Simulation output
Description
Output data from the Anylogic simulation model used for training machine learning models in Ashraf, M., Eltawil, A., & Ali, I. (2024). Disruption detection for a cognitive digital supply chain twin using hybrid deep learning. Operational Research, 24(2), 23. https://doi.org/10.1007/s12351-024-00831-y. Each file contains relevant replications under a particular scenario. Each file contains the observed daily values for each feature in the form of (timestamp(day), value) pairs. The features are: MeanInterarrivalTime, MeanSupplierProcessingTime, MeanManufacturerProcessingTime, MeanDisributorProcessingTime, SupplierUtilizationFactor, ManufacturerUtilizationFactor, DistributorUtilizationFactor, MeanSupplierQueueLength, MeanManufacturerQueueLength, MeanDistributorQueueLength, MeanBacklog, MeanWorkInProcess, MeanLeadTime, MeanFlowTime, MeanWaitingTime, MeanProcessingTime, and DailyOutput. For disrupted scenarios, disruption end time stamp as well as disruption impact start and end timestamps are included for each replication.
Files
Steps to reproduce
Please refer to Ashraf, M., Eltawil, A., & Ali, I. (2024). Disruption detection for a cognitive digital supply chain twin using hybrid deep learning. Operational Research, 24(2), 23. https://doi.org/10.1007/s12351-024-00831-y
Institutions
Categories
Funding
Ministry of Higher Education, Egypt
10.13039/501100004532
Japan International Cooperation Agency
10.13039/501100002385