is directly involved with the aircraft airworthiness. Fundamentally, a scheduled inspection has to be finished on time in a well-defined Maintenance program is operator’s carefully topic. To increase the airlines’ profits, operators have to reduce the maintenance cost and aircraft ground time, without infringing the laws/ regulations and manufacturer’s recommendation This research is based on the BK117 that was manufactured by Kawasaki Heavy Industry (KHI, Japan) and Messerschmitt-Bölkow-Blohm (MBB, Germany). To establish a mathematics formula of maintenance person’s shift under the heavy scheduled inspection work plan arranged by the maintenance controller to minimize the waste of total man hours spent and solved by the Genetic Algorithm. We matched with one of Taiwan airline’s real situation, and all of these calculated shifts can tally with limitations and no any manpower wasted. Apply this research to arrange shift during heavy scheduled inspection, not only reduce the time of arrange shift, but also to improve the work efficiency and make aircraft dock out on time. The maintenance shift can be arranged via person resources at the time before aircraft will be ground to perform scheduled inspection, and reduce the invisible operation cost.

碩士
國立臺灣海洋大學
電機工程學系
102
In many different projects of various industries, without computer science assistance, it’s very hard for human to calculate what would be the optimized schedule of a project that will allow the project expenditure at the minimum overall costs. Project managers often talk of a “triple constraint”- project scope, time and cost - in managing competing project requirements stated in PMBOK® [1] . Assuming Scope is fixed in any of the projects, the other two elements, time and cost, actually increase or decrease against each other. Which means, increasing or decreasing the project schedule might either decreasing or increasing the project cost or vice versa. As the leader of the project, a Project Manager with the planning team, how could they apply a simple algorithm in order to find the optimized estimation of the Project Schedule or Cost regarding the dynamic system? This becomes more demanded in modern world of project management. This thesis is mainly to apply the Genetic Algorithm solving the question above and to proof that there is an optimized project schedule by which we could find the minimum overall project cost. Note: the result of this provided solution regarding the optimized project schedule has not considered the risk portion while choosing it as the exact project durations; it leaves further studies of qualitative risk analysis or what-if scenario analysis of how much risk the project will take if the above optimized project schedule is chosen. A Telecommunication project of Radio Base Station Construction will be the reference case to build the model of the dynamic system. Around the model, MATLAB programs will be built to simulate the Genetic Algorithm for creating the time-cost chart, finding the minimum cost as well as the optimized schedule. The conclusion of this study will provide a methodology or solution to any practical projects trying to find the optimized schedule at minimum project cost with the set of input parameters like: the total amount of repeated tasks, the duration of time of each repeated tasks, the unit rate of direct resources, the percentage used to calculate the indirect cost from direct cost, and the interest rate of the opportunity cost, etc.

碩士
國立臺灣科技大學
營建工程系
107
Nowadays, the minimization of project time and cost is an important issue. However, time and cost problems are difficult to solve. They are affected by the uncertain factor. Then, the construction project always fails to achieve the effectiveness of time and cost performance. It causes delays and cost overrun. Over the past few years, the Earned Value Method (EVM) is used for forecasting project time and cost. However, its method does not consider uncertainties. In this research, SOS-NN-LSTM is required to establish the ESTC and ECTC prediction model based on time now performance. Then, the prediction model will be integrated with MOSOS to obtain the optimal prediction value. The integration is needed because there is no direct equation to calculate the ESTC and ECTC. The Pareto curve identified based on the prediction values of MOSOS. The Pareto curve is used to determine the optimal trade-off between project duration and project cost. Then, the indifference curve is used to solve the trade-off problem between ESTC and ECTC which give the decision-maker preference.

Efficient memory usage is crucial for data-intensive applications as a smaller memory footprint ensures better cache performance and allows one to run a larger problem size given a axed amount of main memory. The solutions found by existing techniques for automatic storage optimization for arrays in a new loop-nests, which minimize the storage requirements for the arrays, are often far from good or optimal and could even miss nearly all storage optimization potential. In this work, we present a new automatic storage optimization framework and techniques that can be used to achieve intra-array as well as inter-array storage reuse within a new loop-nests with a pre-determined schedule. Over the last two decades, several heuristics have been developed for achieving complex transformations of a new loop-nests using the polyhedral model. However, there are no comparably strong heuristics for tackling the problem of automatic memory footprint optimization. We tackle the problem of storage optimization for arrays by formulating it as one of ending the right storage partitioning hyperplanes: each storage partition corresponds to a single storage location. Statement-wise storage partitioning hyperplanes are determined that partition a unit end global array space so that values with overlapping live ranges are not mapped to the same partition. Our integrated heuristic for exploiting intra-array as well as inter-array reuse opportunities is driven by a fourfold objective function that not only minimizes the dimensionality and storage requirements of arrays required for each high-level statement, but also maximizes inter-statement storage reuse. We built an automatic polyhedral storage optimizer called SMO using our storage partitioning approach. Storage reduction factors and other results we report from SMO demon-strate the e activeness of our approach on several benchmarks drawn from the domains of image processing, stencil computations, high-performance computing, and the class of tiled codes in general. The reductions in storage requirement over previous approaches range from a constant factor to asymptotic in the loop blocking factor or array extents { the latter being a dramatic improvement for practical purposes. As an incidental and related topic, we also studied the problem of polyhedral compilation of graphical data programs. While polyhedral techniques for program transformation are now used in several proprietary and open source compilers, most of the research on poly-herald compilation has focused on imperative languages such as C, where the computation is species in terms of statements with zero or more nested loops and other control structures around them. Graphical data ow languages, where there is no notion of statements or a schedule specifying their relative execution order, have so far not been studied using a powerful transformation or optimization approach. The execution semantics and ref-eventual transparency of data ow languages impose a di errant set of challenges. In this work, we attempt to bridge this gap by presenting techniques that can be used to extract polyhedral representation from data ow programs and to synthesize them from their equivalent polyhedral representation. We then describe Polyglot, a framework for automatic transformation of data ow programs that we built using our techniques and other popular research tools such as Clan and Pluto. For the purpose of experimental evaluation, we used our tools to compile LabVIEW, one of the most widely used data ow programming languages. Results show that data ow programs transformed using our framework are able to outperform those compiled otherwise by up to a factor of seventeen, with a mean speed-up of 2.30 while running on an 8-core Intel system.