Sold by: MphasisÂ
Deployed on AWS
The solution uses a heuristics based approach to optimize crew swapping during reassignments due to flight delays
Overview
This solution solves crew rostering problem due to flight time variability or flight delays. It minimizes the changes in the existing crew schedule to provide the optimum revised schedule for each crew member
Highlights
- This is a heuristics based method for crew rostering (reschedule of crew duties) that tries to accomodate flight delays and deviations from existing schedule. The Solution rapidly and optimally modifies the schedule provided by the user based on constraints of crew scheduling. It minimizes number of crew swaps while considering constraints such as number of crew on each flight, minimum and maximum flying hours as defined in the provided input.
- This solution is primarily focused on Airlines but can be repurposed for other use cases like trucking, railroads etc. It can help companies improve the utilization of crew, reduce operations cost and improve employee satisfaction.
- Mphasis Optimize.AI is an AI-centric process analysis and optimization tool that uses AI/ML techniques to mine the event logs to deliver business insights. Need customized Machine Learning and Deep Learning solutions? Get in touch!
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Delivery method
Latest version
Deployed on AWS
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Financing for AWS Marketplace purchases
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $20.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
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Delivery details
Amazon SageMaker model
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Version release notes
Bug Fixes and Performance Enhancement
Additional details
Inputs
- Summary
Input zip file consists of- BASE1,BASE2,BASE3 - Crew schedule corresponding to each crew base. In which crew number is index and flight no is column name. delay_df - Delay data frame which consists expected timing of delayed flight and their base df - Which consists actual start and end time of flight, their base, and crew pair id. input parameter - user defined parameter of rest time for crew pair
- Input MIME type
- text/plain, application/zip
https://github.com/Mphasis-ML-Marketplace/Crew-Rostering-for-Flight-time-Variation/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Crew-Rostering-for-Flight-time-Variation/tree/main/input
Resources
Vendor resources
Support
Vendor support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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