Sold by: MphasisÂ
Deployed on AWS
The solution uses a classical optimization based approach to derive pairings, i.e., combination of flight legs.
Overview
This solution provides an optimized solution for Airline crew pairing considering minimization of number of deadheads. Pairing is a schedule for an aircraft consisting of different flight legs while each leg consists of detail of upcoming journey, such that arrival and destination airport, flight duration, and flight base etc. Deadheading occurs when off duty crew members take a flight to a different airport for their shift. This solution considers multiple operational constraints, like flying duration, rest time between two flights, start and end base of sequential flights etc.
Highlights
- This is a classical optimization based method for crew pairing that tries to minimize number of deadheads. The algorithm considers constraints such as minimum and maximum flying hours, number of legs in a pairing, and start and end base should be same, etc.
- 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 resources while reducing the operational cost.
- 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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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 | $0.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $0.00 |
ml.m5.4xlarge Training Recommended | Algorithm training on the ml.m5.4xlarge instance type | $50.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $0.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $0.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $0.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $0.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $0.00 |
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is the first version.
Additional details
Inputs
- Summary
The dataset columns are as follows:
- #leg_nb
- date_dep
- date_arr
- airport_dep
- airport_arr
- hour_dep
- hour_arr
- Limitations for input type
- Inferencing is done within training pipeline. Real time inference endpoint/batch transform job is not required.
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/Airline-Crew-Pairing-Optimization/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Airline-Crew-Pairing-Optimization/tree/main/input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
#leg_nb | Unique flight Leg Number | Type: FreeText | Yes |
date_dep | Date of flight departure | Type: FreeText
Limitations: Format (yyyy-mm-dd) | Yes |
date_arr | Date of flight arrival | Type: FreeText
Limitations: Format (yyyy-mm-dd) | Yes |
airport_dep | Name of Departure Airport | Type: FreeText | Yes |
airport_arr | Name of Arrival Airport | Type: FreeText | Yes |
hour_dep | Time of flight departure | Type: FreeText
Limitations: format is hh:mm | Yes |
hour_arr | Time of flight arrival | Type: FreeText
Limitations: format is hh:mm | Yes |
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