Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Optimized Airline Crew Rostering
By:
Latest Version:
2.1
Airline Crew Rostering helps to optimally schedule the crew work shifts considering equal distribution of workloads among the crew members.
Product Overview
This solution focuses on crew rostering problem of airline industry. It provides the work schedule for each crew member considering different aspects like limitation on maximum flying hours, maximum overall hours, number of assigned crew for each flight, and rest period of a crew between two consecutive flights. This solution derives the work schedules of each crew member while reducing load imbalance among the crew members. This reduces the cost of operations, optimizes the crew size required, improves the service levels and airline safety and at the same time ensures crew well- being.
Key Data
Version
By
Type
Model Package
Highlights
Airline Crew Rostering uses heuristic based optimization approach, which is computationally efficient to handle large datasets as compared to the classical optimization based approaches. This solution is tested on a very large publicly available dataset. It outputs the work schedules of each crew member for the given timeperiod based on user input constraint parameters.
This solution can be used by Airlines to generate the optimal rosters for their crew keeping in view their well-being and at the same time optimizing the costs.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
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Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$10.00/hr
running on ml.m5.xlarge
Model Batch Transform$20.00/hr
running on ml.m5.large
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.23/host/hr
running on ml.m5.xlarge
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.m4.4xlarge | $10.00 | |
ml.m5.4xlarge | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge | $10.00 | |
ml.p3.16xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.p3.2xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.p2.xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.p2.16xlarge | $10.00 | |
ml.c4.4xlarge | $10.00 | |
ml.m5.xlarge Vendor Recommended | $10.00 | |
ml.c5.9xlarge | $10.00 | |
ml.m4.xlarge | $10.00 | |
ml.c5.4xlarge | $10.00 | |
ml.p3.8xlarge | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.m5.large | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 |
Usage Information
Model input and output details
Input
Summary
Input zip file consists of- day_i - ith day wise csv file which have leg no, departure base, date of depart, hour of depart, arrival base, date and hour of arrival IntialSolution.in - Text file which have dict for crew pairing considering diff air bases listOfBases - CSV file which have airport(Base name), status, and no of employees available at each base. input_parameters - Text file which provides user defined data such as -max flying hours, max overall hours, no of crew, rest time b/w duty
Input MIME type
text/plain, application/zipSample input data
Output
Summary
Output consists of below listed files- Json - {Base no-{Crew name - assigned flight no,....}} A json file which consists of schedule or assigned flight corresponding to each crew for each base
Output MIME type
text/plain, application/jsonSample output data
Sample notebook
Additional Resources
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Support Information
Optimized Airline Crew Rostering
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