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733 results for “Scheduling”
Production line dataset for task scheduling and energy optimization - Demand Response Participation
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> it was simulated an announcement of a demand response program at period 757, describing a demand response event from period 937 (Friday at 21:00h) to 960 (Friday at 23:00h) , where each period represents five minutes. The demand response program imposed a limit consumption, during its event, of 2.5 kWh. The announcement of the demand response allowed the use of the proposed solution to limit the energy consumption. For that, the algorithm described in section 3.3 was executed at period 769 (Friday at 7:00h).</p> <p>The API can be found at <<a href="http://www.gecad.isep.ipp.pt/api/spear/%3E">http://www.gecad.isep.ipp.pt/api/spear/</a>></p> <p>File Description:</p> <ul> <li>Input_JSON_Demand_Response_Optimization - JSON input data for the demand response participation</li> <li>Output_JSON_Demand_Response_Optimization - JSON output data for the demand response participation</li> <li>Output_Statistics_Demand_Response_Optimization - Excel output demand response participation statistics</li> <li>Comparison_Output_Statistics_Demand_Response - Excel output statistics comparing the before and after the demand response participation</li> </ul>
Production line dataset for task scheduling and energy optimization - Schedule Optimization
<p>The case study of this dataset uses real production data, provided by a textile company that manufactures hang tags. Their working schedule is from 7h00 of Monday to 23h00 of Saturday. This dataset uses a period of 5 minutes for all task durations and energy data. The case study considers a six-day period from 7h00 of Monday to 23h00 of Saturday. The scheduling algorithm was used for three machines that share the same cell.<br> <br> The API can be found at <http://www.gecad.isep.ipp.pt/api/spear/><br> <br> File Description:</p> <ul> <li>Input_JSON_Schedule_Optimization - JSON input data for the schedule optimization</li> <li>Output_JSON_Schedule_Optimization - JSON output data for the schedule optimization</li> <li>Output_Statistics_Schedule_Optimization - Excel output schedule optimization statistics</li> </ul>
Maximum Independent Set Satellite Scheduling World Cities Data Set
<h1>Satellite Scheduling World Cities Data Set</h1> <p>The Satellite Scheduling World Cities Data Set is the a set of cities treated as point locations used to simulate a set of image collection tasking requests for AIAA paper "A Maximum Independent Set Method for Scheduling Earth-Observing Satellite Constellations". It provides an open reference and benchmark for the satellite task scheduling problem. This could also be considered as a sparse Maximum Independent Set problem for a generic graph. The requests represent point collects, from which we can compute multiple distinct collection opportunities. The tasking problem is then to select a subset of these collects that it is possible for the spacecraft to feasibly collect in a given time period, subject to constraints on the spacecraft's agility and constraints on only collecting a single collect per request (no duplication of effort).<br><br>The data set is hosted on both <a href="https://github.com/duncaneddy/aiaa-mis-satellite-scheduling-dataset">Github</a> and <a href="../">Zenodo</a>. The Github repository contains the original source data, the associated requests generated from the source data, and scripts to reproduce the scenario files. Zenodo (DOI 10.5281/zenodo) hosts copies of the output Metis graph files and collect data files. Due to the large size of produced files these are not included in the Github repository.</p> <h2>Notes</h2> <p><strong>Notes</strong><br><br>Please note that while the source data and generation methods are identical to the satellite task planning paper it was created for. The specific generated problems do not exactly reproduce the scenario in the paper. Since the original reproduction, updates in upstream software dependencies have changed the output of the generation process (specifically, Earth orientaiton parameter handling libraries). This can be determined by considering the cardinality of the generated collect set. However, these differences are generally small and since the constriant rate is similar, the results should be comparable.</p> <table> <tbody> <tr> <td>Spacecraft Count</td> <td>Orignial Publication Collect Count</td> <td>Reproduction Collect Count</td> </tr> <tr> <td>4</td> <td>59356</td> <td>59624</td> </tr> <tr> <td>6</td> <td>90777</td> <td>91204</td> </tr> <tr> <td>12</td> <td>180008</td> <td>180939</td> </tr> <tr> <td>24</td> <td>359170</td> <td>361519</td> </tr> </tbody> </table> <p><br>This repository also adds additional scenarios for 1, 2, and 36 satellites. Note, the provided scenarios represent the largest 10,000 request data set. Should a smaller request set be desired, the requests should be filtered to the top `x` request based on city population and any collects not associated with those requests should be discarded.</p> <p>Note the Zenodo repository excludes the collect and graph files for the 1 and 2 satellite scenarios to avoid the file limits. These can still be reproduced from the Github source code.</p> <h2>Acknolwedgement</h2> <p>If this data set is used in your research, please cite the following paper</p> <p><a href="https://arc.aiaa.org/doi/abs/10.2514/1.A34931">A Maximum Independent Set Method for Scheduling Earth-Observing Satellite Constellations</a></p> <blockquote> <pre><code>@article{eddy2021maximum, title={A Maximum Independent Set Method for Scheduling Earth-Observing Satellite Constellations}, author={Eddy, Duncan and Kochenderfer, Mykel J}, journal={Journal of Spacecraft and Rockets}, volume={58}, number={5}, pages={1416--1429}, year={2021}, publisher={American Institute of Aeronautics and Astronautics} }</code></pre> </blockquote> <h2>Licensing</h2> <p>The source of the world cities data is from the <a href="https://simplemaps.com/data/world-cities">simplemaps.com</a> website,<br>licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License </a>with the specific license found at `./data/worldcities_license.txt`.</p>
Datasets of synthetic workflows for evaluating a multi-objective and multi-constrained scheduling approach for cyber-physical applications
<p>These datasets of synthetic workflows (task graphs) were generated to evaluate the performance and scalability of a multi-objective and multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) based on the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors with different processing core failure rates and varied sensing/actuating or other specialized capabilities. Our objectives were the minimization of the overall latency, the minimization of the overall energy consumption, and the maximization of the overall reliability of the workflow application in the specific CPS, under deadline, reliability, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random task graphs with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability, reliability threshold) were included post-generation, using appropriate values. More details are provided in README.txt.<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.<br>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p>
EV Charging Schedules (V1G, V2G)
<p>This dataset is related to the application of different EV charging policies by a smart charging software application. The dataset consists of the following three files: charging_requests.xlsx, dynamic_prices.csv, and charging_results.xlsx.</p> <p>charging_requests: This is an input file, which contains characteristics of five EV charging sessions, related to arrival time, session duration and EV battery capacity and requested energy.</p> <p>dynamic_prices: This is an input file, which contains simulated dynamic electricity prices for a specific day. Data are used for calculating cost and applying cost-based policies.</p> <p>charging_results: This is the output file that contains the derived 15-min interval schedules for the five sessions after applying four different policies, namely, cost-optimal, time-optimal, multi-objective, and V2G. The time series consists of the amount of power (Watt) which is delivered to the EVs or provided by the EVs, at each timestamp.</p>
Data and R script for 'Evaluating the cyclic ratio schedule as an assay of feeding behaviour in the European starling (Sturnus vulgaris)'
<p>Data files and R script for Dunn et al. "Evaluating the cyclic ratio schedule as an assay of feeding behaviour in the European starling (<em>Sturnus vulgaris</em>)"</p> <p>Includes a single R script that produces all the analyses in the paper. The script makes use of three different .csv data files.</p>
A Systematic Survey of Datacenter Scheduling: Data Artifacts
<p>This release contains the raw search results of the survey conducted in the paper <em>A Systematic Survey of Datacenter Scheduling</em>. </p>
Improved upper bounds for permutation flowshop scheduling benchmarks (Taillard and VRF)
<p>Optimal makespans and permutation schedules (found and proven optimal by Branch-and-Bound) for Taillard instances Ta112, Ta116 (500 jobs, 20 machines) and 74 instances of the VRF benchmark.</p>
Case studies related to the manuscript Tuning Trains Speed in Railway Scheduling
<p>This dataset is dedicated to the case studies related to the manuscript <strong>Tuning Trains Speed in Railway Scheduling</strong> by Étienne André, published in the proceedings of the 25th International Conference on Formal Engineering Methods (ICFEM 2024).</p> <p>See README.md for more information.</p>
Synthetic multi-day activity-travel schedules for Swedish residents
<div> <h2><strong>About </strong></h2> <p>This dataset contains multi-day activity-travel schedules for <strong>over 263,000 individuals residing in Sweden</strong>, representing approximately <strong>2.6% of country's population</strong>. The individuals and their daily schedules are derived from mobile phone application data covering seven months in 2019. Mobile phone application data, one example of emerging mobility data sources, offers an alternative to other data collection methods. This data is collected by capturing phone users' geographical locations with their consent as they interact with various mobile applications. </p> </div> <div> <p>This open data repository includes activity-travel schedules for each individual <strong>over five simulated average weekdays</strong>, <strong>incorporating daily variability at the individual level</strong>. <strong>Each simulation day provides:</strong> </p> </div> <div> <ul> <li> <p><strong>Anonymized Identifiers:</strong> Unique IDs that link individuals across all simulation days. </p> </li> </ul> </div> <div> <ul> <li> <p><strong>Activity Locations:</strong> Locations for home, work/school and other activities. </p> </li> </ul> </div> <div> <ul> <li> <p><strong>Daily Activity-Travel Schedules:</strong> Detailed information on activity sequence, type, start and end times, and locations. </p> </li> </ul> <p> </p> <div> <h2><strong>Background </strong></h2> </div> <div> <p>The activity-travel schedules were created using a novel generative model that synthesizes individuals' average weekday activity-travel schedules from mobile phone application data. Mobile data provides geographically and population-wise extensive observations over extended periods, offering valuable insights into individuals' whereabouts. However, these datasets often include sampling biases in the population coverage and individual-level data sparsity due to intermittent and irregular phone application activities, from which the underlying geolocation data were passively collected. </p> </div> <div> <p>The generative model combines mobile data with the Swedish national travel survey [1]. The model employs state-of-the-art primary activity identification methods to infer individuals’ primary activity locations, i.e., home and work/school snapped to buildings. The proposed model can generate multiple schedules for each individual, showing activity sequences, types, start/end times and locations, incorporating daily variability in specific schedule attributes. At the individual level, variations occur across all elements of activity schedules, i.e., activity sequences, type, start/end times, and other activity locations, while maintaining the residential and workplace locations. Moreover, the model calculates a weight for each individual based on their residential location and inferred employment status, addressing sampling biases and ensuring a representative sample of the Swedish population. </p> </div> <div> <p>The performance of the generative model is evaluated by comparing its synthesized activity-travel schedules with those from <a href="https://www.sciencedirect.com/science/article/pii/S2352340923003281?via%3Dihub" target="_blank" rel="noopener">the SySMo model</a> [2], large-scale agent-based model of Sweden and with underlying travel survey data. The results demonstrate that the proposed model effectively addresses biases and sparsity in mobile phone application data, resulting in realistic and reliable activity-travel schedules. The pre-print paper "<a href="https://arxiv.org/abs/2410.22386" target="_blank" rel="noopener">Mobile Phone Application Data for Activity Plan Generation</a>" details the model's methodology and evaluation.<br><br></p> <div> <h2><strong>Data Description </strong></h2> </div> <div> <p>The current data covers 5 data files, each showing a simulation day. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>PId </p> </div> </div> </td> <td> <div> <div> <p>Unique Anonymized Identifiers </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>employment </p> </div> </div> </td> <td> <div> <div> <p>Employment Status (0 = Not Employed, 1 = Employed) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>weight </p> </div> </div> </td> <td> <div> <div> <p>Weight showing the representativeness of the individuals </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>act_id </p> </div> </div> </td> <td> <div> <div> <p>Activity index of each agent </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>act_purpose </p> </div> </div> </td> <td> <div> <div> <p>Activity purpose (work/ home/ other) </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>act_start </p> </div> </div> </td> <td> <div> <div> <p>Start time of activity in minute (0-1439) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>minute </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>act_end </p> </div> </div> </td> <td> <div> <div> <p>End time of activity in minute (0-1439) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>minute </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>point_x </p> </div> </div> </td> <td> <div> <div> <p>Coordinate X of activity location (SWEREF99TM) </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>meter </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>point_y </p> </div> </div> </td> <td> <div> <div> <p>Coordinate Y of activity location (SWEREF99TM) </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>meter </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>point_lat </p> </div> </div> </td> <td> <div> <div> <p>Latitude of activity location (WGS 84) </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>degrees </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>point_lng </p> </div> </div> </td> <td> <div> <div> <p>Longitude of activity location (WGS 84) </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>degrees </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <h2> </h2> <h2><strong>Privacy Policy </strong></h2> </div> <div> <p>The data underlying this study were purchased from PickWell and are subject to restrictions due to licensing and privacy considerations under the European General Data Protection Regulation (GDPR). Therefore, these data are not publicly available but can be requested for research purposes through commercial access. We adhere to the guidelines established by the Chalmers Institutional Review Board (IRB) following the Swedish Ethical Review Act (2003:460) and GDPR 2016/679. The dataset contains no personal information traceable to individuals. Geolocations in this dataset are synthesized from empirical mobile application data, ensuring privacy while retaining their utility for studying mobility behavior and simulating large-scale travel demand. </p> </div> <div> <p> </p> <h2><strong>Acknowledgement</strong> </h2> </div> <div> <p>This research is funded by the Swedish Research Council Formas (Project Number 2018-01768). The authors acknowledge Sonia Yeh for her intellectual contributions to the study. Additionally, the authors sincerely thank Jorge Gil for providing the mobile phone application data. </p> <div> </div> </div> <p> </p> </div> </div>
Data for "Quantum combinatorial optimization beyond the variational paradigm: simple schedules for hard problems"
<p>Contains instances of combinatorial optimizations problems (Sherrington-Kirkpatrick and MAX 2-SAT) as well as further results and plotting notebooks for the paper "Quantum combinatorial optimization beyond the variational paradigm: simple schedules for hard problems".</p>
Data for paper "Parametric schedulability analysis of a launcher flight control system under reactivity constraints"
<p>This is the data set (models, sources and results) for the paper "Parametric schedulability analysis of a launcher flight control system under reactivity constraints" published in Informatica Fundamentae in 2021.</p>
Traces for studying Datacenter Scheduler Programming Abstractions
<p>Traces for the experiments for the research work that investigates the performance impact of various datacenter scheduler programming abstractions.</p>
Log Decomposition Dynamics in Interior Alaska 2b - Log Sampling Schedule
The entire dataset (all 7 files) contains detailed information on a time series study of log decomposition in interior Alaska. The species studied include white and black spruce, aspen, birch, balsam poplar and aspen starting as green trees. In addition white and black spruce in recently burned sites are included. The study was designed to produce a time series of log decomposition measurements over the next 100 years. The information to be measured on the logs includes weight and density changes over specified time periods, changes in nutrient concentrations, and hemicellulose, cellouse, and lignin concentrations, and changes in the quantity of nutrients and hemicellulose, cellouse and lignin. (This file contains the sampling schedule for the log decomposition study.)
PortLib Instances for the Port Scheduling Problem.
<p>In the following we present the <em>PortLib</em> instances for the <em>Port Scheduling Problem</em> (PSP), which have been presented in the paper <em>An Adaptive Large Neighbourhood Search Heuristic for Routing and Scheduling Feeder Vessels in Multi-terminal Ports,</em> written by Erik Hellsten, David Sacramento and David Pisinger, and published in <em>European Journal of Operational Research</em>.</p> <p>The repository includes the results for <em>PortLib </em>instances for the <em>Adaptive Large Neighbourhood Search (ALNS)</em> heuristic and the commercial solver <em>CPLEX.</em> Additionally, we further include the results for the <em>Constraint Programming</em> and the <em>ALNS Math-heuristic</em> approaches from the paper <em>Constraint Programming and Local Search Heuristic: A Math-heuristic Approach for Routing and Scheduling Feeder Vessels in Multi-Terminal Ports</em>, written by David Sacramento, Christine Solnon and David Pisinger, and pending for publication in <em>SN Operations Research Forum</em>.</p> <p>Furthermore, this version includes the results for the <em>Constraint Programming</em> models from the paper <em>Integrated Planning of Feeder Vessels at Multi-Terminal Ports</em>, written by David Sacramento and David Pisinger, and pending for publication in <em>4OR</em> <em>- A Quarterly Journal of Operations Research</em>.</p> <p>The PSP represents a new scheduling problem for feeder vessels in multi-terminal ports, which has been defined in close collaboration with the industry. The proposed problem is a General Shop-like problem, and it accounts for most of the practical restrictions faced by the carriers in scheduling the operations. Given a fleet of feeder vessels, which each of them has a number of operations to perform at different terminals, and each terminal can only serve one vessels at a time, the task is to define an operational schedule, i.e. a starting time for each operation, which satisfies the time window and precedence constraints as well as minimises the departure times of the vessels and packs the schedule as tight as possible. </p> <p>The instances are named <strong>PSP.</strong><strong>n.m.r</strong>, where <strong>n</strong> is the number of container-terminals, <strong>m</strong> is the number of vessels, and <strong>r</strong> is the generic name of the scenario.</p> <p>The instances are randomly generated to be realistic, but in addition we ensured that each instance has a feasible solution as well as strove towards that each constraint should have a significant impact. In general, the instances are made to be slightly harder to solve than the problems faced by industry, in order to properly challenge the developed methods, as well as spurring further development.</p>
Macro Scheduler code for data collection
<p>Supporting information associated with the publication "New insights into single-molecule junctions using a robust, unsupervised approach to data collection and analysis", <em>J. Am. Chem. Soc.</em>, 2015, DOI: 10.1021/jacs.5b05693. This Macro Scheduler (MJT Net Ltd, UK) script is a representative example of that used to automatically measure <em>I</em>(<em>s</em>) traces from <strong>1,8-ODT</strong>-coated and blank (uncoated) Au substrates using an Agilent 5100 Scanning Tunnelling Microscope (interfacing with PicoView 1.14, Agilent Technologies). A short 'Guide to...' document is also included to introduce the user, as are additional files (Microsoft Excel spreadsheets and images) required by the script.</p>
Data and R script for Neville, Andrews, Nettle and Bateson, 'Dissociating the effects of alternative early-life feeding schedules on the development of adult depression-like phenotypes'
<p>The R script and raw data files for the paper 'Dissociating the effects of alternative early-life feeding schedules on the development of adult depression-like phenotypes', by Vikki Neville, Clare Andrews, Daniel Nettle and Melissa Bateson.</p>
Daily Activity Schedule Tel Aviv 2040 from SimMobility MIT Preday
<p>This database countain the activities conducted in the Tel Aviv metropolis on a typical day in 2040. The information is derived from the outcomes of the Simobility demand simulator, which operates on synthetic population and land-use inputs specific to the Tel Aviv metropolis predictions.</p><p> </p>
Daily Activity Schedule Tel Aviv 2017 from SimMobility MIT Preday
<p>This database countain the activities conducted in the Tel Aviv metropolis on a typical day in 2017. The information is derived from the outcomes of the Simobility demand simulator, which operates on synthetic population and land-use inputs specific to the Tel Aviv metropolis.</p>
Dataset for A MILP approach for detailed pipeline scheduling and storage management problem in the phosphate industry
<p>Case studies of a multi-product slurry pipeline scheduling and storage management problem in the phosphate industry.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.