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733 results for “Scheduling”
Data from: Shifts in hatch dates do not provide pied flycatchers with a rapid ontogenetic route to adjust offspring time schedules to climate change
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Data from RACE: Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment
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Data from: Linking oceanographic conditions, migratory schedules and foraging behaviour during the non‐breeding season to reproductive performance in a long‐lived seabird
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Data from: Pollination on the dark side: acoustic monitoring reveals impacts of a total solar eclipse on flight behavior and activity schedule of foraging bees
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Data from: Ecological forensics: using single point stable isotope values to infer seasonal schedules of animals after two diet switches
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Temperature-mediated plasticity in incubation schedules is unlikely to evolve to buffer embryos from climatic challenges in a seasonal songbird
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Data from: Simultaneous pulsed flowering in a temperate legume: causes and consequences of multimodality in the shape of floral display schedules
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Data from: Modification of reproductive schedule in response to pathogen exposure in a wild insect: support for the terminal investment hypothesis
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Problem instances for scheduling jobs with time windows on unrelated parallel machines
<p>The following dataset contains problem instances for the unrelated machine scheduling problem with job release dates and deadlines, which are used in the article "Tadumadze, G., Emde, S. & Diefenbach, H. Exact and heuristic algorithms for scheduling jobs with time windows on unrelated parallel machines. <em>OR Spectrum</em> <strong>42, </strong>461–497 (2020). <a href="https://doi.org/10.1007/s00291-020-00586-w">https://doi.org/10.1007/s00291-020-00586-w</a>".</p> <p>The problem instances are stored in table “instances”, where the columns of the table can be interpreted as follows:</p> <p>Problem_ID: <Autonumber></p> <p>n: <number of jobs>;</p> <p>m: <number of machines>;</p> <p>w: <vector with n elements: the j-th element corresponds to the weight of job j>;</p> <p>r: <vector with n elements: the j-th element corresponds to the release date of job j>;</p> <p>d: <vector with n elements: the j-th element corresponds to the deadline of job j>;</p> <p>p: <n*m matrix: each entry in j-th column and i-th row corresponds to the processing time of job j on machine i>;</p> <p>The first 80 entries (Problem_ID between 1-80), contain discrete Berth-allocation problem instances, provided by “Jean-François Cordeau, Gilbert Laporte, Pasquale Legato, Luigi Moccia, (2005) Models and Tabu Search Heuristics for the Berth-Allocation Problem. Transportation Science 39(4):526-538. https://doi.org/10.1287/trsc.1050.0120” and additionally contain machine availability times, which are stored in the following columns:</p> <p>s: <vector with m elements: the i-th element corresponds to the start availability time of machine i>;</p> <p>e: <vector with m elements: the i-th element corresponds to the end availability time of machine i>;</p> <p>The following 270 entries (Problem_ID between 81-270) contain newly generated random problem instances with the instance generation scheme proposed by “Nicholas G. Hall, Marc E. Posner, (2001) Generating Experimental Data for Computational Testing with Machine Scheduling Applications. Operations Research 49(6):854-865. https://doi.org/10.1287/opre.49.6.854.10014”. The first 10 instances (Problem_ID between 81-90) are used for parameter tuning tests and the next 180 (Problem_ID between 91-270) instances for computational performance comparison.</p> <p>The last 30 entries (Problem_ID between 271-300) contain integrated truck and workforce scheduling problem instances with fixed workforce at each door, provided by “Giorgi Tadumadze, Nils Boysen, Simon Emde, Felix Weidinger (2019) Integrated truck and workforce scheduling to accelerate the unloading of trucks. European Journal of Operational Research 278(1):343-362. https://doi.org/10.1016/j.ejor.2019.04.024”.</p> <p>The detailed computational results for each instance, approach and objective function are reported in tables which are named with the following convention: "results_<approach>_<objective value>”.</p>
Problem instances for robust spotter scheduling problem
<p>We randomly generated problem instances for the robust spotter scheduling problem (RSSP) which are proposed in the article "Tadumadze, G., Boysen, and N., Emde, S. (2020): Robust spotter scheduling in trailer yards. <em>OR Spectrum</em>, <a href="https://doi.org/10.1007/s00291-020-00599-5">https://doi.org/10.1007/s00291-020-00599-5</a>". For a detailed instance generation scheme see Section 4.1 of the aforementioned work.</p> <p>We generate RSSP instances for the following three different sized trailer terminals which differ in the number of dock doors <span class="math-tex">\(|D|\)</span> of the terminal and the parking positions<span class="math-tex">\(|\Pi|\)</span> in the parking lot:</p> <ul> <li>S: <span class="math-tex">\(|D| = 20\)</span>; <span class="math-tex">\(|\Pi| \in \{40,60,80,100\}\)</span></li> <li>M: <span class="math-tex">\(|D| = 50\)</span>; <span class="math-tex">\(|\Pi| \in \{100,150,200,250\}\)</span></li> <li>L: <span class="math-tex">\(|D| = 200\)</span>; <span class="math-tex">\(|\Pi| \in \{400,600,800,1000\}\)</span></li> </ul> <p>The dataset contains 450 RSSP instances each of which is described in a text-file (labeled as “<ID>.txt” where ID is a running index of instance (from 0001 to 0450)). In particular, each file contains information about the number of transport requests (i.e., jobs) <span class="math-tex">\(n\)</span>, the spotter fleet size <span class="math-tex">\(m\)</span> , the processing time of transport request <span class="math-tex">\(j\)</span> (<span class="math-tex">\(j=1,…,n\)</span>) <span class="math-tex">\(p_j\)</span> , the completion time of transport request <span class="math-tex">\(j\)</span> (<span class="math-tex">\(j=1,…,n\)</span>) <span class="math-tex">\(C_j\)</span> and the deadheading time it takes a spotter to move from the target position of job <span class="math-tex">\(j\)</span> to the start position of job <span class="math-tex">\(j'\)</span> (<span class="math-tex">\(j,j'=1,…,n\)</span>) <span class="math-tex">\(\delta_{jj'}\)</span> (i.e., sequence-dependent setup times). Further, each file consists of additional information that describes the direction of each transport request (i.e., whether it describes a transport request of a semitrailer from the parking lot to terminal or vice versa) and the index of the corresponding truck (i.e., semitrailer).</p> <p>Each text-file is structured as follows:</p> <p>count_jobs =</p> <p><integer number that describes the number of transport requests (i.e., <span class="math-tex">\(n\)</span>)>.</p> <p>Count_spotters =</p> <p><integer number that describes the number of spotters (i.e., <span class="math-tex">\(m\)</span>)>.</p> <p>processing_times =</p> <p><vector with <span class="math-tex">\(n\)</span> integer numbers: the <span class="math-tex">\(j\)</span>-th element of the vector describes the processing time of for transport request <span class="math-tex">\(j\)</span> (i.e., <span class="math-tex">\(p_j\)</span>)>.</p> <p>completion_times =</p> <p><vector with <span class="math-tex">\(n\)</span> integer numbers: the <span class="math-tex">\(j\)</span>-th element of the vector describes the completion time of <span class="math-tex">\(j\)</span>-th transport request (i.e., <span class="math-tex">\(C_j\)</span>)>.</p> <p>weights =</p> <p><vector with <span class="math-tex">\(n\)</span> integer numbers: the <span class="math-tex">\(j\)</span>-th element of the vector describes the relative importance of transport request <span class="math-tex">\(j\)</span> (i.e., <span class="math-tex">\(w_j\)</span>)>.</p> <p>driving_times =</p> <p><matrix with <span class="math-tex">\(n*n\)</span> integers: each entry in <span class="math-tex">\(j\)</span>-th column and <span class="math-tex">\(j'\)</span>-th row describes the sequence-dependent setup times between jobs <span class="math-tex">\(j\)</span> and <span class="math-tex">\(j'\)</span> (<span class="math-tex">\(\delta_{jj'}\)</span>)>.</p> <p> </p> <p>*********************job_truck_relation*********************</p> <p> </p> <p>to_terminal =</p> <p><vector with n boolean values: the <span class="math-tex">\(j\)</span>-th element of vector has a value “<span class="math-tex">\(True\)</span>”, if the <span class="math-tex">\(j\)</span>-th job stands for transport request of a semitrailer from the parking lot to the terminal; “<span class="math-tex">\(False\)</span>” -- otherwise>.</p> <p>trucks_index =</p> <p><vector with n integer numbers: the <span class="math-tex">\(j\)</span>-th element of vector describes the index of the semitrailer of transport request <span class="math-tex">\(j\)</span>>.</p> <p>The first 360 RSSP instances (i.e., 0001-0360) are generated for the computational performance test (Section 4.2) and the next 90 instances (i.e., 0361-0450) -- for the simulation study (Section 4.4).</p>
Data from: Seed germination schedule and environmental context shaped the population genetic structure of subtropical evergreen oaks on the Yun-Gui Plateau, Southwest China
<p>The evergreen broadleaved forests (EBLFs) of Southwest China have a long-term stable environment and support a diverse flora, thus forming a global biodiversity hotspot. However, the key drivers that have shaped the genetic diversity patterns of species in these EBLFs are not well understood. <i>Quercus delavayi</i>, <i>Q. schottkyana</i>,<i> </i>and<i> Q. kerrii </i>are sympatric oak species with different seed biological traits that are typical for these EBLFs. This study combined multilocus phylogeography and ecological niche modeling to screen 33 <i>Q. delavayi</i> populations. Their population genetic structure was inferred in comparison with previous studies on <i>Q. schottkyana</i> and <i>Q. kerrii</i>. The seed germination traits of all three species were also investigated. cpDNAs showed a significant phylogeographic structure in <i>Q. delavayi</i>, which was not detected in <i>Q. schottkyana</i> or <i>Q. kerrii</i>. <i>Quercus delavayi</i>, <i>Q. kerrii</i>, and <i>Q. schottkyana </i>exhibited different pollen-to-seed migration ratios (r = 219, 117, and 22, respectively), which are linked to the germination schedules of acorns. The distributions of <i>Q. delavayi</i> and <i>Q. schottkyana</i> remained long-term stable since the last glacial maximum (LGM) with a similar nSSR genetic gradient change along latitude. Instead, <i>Q. kerrii</i> experienced a prominent range expansion since the LGM with genetic diversification between the East and the West of the Tanaka line due to environmental heterogeneity. These results identify seed traits and environmental heterogeneity as two key drivers that shape the population genetic structure of EBLF trees in Southwest China. These should be considered in regional forestry conservation and management.</p>
Data from: Effects of female reproductive competition on birth rate and reproductive scheduling in a historical human population
Costly reproductive competition among females is predicted to lead to strategies that reduce these costs, such as reproductive schedules. Simultaneous births of co-resident women in human families can reduce their infant survival, but whether such competition also affects overall birth rates and whether females time their pregnancies to avoid simultaneous births remain unknown, despite being key questions for understanding how intra-female competition affects reproductive strategies. Here, we used detailed parish registers to study female reproductive competition in historical Finnish joint –families, where brothers stayed on their natal farms and sisters married out, and consequently unrelated daughters-in-law often co-resided and competed for household resources. We quantified the time-varying effects of having reproductive-aged competitor(s) on a woman's interval from marriage to first childbirth, on age-specific fertility, and on birth scheduling. Contrary to our hypothesis, the presence of one or several potential female competitors did not lead to longer first birth intervals or lower age-specific probability of reproduction. We also found no evidence that women would schedule their reproduction to avoid the real cost of simultaneous births on their offspring mortality risk; age-specific reproductive rates were unaltered by changes in the presence of other infants in the household. These results raise interesting questions regarding the evolution of fertility suppression in social mammals in different contexts, the costs and benefits of extended families for female reproductive success and strategies deployed, and the cultural practices that may help to avoid the negative outcomes of female reproductive competition in human families.
Impacts of irrigation scheduling on urban green space cooling
<p>This record contains the microclimate and soil moisture data from a field experiment that investigated the impacts of irrigation scheduling on urban green space irrigation in Melbourne, Australia.</p>
JSS Data from Function-specific Scheduling Policies in Cloud-Edge, Multi-Region Serverless Systems
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A new low-carbon project scheduling problem with renewable and traditional energy A comprehensive analysis and its solution
<p>The data and related experimental results used in the article</p>
USING CONSUMER-REGULATORS TO EQUALIZATION OF ELECTRICAL ENERGY SYSTEM LOAD SCHEDULE
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Area-Efficient Memory Scheduling for Dynamically Scheduled High-Level Synthesis
<p>Dataset for Area-Efficient Memory Scheduling for Dynamically Scheduled High-Level Synthesis</p>
Updated JSS Data from Function-specific Scheduling Policies in Cloud-Edge, Multi-Region Serverless Systems
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Additional data for "AI-driven Integrated Online Layout Planning and Scheduling in Flexible Assembly Systems"
<p>Additional data for the thesis on "Integrated online layout planning and scheduling in flexible<br>assembly systems".</p>
System Data for Robust Scheduling of Integrated Electricity and Gas Systems: A Cost and Flexibility Assessment
<p><span>This data is published as a supplement to the following reference:</span></p> <p><span>B. Akbari and G. Sansavini, “Robust scheduling of integrated electricity and gas systems: A cost and flexibility assessment,” <em>International Journal of Electrical Power & Energy Systems</em>, vol. 160, p. 110107, Sep. 2024, doi: <a href="https://doi.org/10.1016/j.ijepes.2024.110107">10.1016/j.ijepes.2024.110107</a>.</span></p>
ScienceDex guides
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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.