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733 results for “Scheduling”
A set of positive examples consisting of short-term schedules for testing the acquisition of MiniZinc scheduling models
<p>To test the robustness of schedule model acquisition in a variety of situations, we generated 48,000 instances of schedules with variations in the following five dimensions:</p> <p>1. task description, 2. temporal constraints, 3. resource constraints, 4. the introduction or absence of noisy columns, and 5. the number of tasks and resources in a schedule.</p> <p>The file 'schedule_robustness_dimensions.pl' contains the list of constraints used to generate each table which, thus, need to be acquired.</p> <p>The dimensions are:</p> <p>1. Different ways to describe a task:<br> a. only the start time and duration columns are part of the table, the task duration is a pre-assigned input parameter,<br> b. only the start time and end time columns are part of the table, the task duration is a pre-assigned input parameter,<br> c. only the duration and end time columns are part of the table, the task duration is a pre-assigned input parameter,<br> d. all three columns are present in the table, the task duration is a pre-assigned input parameter,<br> e. the start time, duration and end time columns are all part of the table, and the task duration is calculated using a formula,<br> f. only the start time and end time columns are part of the table, and the task duration is calculated using a formula,<br> g. only the duration and end time columns are part of the table, and the task duration is calculated using a formula,<br> h. all three columns are present in the table, and the task duration is calculated using a formula.</p> <p>2. Different ways of expressing temporal constraints between task $i$ and its successor $j$<br> a. no temporal constraints at all,<br> b. $\textit{start}_i + \textit{cst} \leq \textit{start}_j$,<br> c. $\textit{start}_i + \textit{cst} \geq \textit{start}_j$,<br> d. $\textit{start}_i + \textit{cst}_1 \leq \textit{start}_j$, $\textit{start}_i + \textit{cst}_2 \geq \textit{start}_j$ ($\textit{cst}_2 \neq \textit{cst}_1$),<br> e. $\textit{start}_i + \textit{cst} = \textit{start}_j$,<br> f. $\textit{start}_i + \textit{cst} \leq \textit{end\_time}_j$,<br> g. $\textit{start}_i + \textit{cst} \geq \textit{end\_time}_j$,<br> h. $\textit{start}_i + \textit{cst}_1 \leq \textit{end\_time}_j$, $\textit{start}_i + \textit{cst}_2 \geq \textit{end\_time}_j$ ($\textit{cst}_2 \neq \textit{cst}_1$),<br> i. $\textit{start}_i + \textit{cst} = \textit{end\_time}_j$,<br> j. $\textit{end\_time}_i + \textit{cst} \leq \textit{start}_j$,<br> k. $\textit{end\_time}_i + \textit{cst} \geq \textit{start}_j$,<br> l. $\textit{end\_time}_i + \textit{cst}_1 \leq \textit{start}_j$, $\textit{end\_time}_i + \textit{cst}_2 \geq \textit{start}_j$ ($\textit{cst}_2 \neq \textit{cst}_1$),<br> m. $\textit{end\_time}_i + \textit{cst} = \textit{start}_j$,<br> n. $\textit{end\_time}_i + \textit{cst} \leq \textit{end\_time}_j$,<br> o. $\textit{end\_time}_i + \textit{cst} \geq \textit{end\_time}_j$,<br> p. $\textit{end\_time}_i + \textit{cst}_1 \leq \textit{end\_time}_j$, $\textit{end\_time}_i+\textit{cst}_2\geq\textit{end\_time}_j$ ($\textit{cst}_2\neq\textit{cst}_1$),<br> q. $\textit{end\_time}_i + \textit{cst} = \textit{end\_time}_j$.<br> Note that we only generate temporal constraints that mention the start time, i.e. 2b–2m, if the start time attribute is part of the table, i.e. not in the cases 1c or 1g.</p> <p>3. Different ways of expressing resource scheduling constraints:<br> a. no scheduling constraints at all,<br> b. a DISJUNCTIVE constraint for each subset of tasks using the same resource,<br> c. a DIFFN constraint on all tasks, so that there is no overlap between tasks that will be assigned to the same resource,<br> d. a SHIFT constraint that forces the start and end times of each task to be within the same availability period, with no gap between two consecutive availability periods,<br> e. a CALENDAR constraint that forces the start and end time of each task assigned to a given resource $r$ to fall within the same period of availability of the resource $r$,<br> f. a set of DISJUNCTIVE constraints and a SHIFT constraint,<br> g. a DIFFN and a SHIFT constraint,<br> h. a set of DISJUNCTIVE constraints and a CALENDAR constraint,<br> i. a DIFFN and a CALENDAR constraint.<br> The combination of certain temporal and resource constraints may lead to infeasibility. For instance, in a temporal constraint of type 2c, the two corresponding tasks may overlap, which is incompatible with a DISJUNCTIVE constraint between these tasks, i.e. a constraint of type 3b. Therefore, we do not generate scheduling instances that mix the dimensions 2c, 2d, 2e, 2g, 2h, 2i, 2o, 2p, and 2q, with the dimensions 3b, 3c, 3f, 3g, 3h, and 3i. Note that the number of resources generated varies according to the number of tasks, as explained in Item 5.</p> <p>4. Creating noisy columns or not:<br> a. no additional noisy columns,<br> b. three extra columns with random values standing for noise.</p> <p>5. Number of tasks and resources referenced by the schedule:<br> a. 10 tasks and 2 resources,<br> b. 100 tasks and 10 resources,<br> c. 1,000 tasks and 20 resources,<br> d. 10,000 tasks and 100 resources.</p> <p><br> For each valid combination of dimensions, we generated 10 instances of schedules.</p>
SCHEDULE Follow Up Visit 5-7 yr
ClinicalTrials.gov study NCT02864706. IPD Sharing: UNDECIDED. Countries: 3. Publications: 1.
Study of a Booster Dose of a Tetravalent Dengue Vaccine in Subjects Who Previously Completed the 3-dose Schedule
ClinicalTrials.gov study NCT02623725. IPD Sharing: YES. Countries: 5. Publications: 1.
Immunogenicity and Safety of Different Vaccination Schedules of Tetravalent Dengue Vaccine in Healthy Subjects 9 to 50 Years of Age
ClinicalTrials.gov study NCT02628444. IPD Sharing: YES. Countries: 2. Publications: 2.
Immune Response to Different Schedules of a Tetravalent Dengue Vaccine Given With or Without Yellow Fever Vaccine
ClinicalTrials.gov study NCT01488890. IPD Sharing: YES. Countries: 1. Publications: 1.
Immunogenicity and Safety of a Tetravalent Dengue Vaccine Booster Injection in Subjects Who Previously Completed a 3-dose Schedule
ClinicalTrials.gov study NCT02824198. IPD Sharing: YES. Countries: 1. Publications: 2.
Data and code from: Cost-effectiveness Analysis of Alternative Infant and Neonatal Rotavirus Vaccination Schedules in Malawi
Open the record for dataset details and reuse information.
Data from: Dinosaurian survivorship schedules revisited: new insights from an age-structured population model
Open the record for dataset details and reuse information.
Promotional Scheduling Software Price For Supermarkets
<p>Supermarket chains and independent grocers use Demo Wizard <a href="http://https//www.demo-wizard.com/pricing.html">promotional Scheduling Software</a> Price to maximize utilization of their floor space for in store demos and improve their customer experience.</p>
Dataset of alternative process plan networks for dynamic integrated process planning and scheduling
<p>This dataset includes 3D models of representative manufacturing parts (jobs) as well as their features. For each of the manufacturing part, alternative process plan networks are given containing alternative machine tools, cutting tools, Tool Access Directions (TADs) and manufacturing times. The dataset also includes a detailed technical specification for all parts and calculated manufacturing times for all operations based on given alternative manufacturing resources.</p>
Scheduling Mechanisms to Control Spread of Covid-19 (Simulation Results)
<p><span>We study scheduling mechanisms that explore the trade-off between containing the spread of COVID-19 and performing in-person activity in organizations. </span><span>Our mechanisms, referred to as<span> </span></span><i>group scheduling</i><span>, are based on partitioning the population<span> </span></span><i>randomly</i><span><span> </span>into groups and scheduling each group on appropriate days with possible gaps (when no one is working and all are quarantined). Each group interacts with no other group and, importantly, any person who is symptomatic in a group is quarantined.</span><br> <br> <span>We show that our mechanisms effectively trade-off in-person activity for more effective control of the COVID-19 virus spread. In particular, we show that a mechanism which partitions the population into two groups that alternatively work in-person for five days each, flatlines the number of COVID-19 cases quite effectively, while still maintaining in-person activity at 70% of pre-COVID-19 level. Other mechanisms that partitions into two groups with less continuous work days or more spacing or three groups achieve even more aggressive control of the virus at the cost of a somewhat lower in-person activity (about 50%). We demonstrate the efficacy of our mechanisms by theoretical analysis and extensive experimental simulations on various epidemiological models based on real-world data.</span></p>
Scheduling success ratios and computational cost of the mechanism
<p>The .xls spreadsheet contains scheduling success ratios and computational cost of the mechanism related to the publication "Seeking Time-Composable Partitions of Tasks for COTS Multicore Processors".</p>
Raw data of the experiments in "Examining the Reproducibility of Using Dynamic Loop Scheduling Techniques in Scientific Applications" (REPPAR workshop at IPDPS 2017)
<p>Raw data of the experiments in "Examining the Reproducibility of Using Dynamic Loop Scheduling Techniques in Scientific Applications" (REPPAR workshop at IPDPS 2017)</p>
Raw data of the experiments in "Towards the Reproducibility of Using Dynamic Loop Scheduling Techniques in Scientific Applications" (ISPDC 2017)
<p>Raw data of the experiments in "Towards the Reproducibility of Using Dynamic Loop Scheduling Techniques in Scientific Applications" (ISPDC 2017)</p>
Parmalat Scheduling Production Requests
<div> <div> <div> <p>The dataset provided in this repository is an anonymized collection of Scheduling Production Requests data, gathered by Parmalat from October to December 2023. Each entry in the dataset is a JSON object with the following keys: <code>prodQty</code> (production quantity), <code>prodPriority</code> (production priority), <code>prodArea</code> (anonymized production area), <code>lastChangeAccepted</code> (timestamp of the last accepted change), <code>prodUM</code> (anonymized unit of measure of quantity), and <code>itemFormat</code> (anonymized item format). The dataset provides valuable insights into the production scheduling process, while ensuring the confidentiality of sensitive information through anonymization performed by the knowlEdge Data Quality Assurance component. The data has been originally collected by the knowlEdge Data Collection Platform and stored within the knowlEdge Historical Data Storage. This dataset can be a valuable resource for researchers and analysts studying production scheduling patterns and strategies. Please note that all identifiers in the dataset have been anonymized for privacy reasons.</p> </div> </div> </div>
Parameterized Task Graph Scheduling Algorithm for Comparing Algorithmic Components - All Figures and Datasets
<p>Figures and Datasets for "Parameterized Task Graph Scheduling Algorithm for Comparing Algorithmic Components"</p> <ul> <li>dataset.zip: All datasets evaluated</li> <li>figures.zip: All plots (1080) for all results for our paper</li> </ul>
Dataset of the paper "A Mixed-Criticality Approach to Fault Tolerance: Integrating Schedulability and Failure Requirements"
<p>Dataset for the paper "A Mixed-Criticality Approach to Fault Tolerance: Integrating Schedulability and Failure Requirements" published in RTAS'22 conference</p>
Instances and solutions for the multi-depot electric vehicle scheduling problem with the objective of minimizing the fleet size (EVSP-MD-FS)
<p>The set of instances and corresponding solutions, which were used in the computational study of the paper “Multi-depot electric vehicle scheduling in in-plant production logistics considering non-linear charging models”.</p>
A scheduler log replayer and logs for BFTrainer evaluation
<p>This is a temporary repository to host source for paper: <code>BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes.</code></p> <p>Files</p> <ul> <li><code>BFTrainer-replay.py</code> the main program to replay real scheduler to evaluate our resource allocation algorithm</li> <li><code>jobs.py</code> implements functions to manage jobs.</li> <li><code>progCBC.py</code> or <code>progGRB.py</code> the implementation of mixed Integer linear programming using Gurobi optimizer (progGRB.py). We also open source our implementation (progGRB.py) using free optimizer (e.g., CBC, Pulp and JuMP). You can get an Trial Licenses or Free Academic Licenses from Gurobi if you want to run the current version. You need to adjust the import source in the <code>BFTrainer-replay.py</code> to use the CBC based solver.</li> <li><code>trace.py</code> has functions to manage scheduler logs for the replay evaluation.</li> </ul>
Data and code to explore annual cycle schedule adjustments in a long distance migrant
<p>Matching the timing of annual cycle events with the required resources can have crucial consequences for individual fitness. But as the annual cycle is comprised of sequential events, a delay at any point may be carried over to the subsequent stage (or more, in a domino effect) and negatively influence individual performance. To investigate how migratory animals navigate their annual schedule, and where and when it may be adjusted, we used full annual cycle data of 38 Icelandic whimbrels <em>Numenius phaeopus islandicus</em> tracked over 7 years – a subspecies that typically performs long-distance migrations to West Africa. We found that individuals apparently used the wintering sites to compensate for delays that mostly arose due to previous successful breeding, and a domino effect was observed from spring departure to laying date, with the potential to affect breeding output. However, the total time saved during all stationary periods is apparently enough to avoid interannual effects between breeding seasons. These findings highlight the importance of preserving good quality non-breeding sites in which individuals may adjust annual schedules and avoid potentially adverse effects of arriving late at the breeding grounds.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.