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733 results for “Scheduling”
Capacitated Lotsizing and Scheduling Problem - CLSP
<p>Instances and source code for the algorithms and models described in the paper "Changeover minimization in the production of metal parts for car seats", published in Computers and Industrial Engineering.</p> <p>Code also available in <a title="CLSP GitHub repository" href="https://github.com/jmcolmenar/CLSP" target="_blank" rel="noopener">https://github.com/jmcolmenar/CLSP</a></p> <p>Please, cite as follows:</p> <p>J. Manuel Colmenar, Manuel Laguna, Raúl Martín-Santamaría, Changeover minimization in the production of metal parts for car seats, Computers & Industrial Engineering, 2024, 110634, ISSN 0360-8352, https://doi.org/10.1016/j.cie.2024.110634.</p> <p>Bibtex:</p> <p>@article{COLMENAR2024110634,<br>title = {Changeover minimization in the production of metal parts for car seats},<br>journal = {Computers & Industrial Engineering},<br>pages = {110634},<br>year = {2024},<br>issn = {0360-8352},<br>doi = {https://doi.org/10.1016/j.cie.2024.110634},<br>url = {https://www.sciencedirect.com/science/article/pii/S0360835224007563},<br>author = {J. Manuel Colmenar and Manuel Laguna and Raúl Martín-Santamaría},<br>keywords = {Lot sizing, Multi-period production scheduling, Nonidentical parallel machines, Metaheuristic optimization},<br>abstract = {We tackle a capacitated lot-sizing and scheduling problem (CLSP) with the main objective of minimizing changeover time in the production of metal parts for car seats. Changeovers occur when a machine (or production line) is reconfigured to produce a different product or part, leading to production downtime and loss of efficiency. In this study, we first provide a mixed-integer programming (MIP) formulation of the problem. We test the limits of solving the problem with commercial mathematical programming software. We also propose two approaches to tackle instances found in practice for which the mathematical programming model is not a viable solution method. Both approaches are based on partitioning the entire production of a part into production runs (or work slots). In the first approach, the work slots are assigned to machines and sequenced by a metaheuristic that follows the search principles of the GRASP (greedy randomized adaptive procedure) and VNS (variable neighborhood search) methodologies. In the second approach, we develop a Hexaly Optimizer (formerly known as LocalSolver) model to assign and sequence work slots. The study provides insights into how to minimize changeovers and improve production efficiency in metal parts manufacturing for car seats. The findings of this study have practical implications for the auto-part manufacturing industry, where efficient and cost-effective production is critical to meet the demands of the market.}<br>}</p>
TSN Scheduler Benchmarking: Scenarios v2.0.0
<p><strong>TSN Scheduler Benchmarking Scenarios </strong></p> <p>This dataset provides scenarios for testing Time-Sensitive Networking (TSN) schedulers, originally developed as part of the following PhD thesis (to be published):</p> <ul> <li>E. Schweissguth, "<strong>Routing and Scheduling of Time-Triggered Ethernet Networks: ILP Models and Scheduler Benchmarking.</strong>". University of Rostock.</li> </ul> <p>An earlier version (v1.0.0) of benchmarking scenarios was published alongside the following publication:</p> <ul> <li>E. Schweissguth, S. Mehner, D. Hellmanns, J. Falk, H. Parzyjegla, P. Danielis, O. Hohlfeld, G. Muehl, and D. Timmermann, "<strong>TSN Scheduler Benchmarking.</strong>" in <em>WFCS 2023</em>. IEEE.</li> </ul> <p>Scenarios of this earlier version are considered deprecated, but may still be useful for testing schedulers which do not support heterogenous cycle times. They are still available in the history (see releases). Compared to v1.0.0, this version contains scenarios with heterogeneous cycle times and frame sizes, reworked scenario parameters, and additional multicast scenarios. </p> <p>Please refer to the thesis for detailed background information. Refer to "<strong>TSN Scheduler Benchmarking: Results</strong>" (<a href="https://github.com/EikeSG/TSNBenchResults">https://github.com/EikeSG/TSNBenchResults</a> or linked projects on Zenodo) for a comprehensive comparison of ILP-based scheduling models based on this scenario dataset. When accessing this dataset through Zenodo, also check the corresponding GitHub repository (<a href="https://github.com/EikeSG/TSNBenchScenarios">https://github.com/EikeSG/TSNBenchScenarios</a>) for updates and for a good in-browser view (especially for markdown files).</p> <p>The scenarios are input files for scheduler benchmarking, intended for re-use by other researchers. For further details about the dataset as well as licensing and citation please see README.md.</p>
TSN Scheduler Benchmarking: Results v2.0.1
<p><strong>TSN Scheduler Benchmarking Results</strong></p> <p>This dataset provides test results of multiple Time-Sensitive Networking (TSN) schedulers that were tested and compared in the following PhD thesis (to be published):</p> <ul> <li>E. Schweissguth, "<strong>Routing and Scheduling of Time-Triggered Ethernet Networks: ILP Models and Scheduler Benchmarking.</strong>". University of Rostock.</li> </ul> <p>An earlier version of this repository contained results from the following publication:</p> <ul> <li>E. Schweissguth, S. Mehner, D. Hellmanns, J. Falk, H. Parzyjegla, P. Danielis, O. Hohlfeld, G. Muehl, and D. Timmermann, "<strong>TSN Scheduler Benchmarking.</strong>" in <em>WFCS 2023</em>. IEEE.</li> </ul> <p>These results are still available in the history (see releases). Compared to v1.0.0, this version contains results for TSN schedulers that were tested on an updated scenario dataset with a general update to scenario parameters, additional multicast scenarios, and heterogeneous cycle times and frame sizes within a single stream set. Moreover, schedulers used are not by different working groups (as in v1.0.0) but model variations of ILP-based schedulers, which are described in detail in the thesis.</p> <p>Please refer to the thesis for detailed background information. The results are based on the following scenarios (i.e., topologies and stream sets): "<strong>TSN Scheduler Benchmarking: Scenarios v2.0.0</strong>" (see <a href="https://doi.org/10.5281/zenodo.13993512" rel="nofollow">https://doi.org/10.5281/zenodo.13993512</a> or <a href="https://github.com/EikeSG/TSNBenchScenarios/releases/tag/v2.0.0">https://github.com/EikeSG/TSNBenchScenarios/releases/tag/v2.0.0</a>). When accessing this dataset through Zenodo, also check the corresponding GitHub repository (<a href="https://github.com/EikeSG/TSNBenchResults">https://github.com/EikeSG/TSNBenchResults</a>) for updates and for a good in-browser view (especially for markdown files).</p> <p>This repository contains all runtime/schedulability/latency plots for all benchmarking tests run throughout the thesis, whereas result plots in the thesis are excerpts from the plots in this repository. See README.md for further details on the directory contents.</p> <p>Note that scheduler result files are available in json format as github release assets and as separate zenodo dataset (<a href="https://doi.org/10.5281/zenodo.13995796" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13995796</a>). They are not part of this dataset because zenodo does not pick up github release assets of linked repositories. For the same version (here: v2.0.1), the dataset on github and the linked zenodo dataset are identical.</p>
Cross-Dock Trailer Scheduling with Workforce Constraints: A Dynamic Discretization Discovery Approach
<p>The zip file contains the dataset used in the research published under the title: Cross-Dock Trailer Scheduling with Workforce Constraints: A Dynamic Discretization Discovery Approach. There are 640 instances in this dataset.</p> <p>There are a total of 1920 .csv files with names starting with: </p> <ol> <li>LoadingDoor (640 files for the 640 instances): contains the loading door index, loading door deadline, and the penalty for violating the deadline at the corresponding loading door</li> <li>Trailer_&_Processing (640 files for the 640 instances): contains the trailer index, unloading door index, and the processing time of the trailer at the unloading door</li> <li>Trailer_&_Shipment (640 files for the 640 instances): contains the trailer index, the arrival time of the trailer, loading door index, and the number of shipments to be cross-docked from the trailer to the loading door.</li> </ol>
scc-process-scheduling-instances: 2021-07-25 version
<p>Deleted excessive files</p>
FIG. 2 in Daily activity schedule, gregariousness, and defensive behaviour in the Neotropical harvestman Goniosoma longipes (Opiliones: Gonyleptidae)
FIG. 2. Small aggregation of Goniosoma longipes on a cave wall at Parque Florestal do Itapetinga, South-east Brazil. Note the overlapping of legs. Scale bar = 2 cm.
FIG. 1 in Daily activity schedule, gregariousness, and defensive behaviour in the Neotropical harvestman Goniosoma longipes (Opiliones: Gonyleptidae)
FIG. 1. Activity schedule of Goniosoma longipes at Parque Florestal do Itapetinga, Southeast Brazil. The moon and the sun indicate dusk and dawn, respectively.
FIG. 6 in Foraging ecology of the giant Amazonian ant Dinoponera gigantea (Hymenoptera, Formicidae, Ponerinae): activity schedule, diet and spatial foraging patterns
FIG. 6. Ritualized territorial contest between Dinoponera gigantea foragers from diVerent colonies at the border of their foraging areas. (A) Ants lock their mandibles together, vigorously antennate each other's head, and constantly kick one another with the Žrst pair of legs. (B) As the contest escalates the dominant ant (right) directs the tip of the gaster against the opponent's body. The subordinate ant eventually walks away as she breaks free.
FIG. 2 in Foraging ecology of the giant Amazonian ant Dinoponera gigantea (Hymenoptera, Formicidae, Ponerinae): activity schedule, diet and spatial foraging patterns
FIG. 2. Frequency distribution of trip duration relative to diVerent activities performed by workers of Dinoponera gigantea in a Brazilian rainforest site. Although foraging ants may be away from the nest for up to 3 h, successful foragers usually return after 30–60 min of searching. Data are based on continuous 12-h observations at colony Nos 9 and 10, from 6.00 a.m. to 6.00 p.m. Two successful foragers from each colony are not included in the graphs because the duration of their foraging trips could not be recorded.
Case studies of a multi-product and multiple-sources slurry pipeline network scheduling problem in the phosphate industry
<p>The data shared summarises two case studies of a multi-product and multiple-sources slurry pipeline network scheduling problem in the phosphate industry.</p>
Problem Instances for "Exact and Meta-Heuristic Approaches for Unrelated Parallel Machine Scheduling"
<p>This dataset contains the following instance sets from the <a href="http://hdl.handle.net/20.500.12708/11486">Master's Thesis</a> and <a href="https://doi.org/10.1007/s10951-021-00714-6">Journal Paper</a> "Exact and Meta-Heuristic Approaches for Unrelated Parallel Machine Scheduling":</p> <ul> <li>Training instances</li> <li>Validation instances, with reference solutions</li> <li>Real-Life instances</li> </ul>
Data of the Paper: Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage
<p>This is the online artifact containing the code, data and evaluation log of the experiment performed for the research paper accepted at IEEE QRS 2022 with the title: </p> <p><strong>Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage</strong></p> <p><strong>Abstract:</strong> Container scheduling is a fundamental part of today’s service and cloud-based applications. Schedulers operate at different levels depending on how much control the system developers have. On the one hand, container orchestration managers such as Google Kubernetes manage the scheduling of containers to different nodes. On the other hand, serverless managers, such as Google Autopilot, take care of the underlying infrastructure automatically, and developers do not need to manage the nodes. However, when it comes to container depletion, i.e., removing the assigned cloud resources to an idle container, current scheduling technologies have limitations. In this paper, we propose our approach to managing cloud resource usage when containers are idle efficiently. For this purpose, we deplete idle containers statefully, i.e., propose a novel manager that monitors idle containers, saves their state, and efficiently depletes them. This manager reconstructs a depleted container using the saved state when reconstruction is needed. In our approach, we suggest an Infrastructure as Code component to automate the creation of new nodes if a depleted container cannot be scheduled on the same node, e.g., because of being overloaded. We provide an analytical model for the stateful depletion of containers and their rescheduling and empirically evaluate the accuracy of our model. For this purpose, we ran an experiment on a private cloud infrastructure and Google Cloud Platform. Our model has a low error rate of 4.28% averaged over public and private clouds.</p>
Summary of results from numerical experiments described in research paper "A New Simheuristic Approach for Stochastic Runway Scheduling"
<p>This file provides a summary of results from the numerical experiments described in our research paper, "A New Simheuristic Approach for Stochastic Runway Scheduling".</p>
Common Workflow Scheduler Evaluation with Nextflow and Kubernetes
<ul> <li>Setup scripts to test Nextflow with CWS on Kubernetes</li> <li>Traces and logs of 990 workflow executions</li> </ul>
Cobot assignment and job shop scheduling problem
<p>Real-world-based and artificially created data sets for the cobot assignment and job shop scheduling problem</p>
Data and results for the paper "Decision Support for the Technician Routing and Scheduling Problem"
<p>Data and results for the paper "Decision Support for the Technician Routing and Scheduling Problem"</p>
The data supporting the findings of "Continuity-skill-restricted Scheduling and Routing Problem: Formulation, Optimization and Implications"
<p>The data supporting the findings of "Continuity-skill-restricted Scheduling and Routing Problem: Formulation, Optimization and Implications"</p> <p>Instances_CSRP provides the instances tested for the CSRP</p> <p>Instances_SCSRP provides the instances tested for the SCSRP</p> <p>Real_instance_sol.csv presents the solutions depicted in Figure 4 and Figures 1-6 in Appendix</p>
Vessels and Train Schedules
<p>Inbound port and destination port of vessels and Train schedules from Valencia to Barcelona and Valencia to Zaragoza and vice versa of each route.</p>
Supplementary material to "Energy-oriented crane scheduling in a steel coil storage"
<p><strong>Benchmark instances for the energy-oriented crane scheduling problem in steel coil storages</strong></p> <p><em>This file describes the structure and content of the accompanying data of the benchmark instances of the paper "Energy-oriented crane scheduling in a steel coil storage". The instances intend to assist future research efforts in the domain of crane scheduling in steel coil storage.</em></p>
Ultrasound Assessment of Gastric Residual Volume in Children Scheduled for Elective Surgery After Clear Fluids Fasting for One Versus Two Hours: a Comparative Study
ClinicalTrials.gov study NCT04228497. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
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
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International Brain Laboratory public data
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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.