Sold by: MphasisÂ
Deployed on AWS
Airline Crew Rostering helps to optimally schedule the crew work shifts considering equal distribution of workloads among the crew members.
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.
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.
- 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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Latest version
Deployed on AWS
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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.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
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- 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/zip
https://github.com/Mphasis-ML-Marketplace/Optimized-Airline-Crew-Rostering-/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Optimized-Airline-Crew-Rostering-/tree/main/Input
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