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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.
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Matsui, Y., K. Narita, T. Inoue, and T. Matsushita. "Screening level analysis for monitoring pesticide in river water using a hydrological diffuse pollution model with limited input data." Water Science and Technology 53, no. 10 (May 1, 2006): 173–81. http://dx.doi.org/10.2166/wst.2006.310.
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To predict rice-farming pesticide concentrations in river water with imprecise model inputs for screening-level analysis, a basin-scale runoff model was developed. The Monte Carlo method was applied to create estimates of input data regarding agricultural work schedules and parameters for pesticide decomposition and sorption in solids and water. The prediction accuracy of the model was evaluated when used with non-optimised pesticide parameters; the model was calibrated using hydrological data alone without reference to observed pesticide concentration data. Overall, predictions for the pesticide concentrations were successful within order-of-magnitude accuracy. The pesticide rankings according to the predicted concentration roughly agreed with those observed. The success of screening-level analysis indicates that the model prediction can help in selection of pesticides to be monitored and in determining the monitoring schedule for the river basin.
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Rakotonirainy, RG, I. Durbach, and J. Nyirenda. "Considering fairness in the load shedding scheduling problem." ORiON 35, no. 2 (December 20, 2019): 127–44. http://dx.doi.org/10.5784/35-2-648.
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Every day national power system networks provide thousands of MW of electric power from generating units to consumers, requiring different operations and planning to ensure secure systems. Where demand exceeds supply, load shedding - controlled, enforced reduction in supply - is necessary to prevent system collapse. Should load shedding need to be implemented, a planned schedule is necessary to allocate geographic areas on the required period of shedding. The problem of how to construct a schedule that fairly allocates load shedding responsibilities over geographic areas with minimum economic impacts is addressed in this paper. Two programming models are proposed. The first model consists of a linear integer programming model in which the objective is to minimise the economic cost subject to different fairness allocation constraints, while the second model involves formulation of the problem as a goal programming model in which different conflicting goals are simultaneously optimised. Several case studies are conducted in the context of a realistic, but hypothetical, scenario to explore the possible solutions that the proposed models provide. Results show that a fair schedule requires a high cost whereas lower cost can only be achieved with some sacrifices to the fairness of the schedule.
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Guest, D. S. H., R. Jena, C. Watts, S. C. Short, N. G. Burnet, K. J. Kirkby, and N. F. Kirkby. "81 MATHEMATICAL MODELLING OF A RADIOBIOLOGICALLY OPTIMISED FRACTIONATION SCHEDULE FOR A HETEROGENEOUSLY DIFFERENTIATED CELL MODEL OF GLIOBASTOMA." Radiotherapy and Oncology 102 (March 2012): S29—S30. http://dx.doi.org/10.1016/s0167-8140(12)70058-0.
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Lim, Nigel, and Lucas Lim. "An application and subsequent benefits of digitalisation to pipeline engineering using a 3D pipe-in-pipe bulkhead design project case study." APPEA Journal 60, no. 2 (2020): 641. http://dx.doi.org/10.1071/aj19125.
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Digitalisation of design processes is being rapidly accepted as a major step-change in engineering of pipelines and components. Incremental digitalisation of best engineering practice, analytical portions of industry codes and iterative optimisation tasks enable multiple advantages over typical project execution models. These include (a) maintaining flexibility throughout the design process to deal with changes particularly at critical interface points, (b) utilising computing power to drive an optimised solution, (c) cost and schedule advantages and (d) development of platforms to implement in future projects. These benefits are demonstrated by detailing an application of Genesis’s small-scale digitalisation strategy, as implemented on a pipe-in-pipe (PiP) bulkhead design in the Edvard Grieg Tie-Backs engineering, procurement, construction and installation development in the North Sea. The application of this strategy involved initial scoping and production of a design platform consisting of three Python modules. The platform enabled full parameterisation of a 3D end-bulkhead finite element analysis modelling process and post-processing criteria. The flexibility of this digitalisation strategy enabled simulations of (a) multiple material configurations, such as the application or removal of corrosion resistant cladding and (b) streamlined execution of highly iterative design processes. Through this platform, parallel execution of detail design and procurement was optimised, leading to achievement of material cost savings while minimising schedule and variation impact. This example of small-scale digitalisation establishes a philosophy for the future execution of PiP design and aligns both strategic and incremental steps towards a larger digitalisation initiative.
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Akram, Junaid, Hafiz Suliman Munawar, Abbas Z. Kouzani, and M. A. Pervez Mahmud. "Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks." Sensors 22, no. 3 (January 30, 2022): 1083. http://dx.doi.org/10.3390/s22031083.
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Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.
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Chung, Woodam, Greg Jones, Kurt Krueger, Jody Bramel, and Marco Contreras. "Optimising fuel treatments over time and space." International Journal of Wildland Fire 22, no. 8 (2013): 1118. http://dx.doi.org/10.1071/wf12138.
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Fuel treatments have been widely used as a tool to reduce catastrophic wildland fire risks in many forests around the world. However, it is a challenging task for forest managers to prioritise where, when and how to implement fuel treatments across a large forest landscape. In this study, an optimisation model was developed for long-term fuel management decisions at a landscape scale. Using a simulated annealing algorithm, the model optimises locations and timing of fuel treatments, while considering changes in forest dynamics over time, fire behaviour and spread, values at risk, and operational feasibility. The model employs the Minimum Travel Time algorithm in FlamMap and the Fire and Fuels Extension to the Forest Vegetation Simulator to assess spatial and temporal effects with and without fuel treatments. The objective function is set to minimise total expected loss from a landscape due to wildfires throughout the planning horizon. The model was applied to a 14 000-ha study landscape located on the west side of the Bitterroot Valley in Montana. Comparisons between the optimised and random solutions show that the model was able to strategically locate and schedule fuel treatments to efficiently reduce expected loss from the landscape.
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Wang, Peng, Peng Wu, Xiangyu Wang, Xin Chen, and Tao Zhou. "Developing optimal scaffolding erection through the integration of lean and work posture analysis." Engineering, Construction and Architectural Management 27, no. 9 (May 13, 2020): 2109–33. http://dx.doi.org/10.1108/ecam-04-2019-0193.
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PurposeFacility structures in liquefied natural gas (LNG) plants require tremendous amounts of scaffolding to facilitate relevant industrial operation and maintenance. As such, the productivity of scaffolding operations in turnaround maintenance (TAM) has attracted much attention in recent years. In addition, health and safety issues have been recognised as a key contributor along with productivity improvement in the LNG industry. This study aims to integrate work posture analysis into value stream mapping to achieve an optimised and balanced improvement in both productivity and health and safety.Design/methodology/approachA case study approach is adopted to integrate lean and work posture analysis in a TAM site. The lean improvement is conducted through value stream mapping, and the work posture analysis is conducted through the Ovako Working Posture Analysis System method. A three-step optimisation strategy is then developed for achieving optimised performance in waste reduction and work posture improvement.FindingsIt is found that the implementation of value stream mapping can help eliminate waste in the installation process, therefore eliminating potential health and safety risks. However, health and safety of onsite workers does not always improve as lean implementation intensifies. There is an optimised erection schedule that has the lowest health and safety risk within a waste reduction target.Originality/valueIn contradiction to previous studies, which rely on qualitative assessment to identify the a positive correlation between lean and health and safety, this study reveals the distinct difference between lean attributes and health and safety attributes through a quantitative assessment and is more readily to be implemented at the site level for simultaneous improvement in lean and health and safety.
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Pradhan, Ninad, Dinesh Patlolla, and Rupy Sawhney. "Scheduling and route optimisation for labour cost reduction in facility custodial maintenance." Journal of Facilities Management 15, no. 2 (May 15, 2017): 190–206. http://dx.doi.org/10.1108/jfm-11-2016-0048.
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Purpose The purpose of this paper is to present an optimised scheduling system for facility mangers and custodians. Experience-driven systems currently in use can result in poor ratings for facility maintenance metrics such as overtime hours, utilisation difference and labour costs. Design/methodology/approach The cleaning schedule and custodian work assignments defined by the manager are simulated for the entire year. Clustering and routing algorithms assign work to custodians equally and find optimal cleaning routes. The manager may use the resulting feedback to iteratively find a suitable schedule which lowers costs. Findings Data were collected at a large university building in consultation with facility management and custodians. Results indicate a significant reduction in overtime hours, improvement in utilisation difference and a lowering of labour costs. Research limitations/implications The methodology was validated at a single building in the facility. Variable selection and optimisation model design will benefit from a comprehensive case study which spans the entire facility. Practical implications The methodology may easily be integrated with existing facility maintenance software, adding to it features such as a manager scheduling interface with feedback on critical cleaning metrics and a custodian user interface which highlights room visitation routes and task times. Originality/value This study acts on the need for facility cleaning labour cost management highlighted in literature. It achieves its goals using a novel combination of scheduling, simulation and optimisation. It is designed to empower key decision-makers, i.e. facility managers and custodians, with better information.
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Kersting, Mathilde, Hermann Kalhoff, Michael Melter, and Thomas Lücke. "Vegetarische Kostformen in der Kinderernährung?" DMW - Deutsche Medizinische Wochenschrift 143, no. 04 (February 2018): 279–86. http://dx.doi.org/10.1055/s-0043-119864.
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AbstractIn Germany, the “Dietary Schedule for the 1st year of life” and the “Optimised Mixed Diet” for children and adolescents serve as scientifically based and generally applicable dietary concepts throughout the period of growth and development. Vegetarian diets as the lacto-ovo-vegetarian diet (exclusion of meat, fish) and the vegan diet (exclusion of all food groups of animal origin) need to be evaluated for their potential to safely meet the high and specific requirements for growth and development. In this regard, high-quality studies are needed. In individuals on lacto-ovo-vegetarian diets, the safe supply with critical nutrients should be checked by thorough dietary history, possibly additional laboratory tests in risk situations like pregnancy, infancy and toddlerhood. Children on pure vegan diet need ongoing elaborate dietary strategies and continuous supplementation at any age, similar to nutritional management in children with metabolic disorders. A vegan diet is disadvised during all periods with intense growth and development.
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Lappin, James, Tom Jackson, Graham Matthews, and Clare Ravenwood. "Rival records management models in an era of partial automation." Archival Science 21, no. 3 (January 22, 2021): 243–66. http://dx.doi.org/10.1007/s10502-020-09354-9.
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AbstractTwo rival records management models emerged during the 1990s. Duranti’s model involved moving records out of business applications into a repository which has a structure/schema optimised for recordkeeping. Bearman’s model involved intervening in business applications to ensure that their functionality and structure/schema are optimised for record keeping. In 2013 the US National Archives and Records Administration began asking Federal agencies to schedule important email accounts for permanent preservation. This approach cannot be mapped to either Duranti or Bearman’s model. A third records management model has therefore emerged, a model in which records are managed in place within business applications even where those applications have a sub-optimal structure/schema. This model can also be seen in the records retention features of the Microsoft 365 cloud suite. This paper asks whether there are any circumstances in which the in-place model could be preferable to Duranti and Bearman’s models. It explores the question by examining the evolution of archival theory on the organisation of records. The main perspectives deployed are those of realism and of records continuum theory. The paper characterises the first two decades of this century as an era of partial automation, during which organisations have had a general capability to automate the assignment of business correspondence to a sub-optimal structure/schema (that of their email system and/or other messaging system) but not to an optimal structure/schema. In such an era any insistence on optimising the structure/schema within which correspondence is managed may paradoxically result in a reduction in recordkeeping efficiency and reliability.
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Burgett, Rebecca Nicole, Thomas Michael Farley, and Lori Ann Beireis. "Acute treatment of psychotic symptoms in a newly diagnosed Lewy body dementia patient with an accelerated titration schedule of rivastigmine and de-escalation of antipsychotics." BMJ Case Reports 12, no. 9 (September 2019): e230193. http://dx.doi.org/10.1136/bcr-2019-230193.
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A 76-year-old man presented with complaints of increased confusion, visual hallucinations and decline in memory. He was admitted to the hospital and started on quetiapine 50 mg daily for symptom management. Shortly after, he was diagnosed with Lewy body dementia and started on rivastigmine, a cholinesterase inhibitor (ChEI), at 1.5 mg two times per day. The patient’s symptoms continued to worsen and antipsychotics increased for aggressive behaviour. After he became physically aggressive, an extensive medication management review was conducted, prompting an alternative treatment strategy. The quetiapine dose was reduced from 150 mg daily to 12.5 mg daily, his rivastigmine was increased to 3 mg two times per day and all other antipsychotics were discontinued. The up-titration of his rivastigmine after 10 days of therapy was well tolerated with no adverse effects. He demonstrated a clear clinical response to optimised ChEI therapy and low dose quetiapine, showing improvements in alertness and functioning.
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Fitzgerald, Ita, Jean O'Connell, Dolores Keating, Caroline Hynes, Stephen McWilliams, and Erin K. Crowley. "Metformin in the management of antipsychotic-induced weight gain in adults with psychosis: development of the first evidence-based guideline using GRADE methodology." Evidence Based Mental Health 25, no. 1 (September 29, 2021): 15–22. http://dx.doi.org/10.1136/ebmental-2021-300291.
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BackgroundAdjunctive metformin is the most well-studied intervention in the pharmacological management of antipsychotic-induced weight gain (AIWG). Although a relatively unaddressed area, among guidelines recommending consideration of metformin, prescribing information that would facilitate its applied use by clinicians, for example, provision of a dose titration schedule is absent. Moreover, recommendations differ regarding metformin’s place in the hierarchy of management options. Both represent significant barriers to the applied, evidence-based use of metformin for this indication.ObjectiveTo produce a guideline solely dedicated to the optimised use of metformin in AIWG management, using internationally endorsed guideline methodology.MethodsA list of guideline key health questions (KHQs) was produced. It was agreed that individual recommendations would be ‘adopted or adapted’ from current guidelines and/or developed de novo, in the case of unanswered questions. A systematic literature review (2008–2020) was undertaken to identify published guidelines and supporting (or more recent) research evidence. Quality appraisal was undertaken using the Appraisal of Guidelines Research and Evaluation II tool, A Measurement Tool to Assess Systematic Reviews (AMSTAR) assessment,and the Cochrane Risk of Bias 2 tool, where appropriate. Assessment of evidence certainty and recommendation development was undertaken using Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology.FindingsWe confirmed that no published guideline—of appropriate quality, solely dedicated to the use of metformin to manage AIWG was available. Recommendations located within other guidelines inadequately addressed our KHQs.ConclusionAll 11 recommendations and 7 supporting good practice developed here were formulated de novo.Clinical implicationsThese recommendations build on the number and quality of recommendations in this area, and facilitate the optimised use of metformin when managing AIWG.
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Schrage, Rico, Paul Hendrik Tiemann, and Astrid Niesse. "A Multi-Criteria Metaheuristic Algorithm for Distributed Optimization of Electric Energy Storage." ACM SIGEnergy Energy Informatics Review 2, no. 4 (December 2022): 44–59. http://dx.doi.org/10.1145/3584024.3584029.
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The distributed schedule optimization of energy storage constitutes a challenge. Such algorithms often expect an input set containing all feasible schedules and, therefore, require searching the schedule space efficiently. However, it is hardly possible to accomplish this with energy storage due to its high flexibility. In this paper, the problem is introduced in detail and addressed by a metaheuristic algorithm, which generates a preselection of schedules. Two contributions are presented to achieve this goal: First, an extension for a distributed schedule optimization allowing a simultaneous optimization is developed. Second, an evolutionary algorithm is designed to generate optimized schedules with respect to multiple criteria. It is shown that the presented approach is suitable to schedule electric energy storage in actual households and industries with different generator and storage types.
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Savage, David W., Douglas G. Woolford, Bruce Weaver, and David Wood. "Developing emergency department physician shift schedules optimized to meet patient demand." CJEM 17, no. 1 (January 2015): 3–12. http://dx.doi.org/10.2310/8000.2013.131224.
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AbstractObjectives: 1) To assess temporal patterns in historical patient arrival rates in an emergency department (ED) to determine the appropriate number of shift schedules in an acute care area and a fast-track clinic and 2) to determine whether physician scheduling can be improved by aligning physician productivity with patient arrivals using an optimization planning model.Methods: Historical data were statistically analyzed to determine whether the number of patients arriving at the ED varied by weekday, weekend, or holiday weekend. Poisson-based generalized additive models were used to develop models of patient arrival rate throughout the day. A mathematical programming model was used to produce an optimal ED shift schedule for the estimated patient arrival rates. We compared the current physician schedule to three other scheduling scenarios: 1) a revised schedule produced by the planning model, 2) the revised schedule with an additional acute care physician, and 3) the revised schedule with an additional fast-track clinic physician.Results: Statistical modelling found that patient arrival rates were different for acute care versus fast-track clinics; the patterns in arrivals followed essentially the same daily pattern in the acute care area; and arrival patterns differed on weekdays versus weekends in the fast-track clinic. The planning model reduced the unmet patient demand (i.e., the average number of patients arriving at the ED beyond the average physician productivity) by 19%, 39%, and 69% for the three scenarios examined.Conclusions: The planning model improved the shift schedules by aligning physician productivity with patient arrivals at the ED.
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Xiao, B., Eric J. Palmiere, A. A. Howe, and H. C. Carey. "Multi-Pass Simulation of Heavy Plate Rolling Including Intermediate Forced Cooling." Advanced Materials Research 409 (November 2011): 443–48. http://dx.doi.org/10.4028/www.scientific.net/amr.409.443.
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Thermomechanical Controlled Processing (TMCP) including accelerated cooling after the final hot rolling pass is a well-established technology, widely applied in HSLA steel plate production. However, there are still certain limitations, especially for thicker plate. The rolling schedule includes a long holding period (HP) after the roughing stage to allow the temperature to fall sufficiently for optimised TMCP during finishing. Intermediate Forced Cooling (IFC) applied during the HP can increase productivity by decreasing the required hold time, can restrict austenite grain growth, and can also improve the subsequent strain penetration in thick plate with further metallurgical benefits. Multi-pass plane strain compression (PSC) tests have been performed on the thermomechanical compression (TMC) machine at Sheffield University including different severities of IFC. Clearly it is impossible to simulate all aspects of the temperature and strain gradients present in thick plates in laboratory specimens, and most of the tests were conducted at temperatures and strains calculated by Finite Element modelling as relevant to specific positions through the plate thickness. However, some aspects of the gradients were addressed with tests using cold platens. The results have indeed shown that IFC can shorten the HP and reduce austenite grain growth and its variation across thick plate.
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Hanappe, P., A. Beurivé, F. Laguzet, L. Steels, N. Bellouin, O. Boucher, Y. H. Yamazaki, T. Aina, and M. Allen. "FAMOUS, faster: using parallel computing techniques to accelerate the FAMOUS/HadCM3 climate model with a focus on the radiative transfer algorithm." Geoscientific Model Development 4, no. 3 (September 27, 2011): 835–44. http://dx.doi.org/10.5194/gmd-4-835-2011.
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Abstract. We have optimised the atmospheric radiation algorithm of the FAMOUS climate model on several hardware platforms. The optimisation involved translating the Fortran code to C and restructuring the algorithm around the computation of a single air column. Instead of the existing MPI-based domain decomposition, we used a task queue and a thread pool to schedule the computation of individual columns on the available processors. Finally, four air columns are packed together in a single data structure and computed simultaneously using Single Instruction Multiple Data operations. The modified algorithm runs more than 50 times faster on the CELL's Synergistic Processing Element than on its main PowerPC processing element. On Intel-compatible processors, the new radiation code runs 4 times faster. On the tested graphics processor, using OpenCL, we find a speed-up of more than 2.5 times as compared to the original code on the main CPU. Because the radiation code takes more than 60 % of the total CPU time, FAMOUS executes more than twice as fast. Our version of the algorithm returns bit-wise identical results, which demonstrates the robustness of our approach. We estimate that this project required around two and a half man-years of work.
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Kagiri, Charles, Lijun Zhang, and Xiaohua Xia. "A Hierarchical Optimisation of a Compressed Natural Gas Station for Energy and Fuelling Efficiency under a Demand Response Program." Energies 12, no. 11 (June 6, 2019): 2165. http://dx.doi.org/10.3390/en12112165.
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Compressed natural gas stations serve customers who have chosen compressed natural gas powered vehicles as an alternative to diesel and petrol based ones, for cost or environmental reasons. The interaction between the compressed natural gas station and electricity grid requires an energy management strategy to minimise a significant component of the operating costs of the station where demand response programs exist. Such a strategy when enhanced through integration with a control strategy for optimising gas delivery can raise the appeal of the compressed natural gas, which is associated with reduced criteria air pollutants. A hierarchical operation optimisation approach adopted in this study seeks to achieve energy cost reduction for a compressed natural gas station in a time-of-use electricity tariff environment as well as increase the vehicle fuelling efficiency. This is achieved by optimally controlling the gas dispenser and priority panel valve function under an optimised schedule of compressor operation. The results show that electricity cost savings of up to 60.08% are achieved in the upper layer optimisation while meeting vehicle gas demand over the control horizon. Further, a reduction in filling times by an average of 16.92 s is achieved through a lower layer model predictive control of the pressure-ratio-dependent fuelling process.
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Vithanage, S. C., M. C. P. Sing, P. Davis, and T. Newaz. "The Influence of Organisational Safety Climate Factors on Offsite Manufacturing Safety Performance." IOP Conference Series: Earth and Environmental Science 1101, no. 4 (November 1, 2022): 042028. http://dx.doi.org/10.1088/1755-1315/1101/4/042028.
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Abstract The construction industry consistently reports higher fatality and injury rates due to its inherent dynamic nature. As an alternative to traditional construction, offsite manufacturing (OSM) has become a construction method that presents many benefits including optimised schedule, quality control, waste reduction and improved site safety. Contrastingly, recent statistics on OSM indicated that in the USA for instance, the number of safety incidents were higher compared to traditional construction. There is a suggestion that organisations simply shift site-based construction activities to a factory. Therefore, this study aims to investigate the influence of organisational safety climate on OSM safety performance. Initially, a comprehensive literature review was conducted to identify organisational safety climate factors. Following this, 131 questionnaires were collected from Australian OSM practitioners. Finally, a Pearson correlation analysis was performed to examine the influence of organisational safety climate on OSM safety performance. Findings asserted that safety communication has the greatest impact on safety performance. Contrary to traditional construction practice, greater influence from managers was identified over supervisors towards safety performance. The novelty of this research lies in its attempt to comprehend the association between organisational safety climate and safety performance, which may make contribution to OSM safety knowledge and practice.
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Hidri, Lotfi, Achraf Gazdar, and Mohammed M. Mabkhot. "Optimized Procedure to Schedule Physicians in an Intensive Care Unit: A Case Study." Mathematics 8, no. 11 (November 6, 2020): 1976. http://dx.doi.org/10.3390/math8111976.
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Hospitals are facing an important financial pressure due to the increasing of the operating costs. Indeed, the growth for the hospitals’ services demand causes a rising in the number of required qualified personnel. Enlarging the personnel number increases dramatically the fixed total cost. Based on some studies, 50% of operating costs in US hospitals are allocated to healthcare personnel. Therefore, reducing these types of costs without damaging the service quality becomes a priority and an obligation. In this context, several studies focused on minimizing the total cost by producing optimal or near optimal schedules for nurses and physicians. In this paper, a real-life physicians scheduling problem with cost minimization is addressed. This problem is encountered in an Intensive Care Unit (ICU) where the current schedule is manually produced. The manual schedule is generating a highly unbalanced load within physicians in addition to a high cost overtime. The manual schedule preparation is a time consuming procedure. The main objective of this work is to propose a procedure that systematically produces an optimal schedule. This optimal schedule minimizes the total overtime within a short time and should satisfies the faced constraints. The studied problem is mathematically formulated as an integer linear program. The constraints are real, hard, and some of them are non-classical ones (compared to the existing literature). The obtained mathematical model is solved using a state-of-the-art software. Experimental tests on real data have shown the performance of the proposed procedure. Indeed, the new optimal schedules reduce the total overtime by up to 69%. In addition, a more balanced workload for physicians is obtained and several physician preferences are now satisfied.
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Goekbuget, Nicola, Karl-Heinz Baur, Joachim Beck, Helmut Diedrich, Monika Lamprecht, Lothar Leimer, Thomas Lipp, et al. "Dexamethasone Dose and Schedule Significantly Influences Remission Rate and Toxicity of Induction Therapy in Adult Acute Lymphoblastic Leukemia (ALL): Results of the GMALL Pilot Trial 06/99." Blood 106, no. 11 (November 16, 2005): 1832. http://dx.doi.org/10.1182/blood.v106.11.1832.1832.
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Abstract In 1999 the German Multicenter Study Group for Adult ALL (GMALL) activated a pilot study (GMALL 06/99). One major aim was to develop a new, shortened and intensified induction regimen based on the following new principles compared to previous GMALL trials: 1) Dexamethasone (DEXA) instead of prednisone to improve antileukemic activity and prophylaxis of CNS relapse 2) prephase with cyclophosphamide (CYCLO) 3) G-CSF parallel to chemo 4) intensified daunorubicin with two 2day cycles (DNR) vs 4 wkly applications 5)1 dose PEG-L-Asparaginase (ASP) instead of 14 d conventional ASP Induction I was followed by GMALL induction phase II as previously reported and a uniform consolidation I. Remission control took place on d24 and d44. Thereafter treatment was risk adapted. Induction I consisted of DEXA, CYCLO and G-CSF. In addition pts received PEG-ASP 1000 U/m2 (d13), vincristin 2 mg (d4,11,18) and DNR 45 mg/m2 (d4+5,11+12). The regimen was modified by 3 amendments which separated the study to 4 pilot phases. The major modifications referred to reduction of DEXA/CYCLO and earlier application of G-CSF. Table 1: Major modifications of induction phase I Drug Pilot 1 Pilot 2 Pilot 3 DEXA 40 mg/m2 (d1–3) 10 mg/m2 (d4–17) 10 mg/m2 (d 1–6,11–16) 10 mg/m2 (d 1–5,11–14) CYCLO 200 mg/m2 (d1–3) none none G-CSF from d13 from d4 from d4 Overall 843 pts were included between 4/99 and 10/03. The median age was 36 (15–65) yrs. Subtypes distribution was c-/pre B 65%, pro B 8%, early T 8%, thymic 14%, mature T 6%. 23% had Ph/BCR-ABL+ ALL. The overall CR rate was 83%, with 12% failure/PR and 7% early death (ED). Significant differences were detected for the pilot phases (p=.0008). The high mortality in pilot I was mainly due to infections. With lower doses of DEXA the rate of ED (p=.0002) and severe infections decreased significantly whereas the failure rate increased slightly. The earlier application of G-CSF contributed to a significant decrease of grade III/IV granulocytopenias and probably also mucositis. Table 2: Results and major toxicities (grade III/IV) of induction therapy Pilot 1 Pilot 2 Pilot3 P Evaluable 103 100 605 CR 76% 83% 82% .0008 PR/Failure 9% 9% 14% ED 16% 8% 5% Survival (3y) 45% 47% 47% >.05 Granulopenia 84% 72% 69% .008 Median duration 17d 15d 12d <.0001 Infections 30% 33% 14% <.0001 Mucositis 23% 14% 6% <.0001 We conclude that dose and schedule of DEXA in induction of ALL has a significant effect on rate of infections and ED. A dose reduced schedule with interruption to allow for early detection of infections led to a favourable CR rate of 82% with ED of 5% after only 6 wks of induction therapy. The parallel application of chemo and G-CSF rendered the regimen more feasible and contributed to the lower rate of infections and mucositis. The optimised regimen which includes again prephase with CYCLO is now used as induction therapy of the ongoing GMALL study 07/2003. Interim results confirmed the high antileukemic activity with CR rates of 89% and the feasibility with 4% ED. Beside the optimised induction the use of Imatinib in Ph+ ALL parallel to induction contributed to the improvement. Further progress is now attempted by additional use of rituximab in CD20 positive pts.
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Wang, Yabin, Chenghao Guo, and Jin Yu. "Immune Scheduling Network Based Method for Task Scheduling in Decentralized Fog Computing." Wireless Communications and Mobile Computing 2018 (September 2, 2018): 1–8. http://dx.doi.org/10.1155/2018/2734219.
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Fog computing has changed the distributed computing rapidly by including the smart devices widely distributed at the network edges. It is able to provide less latency and is more capable of decreasing traffic jam in the network. However, it will bring more difficulties for resource managing and task scheduling especially in a decentralized ad hoc network. In this paper, we propose a method that takes advantages of the immune mechanism to schedule tasks in a decentralized way for fog computing. By using forward propagation and backward propagation in the ad hoc network, the power of distributed schedulers is used to generate the optimized scheduler strategies to deal with computing nodes overloaded and achieve the optimal task finishing time reducing. The experiment results show that our approach can beat similar methods.
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Peng, Zhigong, Baozhong Zhang, Jiabing Cai, Zheng Wei, He Chen, and Yu Liu. "Optimization of Spring Wheat Irrigation Schedule in Shallow Groundwater Area of Jiefangzha Region in Hetao Irrigation District." Water 11, no. 12 (December 13, 2019): 2627. http://dx.doi.org/10.3390/w11122627.
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Due to the large spatial variation of groundwater depth, it is very difficult to determine suitable irrigation schedules for crops in shallow groundwater area. A zoning optimization method of irrigation schedule is proposed here, which can solve the problem of the connection between suitable irrigation schedules and different groundwater depths in shallow groundwater areas. The main results include: (1) Taking the annual mean groundwater depth 2.5 m as the dividing line, the shallow groundwater areas were categorized into two irrigation schedule zones. (2) On the principle of maximizing the yield, the optimized irrigation schedule for spring wheat in each zone was obtained. When the groundwater depth was greater than 2.5 m, two rounds of irrigation were chosen at the tillering–shooting stage and the shooting–heading stage with the irrigation quota at 300 mm. When the groundwater depth was less than 2.5 m, two rounds of irrigation were chosen at the tillering–shooting stage, and one round at the shooting–heading stage, with the irrigation quota at 240 mm. The main water-saving effect of the optimized irrigation schedule is that the yield, the soil water use rate, and the water use productivity increased, while the irrigation amount and the ineffective seepage decreased.
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Taha, Dalia H. Y., Huseyin Haci, and Ali Serener. "Novel Channel/QoS Aware Downlink Scheduler for Next-Generation Cellular Networks." Electronics 11, no. 18 (September 13, 2022): 2895. http://dx.doi.org/10.3390/electronics11182895.
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Downlink schedulers play a vital part in the current and next-generation wireless networks. The next generation downlink scheduler should satisfy the demand for different requirements, such as dealing with ultra-dense networks and the need to run real-time (RT) and non-real-time (nRT) applications, with a high quality of service (QoS). Many researchers have developed various schedulers for these, but none have introduced one scheduler to target them all. This paper introduces a novel channel/QoS aware downlink scheduler algorithm, called Advanced Fair Throughput Optimized Scheduler (AFTOS), for ultra-dense networks. AFTOS is a multi-QoS scheduler that aims to maximize system spectrum efficiency and user throughput with enhanced fairness, delay, and packet loss ratio (PLR). It is capable of handling RT and nRT traffic. We developed two new policies, called Adjusted Largest Weighted Delay First (ALWDF) and Fair Throughput Optimized Scheduler (FTOS), for RT and nRT traffic. Then, we joint them to introduce our novel downlink scheduler Advanced Fair Throughput Optimized Scheduler (AFTOS). For evaluating the suggested algorithm, we undertook experiments to decide the ideal parameter value for the proposed approaches and compared the proposed solution to current best practices. The findings prove that the AFTOS algorithm can achieve its objectives, outperforming the alternative techniques.
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Zhu, Hongli, and Hong Zhou. "Single Machine Predictive Scheduling Using Inserted Idle Times." Journal of Applied Mathematics 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/304808.
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A single machine predictive scheduling problem is considered. The primary objective is to minimize the total completion times. The predictability of the schedule is measured by the completion time deviations between the predictive schedule and realized schedule. The surrogate measure of predictability is chosen to evaluate the completion time deviations. Both of the primary objective and predictability are optimized. In order to absorb the effects of disruptions, the predictive schedule is generated by inserting idle times. Right-shift rescheduling method is used as the rescheduling strategy. Three methods are designed to construct predictive schedules. The computational experiments show that these algorithms provide high predictability with minor sacrifices in shop performance.
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Huang, Yu-Li, Narges Shahraki, Erin M. Wallin, Eric W. Klavetter, and Kyle W. Klarich. "Provider time allotment tracking tool to effectively manage assignment commitments." Journal of Hospital Administration 10, no. 3 (May 11, 2021): 10. http://dx.doi.org/10.5430/jha.v10n3p10.
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Due to the rising demand with limited health service capacity, managing available resources effectively becomes an important task to reduce patient care delays and avoid unnecessary and costly capacity expansions. At the same time, staff satisfaction and/or burnout is a complementary consideration when designing optimal schedules. Deviation from the scheduled plan can cause delays in patient access and may lead to unsatisfaction among providers. Balancing demand management, staff satisfaction and generating optimized schedules quickly reveals the need for a tool that tracks provider time allotment over time, especially for the academic healthcare organization where providers are committed to multiple assignments, clinical and non-clinical. This tracking tool should allow management to proactively adjust allotment to unplanned changes in the schedule and increase participation. In this study, a tool is developed to track monthly provider assignments for the Department of Cardiovascular Medicine at Mayo Clinic, Rochester. The proposed tool produces two key outputs for each provider and assignment: 1) the recommended target workdays and 2) workday upper and lower bounds to accommodate for variability. This tracking tool is successfully implemented with implementation criteria, and the feedback is positive. The tool pulls the data systematically from the Mayo data platform and performs the necessary analysis on the data. It also automatically updates the values for the recommended target as well as upper and lower bounds for the remaining months in a year based on changes in the schedule so that provider commitment can be met at the end of year.
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Mamdouh, Maged, Mostafa Ezzat, and Hesham Hefny. "Optimized Planning of Resources Demand Curve in Ground Handling based on Machine Learning Prediction." International Journal of Intelligent Systems and Applications 13, no. 1 (February 8, 2021): 1–16. http://dx.doi.org/10.5815/ijisa.2021.01.01.
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Determining the resource requirements at airports especially in-ground services companies is essential to successful planning in the future, which is represented in the resources demand curve according to the future flight schedule, through which staff schedules are created at the airport to cover the workload with ensuring the highest possible quality service provided. Given in the presence of variety service level agreements used on flight service vary according to many flight features, the resources assumption method makes planning difficult. For instance, flight position is not included in future flight schedule but it's efficacious in the identification of flight resources. In this regard, based on machine learning, we propose a model for building a resource demand curve for future flight schedules. It is divided into two phases, the first is the use of machine learning to predict resources of the service level agreement required on future flight schedules, and the second is the use of implement a resource allocation algorithm to build a demand curve based on predicted resources. This proposal could be applicable to airports that will provide efficient and realistic for the resources demand curve to ensure the resource planning does not deviate from the real-time resource requirements. the model has proven good accuracy when using one day of flights to measuring deviation between the proposed model predict demand curve when flights did not include the location feature and the actual demand curve when flights include location.
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Her, Emily J., Annette Haworth, Yu Sun, Scott Williams, Hayley M. Reynolds, Angel Kennedy, and Martin A. Ebert. "Biologically Targeted Radiation Therapy: Incorporating Patient-Specific Hypoxia Data Derived from Quantitative Magnetic Resonance Imaging." Cancers 13, no. 19 (September 29, 2021): 4897. http://dx.doi.org/10.3390/cancers13194897.
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Purpose: Hypoxia has been linked to radioresistance. Strategies to safely dose escalate dominant intraprostatic lesions have shown promising results, but further dose escalation to overcome the effects of hypoxia require a novel approach to constrain the dose in normal tissue.to safe levels. In this study, we demonstrate a biologically targeted radiotherapy (BiRT) approach that can utilise multiparametric magnetic resonance imaging (mpMRI) to target hypoxia for favourable treatment outcomes. Methods: mpMRI-derived tumour biology maps, developed via a radiogenomics study, were used to generate individualised, hypoxia-targeting prostate IMRT plans using an ultra- hypofractionation schedule. The spatial distribution of mpMRI textural features associated with hypoxia-related genetic profiles was used as a surrogate of tumour hypoxia. The effectiveness of the proposed approach was assessed by quantifying the potential benefit of a general focal boost approach on tumour control probability, and also by comparing the dose to organs at risk (OARs) with hypoxia-guided focal dose escalation (DE) plans generated for five patients. Results: Applying an appropriately guided focal boost can greatly mitigate the impact of hypoxia. Statistically significant reductions in rectal and bladder dose were observed for hypoxia-targeting, biologically optimised plans compared to isoeffective focal DE plans. Conclusion: Results of this study suggest the use of mpMRI for voxel-level targeting of hypoxia, along with biological optimisation, can provide a mechanism for guiding focal DE that is considerably more efficient than application of a general, dose-based optimisation, focal boost.
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Nair, Vivek, Samuel Auger, Sara Kochanny, Frederick Howard, Daniel Ginat, Olga Pasternak-Wise, Aditya Juloori, et al. "Mathematical predication models to optimize post-treatment surveillance in HPV-associated oropharyngeal cancer." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 6027. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.6027.
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6027 Background: In this study we develop post-treatment imaging surveillance schedules for locally advanced oropharyngeal carcinoma (OPC) specific to the unique recurrence patterns of tumor stage and HPV status, using mathematical models. Current post-treatment imaging surveillance recommendations for OPC are not evidence based. The exception is the use of a positron emission tomography (PET) scan at 3 months post-treatment, after which practice across institutions diverge. An optimized and personalized surveillance schedule for OPC patients can minimize costs and diagnostic delays. Methods: A Markov multi-state model defining local and distant recurrences was trained using 2159 patients from the National Cancer Database. Patients from 2010-2015 treated at an academic or major cancer center with curative radiotherapy were included. Tumors must have been stage III to IVB (AJCC 7th edition) with known p16/HPV status. Model performance was then successfully externally validated using the 2016 International Collaboration on Oropharyngeal cancer Network for Staging (ICON-S) study. Optimized radiographic surveillance schedules were created using this model, assuming a PET at month 3 and including 0 to 6 additional computed tomography (CT) scans of the neck and chest. Optimization was done for minimization of latency, defined as time between disease recurrence and radiographic discovery. Results: Model-selected schedules varied significantly from commonly utilized-surveillance schedules (such as imaging every 3 months within the first year from treatment) and showed lower mean diagnostic latency for every stage and HPV status (shown in Table). In the lowest risk cohort (Stage III HPV+), the optimized schedule had a sensitivity of 65% and latency of 3.1 months. In the highest risk group (Stage IVB HPV-), the optimized schedule had a sensitivity of 76% and latency of 1.9 months. Conclusions: Mathematical model optimization for HPV status and stage is feasible and produces non-intuitive results. These results could be used to inform surveillance if payors reimburse for fewer total scans. Across all cohorts, each added CT scan increases surveillance sensitivity and decreases latency. Incorporation of physical exam and direct visualization results into the model are still needed. Future steps include cost effectiveness research and prospective clinical trials.[Table: see text]
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Dasović, Borna, and Uroš Klanšek. "Integration of Mixed-Integer Nonlinear Program and Project Management Tool to Support Sustainable Cost-Optimal Construction Scheduling." Sustainability 13, no. 21 (November 4, 2021): 12173. http://dx.doi.org/10.3390/su132112173.
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This paper presents the integration of mixed-integer nonlinear program (MINLP) and project management tool (PMT) to support sustainable cost-optimal construction scheduling. An integrated structure of a high-level system for exact optimization and PMT was created. To ensure data compatibility between the optimization system and PMT and to automate the process of obtaining a cost-optimal schedule, a data transformation tool (DTT) was developed within a spreadsheet application. The suggested system can determine: (i) an optimal project schedule with associated network diagram and Gantt chart in continuous or discrete time units; (ii) optimal critical and non-critical activities, including their early start, late start, early finish, late finish along with total and free slack times; and (iii) minimum total project cost along with the allocation of direct and indirect costs. The system provides functionalities such as: (i) MINLP can be updated, and schedules can be re-optimized; (ii) the optimal schedule can be saved as a baseline to track changes; (iii) different optimization algorithms can be engaged whereby switching between them does not require model changes; (iv) PMT can be used to track task completion in the optimized schedule; (v) calendar settings can be changed; and (vi) visual reports can be generated to support efficient project management. Results of cost-optimal project scheduling are given in a conventional PMT environment, which raises the possibility that the proposed system will be more widely used in practice. Integration of MINLP and PMT allows each software to be used for what it was initially designed. Their combination leads to additional information and features of optimized construction schedules that would be significantly more difficult to achieve if used separately. Application examples are given in the paper to show the advantages of the proposed approach.
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Zhao, Mengqi, Xiaoling Wang, Jia Yu, Lei Bi, Yao Xiao, and Jun Zhang. "Optimization of Construction Duration and Schedule Robustness Based on Hybrid Grey Wolf Optimizer with Sine Cosine Algorithm." Energies 13, no. 1 (January 2, 2020): 215. http://dx.doi.org/10.3390/en13010215.
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Construction duration and schedule robustness are of great importance to ensure efficient construction. However, the current literature has neglected the importance of schedule robustness. Relatively little attention has been paid to schedule robustness via deviation of an activity’s starting time, which does not consider schedule robustness via structural deviation caused by the logical relationships among activities. This leads to a possibility of deviation between the planned schedule and the actual situation. Thus, an optimization model of construction duration and schedule robustness is proposed to solve this problem. Firstly, duration and two robustness criteria including starting time deviation and structural deviation were selected as the optimization objectives. Secondly, critical chain method and starting time criticality (STC) method were adopted to allocate buffers to the schedule in order to generate alternative schedules for optimization. Thirdly, hybrid grey wolf optimizer with sine cosine algorithm (HGWOSCA) was proposed to solve the optimization model. The movement directions and speed of grey wolf optimizer (GWO) was improved by sine cosine algorithm (SCA) so that the algorithm’s performance of convergence, diversity, accuracy, and distribution improved. Finally, an underground power station in China was used for a case study, by which the applicability and advantages of the proposed model were proved.
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Perelstein, Elizabeth, Ariella Rose, Young-Chae Hong, Amy Cohn, and Micah T. Long. "Automation Improves Schedule Quality and Increases Scheduling Efficiency for Residents." Journal of Graduate Medical Education 8, no. 1 (February 1, 2016): 45–49. http://dx.doi.org/10.4300/jgme-d-15-00154.1.
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ABSTRACT Background Medical resident scheduling is difficult due to multiple rules, competing educational goals, and ever-evolving graduate medical education requirements. Despite this, schedules are typically created manually, consuming hours of work, producing schedules of varying quality, and yielding negative consequences for resident morale and learning. Objective To determine whether computerized decision support can improve the construction of residency schedules, saving time and improving schedule quality. Methods The Optimized Residency Scheduling Assistant was designed by a team from the University of Michigan Department of Industrial and Operations Engineering. It was implemented in the C.S. Mott Children's Hospital Pediatric Emergency Department in the 2012–2013 academic year. The 4 metrics of schedule quality that were compared between the 2010–2011 and 2012–2013 academic years were the incidence of challenging shift transitions, the incidence of shifts following continuity clinics, the total shift inequity, and the night shift inequity. Results All scheduling rules were successfully incorporated. Average schedule creation time fell from 22 to 28 hours to 4 to 6 hours per month, and 3 of 4 metrics of schedule quality significantly improved. For the implementation year, the incidence of challenging shift transitions decreased from 83 to 14 (P < .01); the incidence of postclinic shifts decreased from 72 to 32 (P < .01); and the SD of night shifts dropped by 55.6% (P < .01). Conclusions This automated shift scheduling system improves the current manual scheduling process, reducing time spent and improving schedule quality. Embracing such automated tools can benefit residency programs with shift-based scheduling needs.
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Labidi, M., M. Mrad, A. Gharbi, and M. A. Louly. "Scheduling IT Staff at a Bank: A Mathematical Programming Approach." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/768374.
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We address a real-world optimization problem: the scheduling of a Bank Information Technologies (IT) staff. This problem can be defined as the process of constructing optimized work schedules for staff. In a general sense, it requires the allocation of suitably qualified staff to specific shifts to meet the demands for services of an organization while observing workplace regulations and attempting to satisfy individual work preferences. A monthly shift schedule is prepared to determine the shift duties of each staff considering shift coverage requirements, seniority-based workload rules, and staff work preferences. Due to the large number of conflicting constraints, a multiobjective programming model has been proposed to automate the schedule generation process. The suggested mathematical model has been implemented using Lingo software. The results indicate that high quality solutions can be obtained within a few seconds compared to the manually prepared schedules.
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Ünsal, Öner, Türker Dündar, Hızır Volkan Görgün, Alperen Kaymakci, Süleyman Korkut, and Nusret As. "Optimizing lumber drying schedules for Oriental beech and sessile oak using acoustic emission." BioResources 15, no. 3 (June 17, 2020): 6012–22. http://dx.doi.org/10.15376/biores.15.3.6012-6022.
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The aim of this work was to detect sounds providing evidence of the creation of drying defects and to correlate such data with drying quality. A further goal was to establish sound wave thresholds of ideal drying through the drying process by using an acoustic emission (AE) monitoring method. Thus, it is projected to decrease long drying times and also drying costs by reaching to ideal drying schedules. In this study, commercially preferred sessile oak and oriental beech structural lumbers were dried with three different schedules in a conventional kiln. The lumbers were “listened to” with AE sensors while drying according to the first two schedules, which were called protective and severe, respectively. AE events of the drying experiments were compared with ambient conditions and drying classes according to the standard of European Drying Group. The third drying schedule was optimized based on the AE peaks and applied. The results showed that ideal drying times were reduced up to 19% relative to the protective drying schedule, while obtaining the same drying quality for both species.
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Le Page, Y. "Optimized Schedule for Large Crystallography Meetings." Journal of Applied Crystallography 29, no. 3 (June 1, 1996): 291–95. http://dx.doi.org/10.1107/s0021889896000647.
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International crystallography meetings like the International Union of Crystallography, European Crystallographic Meetings and American Crystallographic Association ones currently offer very comprehensive programs with four parallel sessions or more over a week or so. Proper scheduling of these sessions is nearly as important for the scientific success of these meetings as the selection of topics. In order to tackle this problem objectively, it is proposed here to poll crystallographers about the sessions they most wish to attend, and to accept that schedule which minimizes the sum of residual conflicts calculated from the results of the poll and also respects a number of constraints. A novel semi-automated optimization in four steps, based on the existence of major interest profiles among participants, was applied to the scheduling of the American Crystallographic Association 1995 Annual Meeting: a simple rearrangement of a `conflict matrix' to produce clusters of sessions with large conflicts, followed by application of constraints within clusters in step 2, then a small number of permutations within clusters only, each of them lowering the sum of conflicts while respecting constraints, and finally assignment of sessions to rooms. Without necessarily producing the absolute best solution, solutions very superior to those from intuition and respecting constraints can be built in this way with limited computing power. The experimental data were taken from 102 replies to an e-mail poll of crystallographers at large. Only 20 permutations were performed in step 3. The solution respected all constraints but one, which was relaxed in order to avoid a bad conflict. At the meeting, the room assignments turned out to be mostly verified, with just a few surprises, all having to do with outstanding contributed talks at sessions with no invited speakers. The total attendance at sessions remained high for the whole duration of the meeting. Calculations were programmed in Fortran and performed on a 386 PC clone.
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Cheng, Hongbo, Yufan Cao, Jiaxin Wang, Wei Zhang, and Han Zeng. "A preventive, opportunistic maintenance strategy for the catenary system of high-speed railways based on reliability." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 10 (November 4, 2019): 1149–55. http://dx.doi.org/10.1177/0954409719884215.
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The catenary is a vital component of the electrified railway system. It consists of many parts which are interrelated; the maintenance schedule of the catenary system should consider the influence of the interrelationship. In this study, a preventive, opportunistic maintenance method is proposed to schedule the maintenance process of the catenary system. First, the reliability of the key parts of the catenary is modeled using Weibull distribution. Second, a reliability margin is proposed to expand the maintenance time from point to interval, and the reliability margin is optimized to minimize the maintenance cost. Then, a preventive opportunistic maintenance schedule can be arranged on the basis of the optimal reliability margin. Case study results verify that the proposed preventive opportunistic maintenance method can reduce the number of maintenance schedules and can effectively save the maintenance cost.
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WATANABE, Shoichiro, Takuya MORI, Takeru MIURA, and Takafumi KOSEKI. "1E22 Energy-Saving Schedule Design by Installing Optimized Rapid Service in DC-Electric Railways(Environment and Energy)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _1E22–1_—_1E22–12_. http://dx.doi.org/10.1299/jsmestech.2015._1e22-1_.
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Chang, Sze-Wei, and Paul Schonfeld. "Optimized Schedules for Airline Routes." Journal of Transportation Engineering 130, no. 4 (July 2004): 412–18. http://dx.doi.org/10.1061/(asce)0733-947x(2004)130:4(412).
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Bendib, Sonia Sabrina, Hamoudi Kalla, Salim Kalla, and Riadh Hocine. "A Self-Organized Scheduling Algorithm for Embedded Real-Time Systems." International Journal of Embedded and Real-Time Communication Systems 12, no. 2 (April 2021): 57–73. http://dx.doi.org/10.4018/ijertcs.2021040104.
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In this paper, the authors present a self-organized approach for scheduling tasks on processors in embedded real-time systems. For such a mapping, two conflicting objectives have to be optimized: the reliability and the schedule length. Since the scheduling problem is NP-hard, a heuristic algorithm is used to produce schedules achieving different trade-offs between the two objectives. Moreover, a self-organization strategy based on dynamic crowding distance is adopted. This allows a better exploration of the objective space as well as an enhanced solution diversity. The proposed algorithm, reliability schedule length trad-offs algorithm (RSTA), is tested and compared with the popular SPEA2 algorithm where experimental results are promising on both quality and diversity of solutions.
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Ahssan, Md Ragib, Mehran Ektesabi, and Saman Gorji. "Gear Ratio Optimization along with a Novel Gearshift Scheduling Strategy for a Two-Speed Transmission System in Electric Vehicle." Energies 13, no. 19 (September 28, 2020): 5073. http://dx.doi.org/10.3390/en13195073.
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A novel gearshift scheduling strategy has been framed for a two-speed transmission system in electric vehicles that can save energy during hilly driving and frequently changing driving conditions through efficient electric motor operation. Unlike the traditional approach, the proposed gearshift strategy is based on the preferred vehicle speed range, vehicle acceleration, and road grade to ensure desired vehicle performances with minimum energy consumption. Meanwhile, the vehicle speed range is chosen around the electric motor rated speed, and two gearshift schedules in relation to vehicle acceleration and road grade are developed based on the motor torque generating capacity and efficiency. Appropriate gear is selected through a combined assessment of the required vehicle speed, acceleration, and road grade information. A guideline is developed and explained for the primary gearshift schedule. Next, the gear ratios and gearshift schedules are optimized combinedly in a Simulink environment using the gradient descent method and pattern search method on three driving cycles separately. Depending on the driving scenarios, around 4% to 7.5% energy saving has been experienced through optimization, while the gear ratios and gearshift schedule in relation to the road grade are found to be major contributors to the vehicle economic driving compared to that with the gearshift schedule for vehicle acceleration.
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Wu, Fei, Paul M. Schonfeld, and Myungseob Kim. "Optimized Restoration Schedule for Disrupted Railroad Network." Journal of Transportation Engineering, Part A: Systems 147, no. 9 (September 2021): 04021053. http://dx.doi.org/10.1061/jtepbs.0000560.
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