Academic literature on the topic 'Optimised schedule'
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Journal articles on the topic "Optimised schedule"
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Raheja, Supriya. "An Intuitionistic Fuzzy Based Novel Approach to CPU Scheduler." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 4 (May 7, 2020): 316–28. http://dx.doi.org/10.2174/1573405614666180903120708.
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Background: The extension of CPU schedulers with fuzzy has been ascertained better because of its unique capability of handling imprecise information. Though, other generalized forms of fuzzy can be used which can further extend the performance of the scheduler. Objectives: This paper introduces a novel approach to design an intuitionistic fuzzy inference system for CPU scheduler. Methods: The proposed inference system is implemented with a priority scheduler. The proposed scheduler has the ability to dynamically handle the impreciseness of both priority and estimated execution time. It also makes the system adaptive based on the continuous feedback. The proposed scheduler is also capable enough to schedule the tasks according to dynamically generated priority. To demonstrate the performance of proposed scheduler, a simulation environment has been implemented and the performance of proposed scheduler is compared with the other three baseline schedulers (conventional priority scheduler, fuzzy based priority scheduler and vague based priority scheduler). Results: Proposed scheduler is also compared with the shortest job first CPU scheduler as it is known to be an optimized solution for the schedulers. Conclusion: Simulation results prove the effectiveness and efficiency of intuitionistic fuzzy based priority scheduler. Moreover, it provides optimised results as its results are comparable to the results of shortest job first.
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Saydam, Berkay, Cem Orhan, Niyazi Toker, and Mansur Turasan. "Optimisation of scheduled tasks by real-time measurement and correlation." New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, no. 12 (April 30, 2020): 36–43. http://dx.doi.org/10.18844/gjpaas.v0i12.4984.
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For functional safety, the scheduler should perform all time critical tasks in an order and within predefined deadlines in embedded systems. Scheduling of time critical tasks is determined by estimating their worst-case execution times. To justify the model design of task scheduling, it is required to simulate and visualise the task execution and scheduling maps. This helps to figure out possible problems before deploying the schedule model to real hardware. The simulation tools which are used by companies in an industry perform scheduling simulation and visualisation of all time critical tasks to design and verify the model. All of them lack the capability of comparing simulation results versus real results to achieve the optimised scheduling design. This sometimes leads the overestimated worst-case execution times and increased system cost. The aim of our study is to decrease the system cost with optimisation of scheduled tasks via using the static analysing method.
Keywords: Schedule visualisation, scheduler optimisation, functional safety, real-time systems, scheduler.
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Mishra, Bhupesh Kumar, Keshav Dahal, and Zeeshan Pervez. "Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment." Applied Sciences 12, no. 16 (August 21, 2022): 8358. http://dx.doi.org/10.3390/app12168358.
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In smart cities, relief items distribution is a complex task due to the factors such as incomplete information, unpredictable exact demand, lack of resources, and causality levels, to name a few. With the development of Internet of Things (IoT) technologies, dynamic data update provides the scope of distribution schedule to adopt changes with updates. Therefore, the dynamic relief items distribution schedule becomes a need to generate humanitarian supply chain schedules as a smart city application. To address the disaster data updates in different time periods, a dynamic optimised model with a sliding time window is proposed that defines the distribution schedule of relief items from multiple supply points to different disaster regions. The proposed model not only considers the details of available resources dynamically but also introduces disaster region priority along with transportation routes information updates for each scheduling time slot. Such an integrated optimised model delivers an effective distribution schedule to start with and updates it for each time slot. A set of numerical case studies is formulated to evaluate the performance of the optimised scheduling. The dynamic updates on the relief item demands’ travel path, causality level and available resources parameters have been included as performance measures for optimising the distributing schedule. The models have been evaluated based on performance measures to reflect disaster scenarios. Evaluation of the proposed models in comparison to the other perspective static and dynamic relief items distribution models shows that adopting dynamic updates in the distribution model cover most of the major aspects of the relief items distribution task in a more realistic way for post-disaster relief management. The analysis has also shown that the proposed model has the adaptability to address the changing demand and resources availability along with disaster conditions. In addition, this model will also help the decision-makers to plan the post-disaster relief operations in more effective ways by covering the updates on disaster data in each time period.
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Kroll, Christian, and Sebastian Pokutta. "Just a perfect day? Developing a happiness optimised day schedule." Journal of Economic Psychology 34 (February 2013): 210–17. http://dx.doi.org/10.1016/j.joep.2012.09.015.
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Koohi, Sina Zangbari, Nor Asilah Wati Abdul Hamid, Mohamed Othman, and Gafurjan Ibragimov. "ROA-CONS: Raccoon Optimization for Job Scheduling." Symmetry 13, no. 12 (November 29, 2021): 2270. http://dx.doi.org/10.3390/sym13122270.
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High-performance computing comprises thousands of processing powers in order to deliver higher performance computation than a typical desktop computer or workstation in order to solve large problems in science, engineering, or business. The scheduling of these machines has an important impact on their performance. HPC’s job scheduling is intended to develop an operational strategy which utilises resources efficiently and avoids delays. An optimised schedule results in greater efficiency of the parallel machine. In addition, processes and network heterogeneity is another difficulty for the scheduling algorithm. Another problem for parallel job scheduling is user fairness. One of the issues in this field of study is providing a balanced schedule that enhances efficiency and user fairness. ROA-CONS is a new job scheduling method proposed in this paper. It describes a new scheduling approach, which is a combination of an updated conservative backfilling approach further optimised by the raccoon optimisation algorithm. This algorithm also proposes a technique of selection that combines job waiting and response time optimisation with user fairness. It contributes to the development of a symmetrical schedule that increases user satisfaction and performance. In comparison with other well-known job scheduling algorithms, the simulation assesses the effectiveness of the proposed method. The results demonstrate that the proposed strategy offers improved schedules that reduce the overall system’s job waiting and response times.
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Fauzi, Z. A. M., and M. A. Mansor. "Development of Dynamic Programming Algorithm for Maintenance Scheduling Problem: A Conceptual Paper." International Journal of Engineering Technology and Sciences 5, no. 1 (June 6, 2018): 34–43. http://dx.doi.org/10.15282/ijets.v5i1.2823.
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Maintenance is one of the important methods that can be used to ensure the machines and equipment can operate within the best condition. A good scheduling for maintenance will maintain the proper performance since all resources will be utilised effectively as the underused of the resource can be reduced. The schedule created ensures the particular task was done within the time provided. What, when, where and how the certain operation will be done will be stated to make sure the planned activity going smoothly without any delays. However, the uncertainty that happened during the maintenance needs to be taken into account to create the fully optimised schedule. This change will affect the schedule developed and caused delays to the scheduled task. Once the beginning task is delayed, the following work will also be postponed. Thus, a mathematical model for maintenance scheduling for repairable and inspection of the machines and equipment was proposed to deal with the uncertainty that happened. This dynamic model will create the new schedule for the maintenance scheduling and help the manager in the decision-making process on where to assign the maintenance team when the uncertainty happened.
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Manganaro, L., A. Attili, T. Bortfeld, and H. Paganetti. "Spatiotemporal optimisation of prostate intensity modulated proton therapy (IMPT) treatments." Physics in Medicine & Biology 67, no. 4 (February 10, 2022): 045005. http://dx.doi.org/10.1088/1361-6560/ac4fa2.
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Abstract Objective. In intensity modulated particle therapy (IMPT), the adoption of spatially and temporally heterogeneous dose distributions allows to decouple the fractionation scheme from the patient anatomy, so that an hypofractionated schedule can be selectively created inside the tumour, while simultaneously exploiting the fractionation effect in the healthy tissues. In this paper, the authors show the reproducibility of the method on a set of prostate patients, quantifying the dependencies of the achievable benefit with respect to conventional and hypofractionated schemes and the sensitivity of the method to setup errors and range uncertainty. Approach. On a cohort of 9 patients, non-uniform IMPT plans were optimised and compared to conventional and hypofractionated schedules. For each patient, the comparison of the three strategies has been based on the output of the cost function used to optimise the treatments. The analysis has been repeated considering different α/β ratios for the tumour, namely 1.5, 3 and 4.5 Gy. For a single patient, setup errors and beam range uncertainty have been analysed: the plans, for each optimisation strategy, have been iteratively forward planned 500 times with randomly varying the patient position in each fraction, and 200 times for systematic range shift. Main results. An average 10% benefit has been shown for the lowest α/β ratio considered for the tumour, where the non-uniform schedule generally converges to hypofractionation; the benefit decreases to 5%–7% for higher α/β ratios, for which the non-uniform schedule always showed better outcomes with respect to the other fractionation schedules. An increased sensitivity to uncertainty, especially for setup errors, has been shown, which can be associated to the spatial non-uniformity of the dose distributions peculiar of the spatiotemporal plans. Significance. This work represents the first investigation of spatiotemporal fractionation for prostate cancer and the beginning of further investigations before clinical implementation can be considered.
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Dijkstra, Sander, Maarten Otten, Gréanne Leeftink, Bas Kamphorst, Angelique Olde Meierink, Anouk Heinen, Rhodé Bijlsma, and Richard J. Boucherie. "Limited waiting areas in outpatient clinics: an intervention to incorporate the effect of bridging times in blueprint schedules." BMJ Open Quality 11, no. 2 (June 2022): e001703. http://dx.doi.org/10.1136/bmjoq-2021-001703.
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BackgroundDistancing measures enforced by the COVID-19 pandemic impose a restriction on the number of patients simultaneously present in hospital waiting areas.ObjectiveEvaluate waiting area occupancy of an intervention that designs clinic blueprint schedules, in which all appointments of the pre-COVID-19 case mix are scheduled either digitally or in person under COVID-19 distancing measures, whereby the number of in-person appointments is maximised.MethodsPreintervention analysis and prospective assessment of intervention outcomes were used to evaluate the outcomes on waiting area occupancy and number of in-person consultations (postintervention only) using descriptive statistics, for two settings in the Rheumatology Clinic of Sint Maartenskliniek (SMK) and Medical Oncology & Haematology Outpatient Clinic of University Medical Center Utrecht (UMCU). Retrospective data from October 2019 to February 2020 were used to evaluate the pre-COVID-19 blueprint schedules. An iterative optimisation and simulation approach was followed, based on integer linear programming and Monte Carlo simulation, which iteratively optimised and evaluated blueprint schedules until the 95% CI of the number of patients in the waiting area did not exceed available capacity.ResultsUnder pre-COVID-19 blueprint schedules, waiting areas would be overcrowded by up to 22 (SMK) and 11 (UMCU) patients, given the COVID-19 distancing measures. The postintervention blueprint scheduled all appointments without overcrowding the waiting areas, of which 88% and 87% were in person and 12% and 13% were digitally (SMK and UMCU, respectively).ConclusionsThe intervention was effective in two case studies with different waiting area characteristics and a varying number of interdependent patient trajectory stages. The intervention is generically applicable to a wide range of healthcare services that schedule a (series of) appointment(s) for their patients. Care providers can use the intervention to evaluate overcrowding of waiting area(s) and design optimal blueprint schedules to continue a maximum number of in-person appointments under pandemic distancing measures.
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Marques, María Remedios, Ana Garcia‐Robles, Iris Usach, Maximo Vento, José Luis Poveda, José Esteban Peris, and Victor Mangas‐Sanjuan. "Topiramate pharmacokinetics in neonates undergoing therapeutic hypothermia and proposal of an optimised dosing schedule." Acta Paediatrica 109, no. 2 (August 22, 2019): 300–308. http://dx.doi.org/10.1111/apa.14944.
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Gómez, Manuel, Lucía Rancel, Pedro P. Gómez, José I. Robla, and Sebastián F. Medina. "Simplification of Hot Rolling Schedule in Ti-Microalloyed Steels with Optimised Ti/N Ratio." ISIJ International 50, no. 6 (2010): 868–74. http://dx.doi.org/10.2355/isijinternational.50.868.
Full textDissertations / Theses on the topic "Optimised schedule"
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Haque, M. N. "Modelling of solar kilns and the development of an optimised schedule for drying hardwood timber /." Connect to full text, 2002. http://setis.library.usyd.edu.au/adt/public_html/adt-NU/public/adt-NU20041011.133438.
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Haque, M. Nawshadul. "Modelling of Solar Kilns and The Development of An Optimised Schedule for Drying Hardwood Timber." Thesis, The University of Sydney, 2002. http://hdl.handle.net/2123/813.
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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.
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Haque, M. Nawshadul. "Modelling of Solar Kilns and The Development of An Optimised Schedule for Drying Hardwood Timber." University of Sydney. Chemical Engineering, 2002. http://hdl.handle.net/2123/813.
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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.
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Iglesias, Estellés Javier. "Study of the ventilation system in a warehouse and a cooking school : Impact of the use of a heat exchanger system and a more optimised operating schedule." Thesis, Högskolan i Gävle, Energisystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-26907.
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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.
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Shoemaker, David R. (David Robert). "An optimized hardware architecture and communication protocol for scheduled communication." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/42660.
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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.
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Miller, Julian. "Tragic Optimism and Universal Values: Reframing the Narrative of Poverty in Central West Virginia." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/asrf/2019/schedule/174.
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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.
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Peschiera, Franco. "Exact and heuristic methods to optimize maintenances and flight schedules of military aircraft." Thesis, Toulouse, ISAE, 2020. http://www.theses.fr/2020ESAE0034.
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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 tailleThis 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
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Radulovic, Igor, and Timmie Abrahamsson. "The Impact Of Optimized Scheduling Within The Swedish Operating Theatre." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18265.
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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.
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Stanogias, Nikolaos. "Combining analytics framework and Cloud schedulers in order to optimise resource utilisation in a distributed Cloud." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177582.
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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.
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Vigeh, Arya. "Investigation of a Simulated Annealing Cooling Schedule used to Optimize the Estimation of the Fiber Diameter Distribution in a Peripheral Nerve Trunk." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/497.
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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.
Books on the topic "Optimised schedule"
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Brandly, Michael S. Schedules for balanced optimized cyclic arrangements of matched pairs of individual players numbered four through thirty-two with provisions. Columbus, Ohio: Beck & Orr, 1993.
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Soghier, Lamia, Katherine Pham, and Sara Rooney, eds. Reference Range Values for Pediatric Care. American Academy of Pediatrics, 2014. http://dx.doi.org/10.1542/9781581108545.
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Here’s the one place to look for normal values and related need-to-know data! Now you no longer have to search through multiple resources for reference ranges and other critical values you need to optimize patient assessment and management. The new Reference Range Values for Pediatric Care brings all the most vital range data - plus diverse clinical evaluation and calculation tools - all together in one concise, compact handbook. Indispensable pediatric reference ranges - right at your fingertips Custom-designed for today’s busy practitioners, this quick-access resource provides commonly used ranges and values spanning birth through adolescence. Data needed for management of preterm newborns and other neonates is highlighted throughout. Look here for practice-focused help with: - Blood pressure ranges - Body surface area calculation - Bone age metrics - Hematology values - Cerebrospinal fluid values - Lymphocyte subset counts - Clinical chemistry ranges - Thyroid function - Umbilical vein and artery catheterization measurements - Caloric intake values - And more! Also includes assessment and management tools you’ll use again and again Save time and simplify clinical problem-solving with a full set of easy-to-use tools from the AAP and other authoritative sources: - APGAR and Ballard newborn screening - Growth charts - Metric conversion tables - Pain scales - Blood pressure nomograms - Hyperbilirubinemia nomograms - Enternal formulas - GIR calculators - AAP immunization schedules - AAP periodicity schedule Drug administration and monitoring guidelines The handbook includes must-know basics on commonly used antibiotics and antiseizure medications - complete with recommended dosages and serum target levels.
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Geyer, Cynthia. Optimizing Integrative and Preventive Medicine. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190241254.003.0013.
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The practice of integrative and preventive medicine plays a critical role in improving the health and well-being of people, families, and communities. This chapter covers several key steps to optimize the effective practice of integrative and preventive medicine, including the importance the clinician–patient partnership; prioritizing pain, stress, and emotional distress; and clearly communicating the reasoning behind recommendations. The successful clinician should be able to engage with patients as partners through their health journey, make appropriate referrals to other members of the integrative medicine team, encourage self-efficacy and health behavior change, ensure patient understanding, and schedule periodic follow-up and reassessment.
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Leo Art Leo Art Designs. Menstrual Activity Tracker: Track Your Cycle along with Physical Activity to Optimize Your Workout Schedule. Independently Published, 2021.
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Philip, Pierre, Stephanie Bioulac, Patricia Sagaspe, and Jean-Arthur Micoulaud-Franchi. Drowsy driving. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198778240.003.0021.
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Drowsy driving increases risk of traffic accidents. A major problem remains in the identification of drowsy drivers at risk for traffic accidents. Drowsy driving is the consequence of various behavioural factors (e.g. sleep duration, work duration, shift-work schedules) combined or not with sleep and iatrogenic disorders (e.g. obstructive sleep apnoea syndrome, hypersomnia, drug-induced sleepiness). Severity of sleep disorders is a non-linear predictor of traffic accident risk. In comparison, sleepiness at the wheel (SAW) can be considered as a reliable indicator of a combination of behavioural and sleep disorder factors, and is a better risk predictor. It remains thus very important to question patients about SAW when clinicians have to determine the medical fitness to drive of such patients. Because of the potential risk of under-reporting of SAW, especially in professional drivers, objective measures can help to complement the clinical evaluation. Further researches are needed to optimize objective measures able to predict the risk of traffic accidents due to drowsy driving.
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Kshirsagar, Suhas, and Michelle D. Seaton. Change Your Schedule, Change Your Life: How to Harness the Power of Clock Genes to Lose Weight, Optimize Your Workout, and Finally Get a ... Dreamscape Media, 2018.
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Lockford, Lesa, Suhas Kshirsagar, and Michelle D. Seaton. Change Your Schedule, Change Your Life: How to Harness the Power of Clock Genes to Lose Weight, Optimize Your Workout, and Finally Get a ... Dreamscape Media, 2018.
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Ranucci, Marco, Serenella Castelvecchio, and Andrea Ballotta. Perioperative management of the high-risk surgical patient: cardiac surgery. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199687039.003.0077.
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During the last decade, as a result of continually improving surgical strategy and the technology which supports it (e.g. anaesthesia), cardiac surgery is offered to patients with advanced age and those with increasingly complex co-existing conditions that were previously considered to be contraindications. In addition, an increasing number of patients have previously undergone angioplasty, thereby delaying their initial coronary artery bypass graft surgery to a more advanced age. In general, candidates for cardiac surgery may now be not only older than in the past, but also more likely to have health problems such as hypertension and diabetes. Risk stratification may help to identify ‘the’ high-risk patient: ‘pre-warned is pre-armed’. In high-risk cardiac surgery patients, the surgical treatment options and perioperative care must be tailored to each patient, in order to optimize the benefits and minimize the risk of detrimental effects. The preoperative anticoagulation practice is an important aspect, balancing the risk between ischaemic and bleeding complications. New antiplatelet agents and oral anticoagulants have been recently delivered, and their role in patients scheduled for heart surgery is an additional important issue.
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Ranucci, Marco, Serenella Castelvecchio, and Andrea Ballotta. Perioperative management of the high-risk surgical patient: cardiac surgery. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199687039.003.0077_update_001.
Full textAbstract:
During the last decade, as a result of continually improving surgical strategy and the technology which supports it (e.g. anaesthesia), cardiac surgery is offered to patients with advanced age and those with increasingly complex co-existing conditions that were previously considered to be contraindications. In addition, an increasing number of patients have previously undergone angioplasty, thereby delaying their initial coronary artery bypass graft surgery to a more advanced age. In general, candidates for cardiac surgery may now be not only older than in the past, but also more likely to have health problems such as hypertension and diabetes. Risk stratification may help to identify ‘the’ high-risk patient: ‘pre-warned is pre-armed’. In high-risk cardiac surgery patients, the surgical treatment options and perioperative care must be tailored to each patient, in order to optimize the benefits and minimize the risk of detrimental effects. The preoperative anticoagulation practice is an important aspect, balancing the risk between ischaemic and bleeding complications. New antiplatelet agents and oral anticoagulants have been recently delivered, and their role in patients scheduled for heart surgery is an additional important issue.
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Ranucci, Marco, Serenella Castelvecchio, and Andrea Ballotta. Perioperative management of the high-risk surgical patient: cardiac surgery. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199687039.003.0077_update_002.
Full textAbstract:
During the last decade, as a result of continually improving surgical strategy and the technology which supports it (e.g. anaesthesia), cardiac surgery is offered to patients with advanced age and those with increasingly complex co-existing conditions that were previously considered to be contraindications. In addition, an increasing number of patients have previously undergone angioplasty, thereby delaying their initial coronary artery bypass graft surgery to a more advanced age. In general, candidates for cardiac surgery may now be not only older than in the past, but also more likely to have health problems such as hypertension and diabetes. Risk stratification may help to identify ‘the’ high-risk patient: ‘pre-warned is pre-armed’. In high-risk cardiac surgery patients, the surgical treatment options and perioperative care must be tailored to each patient, in order to optimize the benefits and minimize the risk of detrimental effects. The preoperative anticoagulation practice is an important aspect, balancing the risk between ischaemic and bleeding complications. New antiplatelet agents and oral anticoagulants have been recently delivered, and their role in patients scheduled for heart surgery is an additional important issue.
Book chapters on the topic "Optimised schedule"
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Wilczyński, Andrzej, and Joanna Kołodziej. "Blockchain-Based Task and Information Management in Computational Cloud Systems." In Cybersecurity of Digital Service Chains, 162–201. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_8.
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AbstractBlockchain can be successfully utilised in diverse areas, including the financial sector and the Information and Communication Technology environments, such as computational clouds (CC). While cloud computing optimises the use of resources, it does not (yet) provide an effective solution for the secure hosting scheduling and execution of large computing and data applications and prevention of external attacks.This chapter briefly reviews the recent blockchain-inspired task scheduling and information processing methods in computational clouds. We pay special attention to security, intrusion detection, and unauthorised manipulation of tasks and information in such systems. As an example, we present the implementation of a new blockchain-based scheduler in the computational cloud. We defined a new Proof of Schedule consensus algorithm, which works with the Stackelberg game, regulates checking and adding new blocks to the blockchain, and determines how to validate schedules stored in transactions. The proposed model assumes competition between different schedule providers. The winner of such a competition takes account of the client’s requirements faster and prepares an optimal schedule to meet them. The presented scheduler extends the possibilities of using different scheduling modules by the end-users. By delegating the preparation of the schedules, providers can get benefits only for that, without executing customer tasks.
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Zahariev, Nikola, Yasir Zaki, Xi Li, Carmelita Goerg, Thushara Weerawardane, and Andreas Timm-Giel. "Optimized Service Aware LTE MAC Scheduler with Comparison against Other Well Known Schedulers." In Wired/Wireless Internet Communication, 323–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30630-3_29.
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Mangla, Chaitanya, Sean B. Holden, and Lawrence C. Paulson. "Bayesian Ranking for Strategy Scheduling in Automated Theorem Provers." In Automated Reasoning, 559–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10769-6_33.
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AbstractA strategy schedule allocates time to proof strategies that are used in sequence in a theorem prover. We employ Bayesian statistics to propose alternative sequences for the strategy schedule in each proof attempt. Tested on the TPTP problem library, our method yields a time saving of more than 50%. By extending this method to optimize the fixed time allocations to each strategy, we obtain a notable increase in the number of theorems proved.
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van Amelsvoort, Marie, Christian Glomb, Richard Kuntschke, Michael Specht, Marco Wagler, Martin Winter, and Rolf Witzmann. "Schedule Coordination for Technically and Economically Optimized Virtual Power Plants." In NEIS Conference 2016, 153–59. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-15029-7_24.
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Sree Lakshmi, Adepu, N. Subhash Chandra, and M. BalRaju. "Optimized Capacity Scheduler for MapReduce Applications in Cloud Environments." In Data Management, Analytics and Innovation, 157–69. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1402-5_12.
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Yom-Tov, Elad, and Yariv Aridor. "A Self-optimized Job Scheduler for Heterogeneous Server Clusters." In Job Scheduling Strategies for Parallel Processing, 169–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78699-3_10.
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Rahil, H., B. Abou El Majd, and M. Bouchoum. "Optimized Air Routes Connections for Real Hub Schedule Using SMPSO Algorithm." In Operations Research/Computer Science Interfaces Series, 369–84. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58253-5_21.
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Ying, Li, Wang Jing-sheng, Wang Hong-rui, and Wei Li-xin. "Optimization of Tandem Cold Rolling Schedule Based on Collaborative Optimized PSO." In Communications in Computer and Information Science, 399–407. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34038-3_55.
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Niu, Huimin. "Application of Genetic Algorithm to Optimize Transit Schedule under Time-Dependent Demand." In Atlantis Computational Intelligence Systems, 71–88. Paris: Atlantis Press, 2012. http://dx.doi.org/10.2991/978-94-91216-80-0_5.
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Robaina, Lidia, Juhani Pirhonen, Elena Mente, Javier Sánchez, and Neill Goosen. "Fish Diets in Aquaponics." In Aquaponics Food Production Systems, 333–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15943-6_13.
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AbstractFish and feed waste provide most of the nutrients required by the plants in aquaponics if the optimum ratio between daily fish feed inputs and the plant growing area is sustained. Thus, the fish feed needs to fulfil both the fish’s and plant’s nutritional requirements in an aquaponic system. A controlled fish waste production strategy where the nitrogen, phosphorus and mineral contents of fish diets are manipulated and used provides a way of influencing the rates of accumulation of nutrients, thereby reducing the need for the additional supplementation of nutrients. To optimize the performance and cost-effectiveness of aquaponic production, fish diets and feeding schedules should be designed carefully to provide nutrients at the right level and time to complement fish, bacteria and plants. To achieve this, a species-specific tailor-made aquaponic feed may be optimized to suit the aquaponic system as a whole. The optimal point would be determined based on overall system performance parameters, including economic and environmental sustainability measures. This chapter thus focuses on fish diets and feed and reviews the state of the art in fish diets, ingredients and additives, as well as the nutritional/sustainable challenges that need to be considered when producing specific aquaponic feeds.
Conference papers on the topic "Optimised schedule"
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Silva, W. P. M., S. Gunatilake, and M. F. F. Fasna. "OPTIMISING VALUE DURING CONSTRUCTION SCHEDULE ACCELERATION." In The 9th World Construction Symposium 2021. The Ceylon Institute of Builders - Sri Lanka, 2021. http://dx.doi.org/10.31705/wcs.2021.38.
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Construction Schedule Acceleration (CSA) can affect not just the time but many other project aspects. During a CSA, there can be many impacts having significant effects on the project value. Previous studies have highlighted the importance of considering quality, productivity and functionality at a CSA along with cost and time. The aim of this research is, therefore, to investigate how value can be optimised during a CSA process. A qualitative research approach was adopted. Altogether, ten semi-structured interviews were conducted. The interview data were transcribed and analysed using a manually performed content analysis. This study has revealed types of CSA based on the purpose (delay minimising purpose or non-delay minimising purpose) and the party who initiates it. Cost, quality, functionality, productivity and profitability were identified as main value considerations during a CSA other than time. Applicability of value management (VM) job plan stages and Earned Value Management (EVM) indicators during different CSA stages to optimise value were also found. Finally, these findings were mapped in a framework to show how VM and EVM concepts could be used in enhancing value during a CSA. The framework conceptualises the relationship between “value” and CSA and how it varies upon distinct parties of a construction project during different stages and types of CSA. The proposed framework can be used as a guidance for optimising the value during the stages of a CSA.
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Joundale, Sneha Bhimrao, Devendra Sutar, and Sajit Sadanandan. "Development of battery machine automation using optimised auto schedule for coke ovens." In 2017 International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2017. http://dx.doi.org/10.1109/iccmc.2017.8282673.
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Krishnababu, Senthil, Vili Panov, Simon Jackson, and Andrew Dawson. "Quick Start of an Industrial Gas Turbine Engine Through the Development of “Silent Start” VGV Schedule." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15983.
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Abstract In this paper, research that was carried out to optimise an initial variable guide vane schedule of a high-pressure ratio, multistage axial compressor is reported. The research was carried out on an extensively instrumented scaled compressor rig. The compressor rig tests carried out employing the initial schedule identified regions in the low speed area of the compressor map that developed rotating stall. Rotating stall regions that caused undesirable non-synchronous vibration of rotor blades were identified. The variable guide vane schedule optimisation carried out balancing the aerodynamic, aero-mechanical and blade dynamic characteristics gave the ‘Silent Start’ variable guide vane schedule, that prevented the development of rotating stall in the start regime and removed the non-synchronous vibration. Aerodynamic performance and aero-mechanical characteristics of the compressor when operated with the initial schedule and the optimised ‘Silent Start’ schedule are compared. The compressor with the ‘Silent Start’ variable guide vane schedule when used on a twin shaft engine reduced the start time to minimum load by a factor of four and significantly improved the operability of the engine compared to when the initial schedule was used.
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Gallar, L., M. Arias, V. Pachidis, and P. Pilidis. "Compressor Variable Geometry Schedule Optimisation Using Genetic Algorithms." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-60049.
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Variable geometry blade rows in axial compressors are devised to fulfil different requirements. Main objectives include their role as a “part speed crutch” to push the front stages out of surge at low spool speeds, modulation of the power output in industrial machines — given the fact that the spool needs to run at synchronous speed with the electric generator frequency — and they can also be re-staggered to attain a modified capacity (usually upflowed) of the same baseline compressor. The operating schedule of the variable vanes is typically obtained from expensive and time consuming performance rig tests in which a large number of possible combinations are compared. In principle, the final choice is dictated by the pursuit of high efficiency at high rotational speeds and increased surge margin at low speeds where large excursions away from the design point are expected. The aim of this work is to integrate a validated genetic algorithm optimiser within an industry proprietary mean line compressor performance prediction code to maximise the machine efficiency while keeping an adequate user-defined value of the surge margin. In so doing, an optimised variable geometry schedule is derived, together with a modified range of rotational speeds for each given operating point. Nevertheless, aware of the detrimental consequences to the whole engine performance that the new arrangement can cause, the whole engine response for the new settings has been investigated. In this regard and to a first order, the working line on the compressor map is considered unaffected by the setting of the variable vanes and the effect of the spool speed variation on the turbine operation is accounted for by a reduction in turbine efficiency proportional to any fall in the shaft speed. Results for a state of the art eight stage compressor show a marked improvement for the coupled compressor-turbine efficiency particularly at low spool speeds for a sensible value of the surge margin. Free from the surge margin constraint the efficiency is further increased at the expense of a hindered compressor operational stability. The work is intended to continue with the incorporation of bleeds and power off take in the calculations for the sake of a greater applicability of the tool.
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Mazorra, Z., X. Popa, B. Garcia, V. Huerta, CE Viada, E. Neninger, PC Rodriguez, Z. Gonzalez, A. Gonzalez, and T. Crombet. "PO-486 Surrogate biomarkers of clinical efficacy in stage IIIB/IV non-small-cell lung cancer patients treated with an optimised EGF-based vaccination schedule." In Abstracts of the 25th Biennial Congress of the European Association for Cancer Research, Amsterdam, The Netherlands, 30 June – 3 July 2018. BMJ Publishing Group Ltd, 2018. http://dx.doi.org/10.1136/esmoopen-2018-eacr25.988.
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Kalel, Navnath, Jayashree Bijwe, and Ashish Darpe. "Enhanced Performance of Eco-friendly Brake-pads by Using Plasma Treated Metallic Particles." In EuroBrake 2021. FISITA, 2021. http://dx.doi.org/10.46720/6792579eb2021-mds-009.
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Metallic particles in various amounts are commonly used in the brake-pad-formulations to improve the friction, mechanical strength, thermal conductivity, heat dissipation, fade etc. The same metallic particles, however, lead to increase in wear. To overcome the wear related problem and to improve the tribo-performance further, wettability/surface free-energy (SFE) of metallic particles needs to be improved so that they will not be easily dug out contributing to higher wear that without metal particles. Current era is of copper-free friction materials. Hence, in this work, stainless steel particles (SSPs) and copper particles were treated with low pressure argon plasma gas using optimised processing parameters (viz., gas- Argon, power- 500 W, treatment time-20 min.,). A series of four multi-ingredient brake-pads using identical composition but differing in the theme-ingredients (3 vol.%) was developed. First two types of brake-pads were based on untreated identical sized particles (SSPs and Cu) and another two types of brake-pads were based on plasma treated particles. One more type of brake-pad was developed without metallic particles. The developed brake-pads were evaluated for different physical, mechanical and chemical properties. Tribological performance was evaluated on a full-scale dynamometer following test schedule (JASO C406). Additionally, noise-vibration (NV) performance was evaluated on NV test rig following SAE J 2521 (partly) test schedule. Results revealed that most of the tribological properties along with NV properties were improved for treated particles in brake-pads. The topography of worn brake-pads was done to understand the wear mechanisms.
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Tanaka, Shunji. "Idle Time Treatment in the Single-Machine Scheduling Problem With Distinct Release Dates." In ASME/ISCIE 2012 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/isfa2012-7172.
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The purpose of this study is to examine how idle time treatment in the single-machine scheduling problem with distinct (unequal) release dates affects schedules. For this purpose, two types of settings are considered: any idle time is permitted and unforced idle time is forbidden. In the latter setting, idle time is permitted only when no jobs are available, that is, release dates of unprocessed jobs are larger than the current time instant. Under these two idle time settings, the problem is solved both offline and online. In offline scheduling, all job information is known in advance and the schedule is optimized only once at time zero while in online scheduling, the schedule is re-optimized for only currently available jobs every time when a new job becomes available at its release date. Benchmark instances in the literature are solved by these approaches and the effect of idle time treatment on the obtained schedules is examined.
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O'Leary, Daniel, Deirdree Polak, Roman Popat, Oliver Eatough, and Tom Brian. "First Use of Machine Learning for Penetration Rate Optimisation on Elgin Franklin." In SPE Offshore Europe Conference & Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205466-ms.
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Abstract Optimising the Rate of Penetration (ROP) on Development wells contributes heavily to delivery of projects ahead of schedule and has long been a goal for drilling engineers. Selecting the best parameters to achieve this has often proved difficult due to the extensive quantities of data concerning formation types, bottom-hole assembly (BHA) design and bit specifications. Legacy drilling data can also be vast and not well characterised, making it very difficult to robustly analyse manually. Additionally, multiple stakeholders can each have their own hypotheses on how to improve drilling performance, including bit vendors, directional drilling companies, drilling engineers and offshore supervisors, creating further confusion in this field. Together with its team of data scientists, TotalEnergies E&P UK (TEPUK) has utilised machine learning to analyse field and equipment data and produce guidelines for optimised drilling rate. The machine learning algorithm identifies parameters which have a statistical likelihood of improving ROP performance whilst drilling. The model was developed using offset well data from TotalEnergies' Realtime Support Centre (RTSC) and bit design information. This represented the first use of Machine Learning in the 20+ years of drilling on Elgin Franklin. Adapting to this new data-based method forms part of a wider digital revolution within TEPUK and the Offshore Drilling Industry. In this case, an integrated approach from the data scientists, drilling engineers and supervisors was required to transition to a new way of working. The first trial of using optimised parameters was on a recent Franklin well (F13) in the Cretaceous Chalk formations. The model generated statistically optimised parameter sheets which were strictly executed on site. Within the guideline sheets were suggested ranges of Revolutions per Minute (RPM), Flowrate, Weight on Bit (WOB) and Torque, as well as recommendations for bit blades and cutters. Heatmaps were generated to show what combination of WOB and RPM would likely achieve best ROP in each sub formation. The parameter range defined was specifically narrow to reduce any time spent varying parameters. In practice the new digital approach was successfully adopted offshore and contributed to the delivery of the 12 ½" and 8 ½" sections in record time for the field, resulting in significant savings versus AFE. Following the success of the guideline implementation, steps have been taken to integrate the machine learning model with live incoming data on TotalEnergies' digital drilling online platform. Since the initial trial on Franklin, online ROP optimisation features have been deployed on the Elgin field and currently provide live parameter guidance, a forecast to section TD and data driven bit change scenario analyses whist drilling.
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Sun, Chao, Yulong Lei, Xiaofeng Yin, Kexu Chen, Chang Dou, and Xiaohua Wu. "Personalized Intelligent Gearshift Schedule Optimization for Electric Vehicles with AMT Considering Multi-performance and Drivetrain Efficiency." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2020-adm-007.
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Research and/or Engineering Questions/Objective With the help of automated manual transmission (AMT), the power demand and size of the traction motor of electric vehicles (EVs) could be reduced and such EVs will have more chances to work within the high efficiency area of motor. To further improve the performance and intelligent level of EVs equipped with AMT, the drivability, energy economy and driver’s performance expectation need to be considered together, and meanwhile, the efficiency of the motor and the gearbox should be taken into account. This investigation aims to optimize the gearshift schedule for EVs considering multi-performance, driver’s intention and efficiency of the whole drivetrain. Methodology An efficiency model of the drivetrain including the traction motor and the gearbox is established using neural network, which consists of one sub-efficiency model for the motor, and one sub-efficiency model for each gear. The latter takes the input torque and the input speed of gearbox as input, and the efficiency of gearbox as output. Based on a linear decreasing weight particle swarm optimization (LDWPSO) algorithm, a new gearshift schedule optimization method considering the drivability, energy economy, and driver’s performance expectation, in which the efficiency model of the whole drivetrain is used to calculate the vehicle performance indexes and the efficiency constraint, is put forward. The proposed method is then used to optimize the personalized intelligent multi-performance optimal gearshift schedules for a test EV equipped with AMT. The proposed method and the vehicle performance using the optimized gearshift schedules have also been evaluated via simulation experiments. Results The neural network-based efficiency model of the drivetrain of electric vehicle could build a precise mathematic mapping between the operating conditions and the efficiency of the drivetrain. The optimized gearshift schedule considering multi-performance, driver’s performance expectation and efficiency of drivetrain could make the electric vehicle have better energy economy and driving range and meanwhile maintain good drivability in line with the driver’s intention. Limitations of this study The energy conversion efficiency of the EV battery has not been included in the efficiency model of the drivetrain in this investigation. Further investigation regarding this issue based on battery performance test, will be part of our future work. What does the paper offer that is new in the field including in comparison to other work by the authors? The efficiency model of EV drivetrain including traction motor and gearbox, as well as the proposed optimization method for the personalized intelligent gearshift schedule for EVs equipped with AMT are new. Conclusions A neural network-based efficiency model of EV drivetrain including traction motor and gearbox has been established, and a novel optimization method based on a linear decreasing weight particle swarm optimization algorithm for personalized intelligent gearshift schedule for EVs equipped with AMT has been proposed. Simulation results show that the proposed method is applicable to improve the comprehensive performance of EVs including drivability and energy economy according to driver’s performance expectation.
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Brilliant, Lisa, Stanley Balamucki, George Burger, Yuan Dong, and Charlie Lejambre. "Application of Multistage CFD Analysis to Low Pressure Compressor Design." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-54263.
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A Low Pressure Compressor (LPC) is unique in its requirements for wide operating range during a flight mission. As a result, the aerodynamic design involves a trade-off between performance and stall margin. The requirement to reduce engine development cost and schedule has resulted in developing LPCs during the engine validation program. With engine validation and certification schedules being compressed continuously, getting the initial design right has become critical. Multistage CFD analysis is used in the current design process to optimize the airfoils and stage matching. Three-dimensional airfoil features, such as bow, that improve secondary flow features and can be optimized using CFD. The PW6000 LPC engine test data has validated the analytical results and demonstrated surge margin and efficiency levels above the requirements. The LPC also achieved all other design objectives in its first build, representing a significant cost saving for a new centerline engine development program.