Sold by: Mphasis
Deployed on AWS
A multimodal container assignment and route selection solution to reduce operational cost for logistics using Quantum algorithm.
Overview
A container can take multiple routes to reach its destination and each route can have multiple modes of transport. Multimodal container assignment identifies the optimal route for each container, utilizing maximum possible track space and minimum operational cost. The solution utilizes state of the art quantum computing algorithm (simulator based) for optimization, making it scalable and robust. This can help organizations in logistics and supply chain to save on operational costs.
Highlights
- The solution uses state of the art quantum algorithm to select the fastest and most efficient routes with different transport modes and optimize on the cost and speed of transporting them to their destinations. This quantum computing based optimization is significantly faster than the conventional optimization approach.
- This solution can be used for large scale logistics operators like postal delivery systems, railway operators, airline transportation, supply chain, ecommerce, etc.
- Need customized Quantum Computing solutions? Get in touch!
Details
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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 | $40.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $20.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $40.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $40.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $40.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $40.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $40.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $40.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $40.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $40.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a 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 Version 3.6
Additional details
Inputs
- Summary
The model requires a CSV file as input. It is mandator to include following columns:
- CB1
- CB2
- CB3
- cTruck
- Capcity_mode
- for each combination of container and mode, specify the tracks in their separate columns
- Limitations for input type
- Max number of containers -1000 Max tracks - 100
- Input MIME type
- text/csv
https://github.com/Mphasis-ML-Marketplace/Quantum-Simulator-Multimodal-Selector/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Quantum-Simulator-Multimodal-Selector/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 |
|---|---|---|---|
CB1 | Cost for each container to be transported via multimodal route 1 | Type: Integer | Yes |
CB2 | Cost for each container to be transported via multimodal route 2 | Type: Integer | Yes |
CB3 | Cost for each container to be transported via multimodal route 3 | Type: Integer | Yes |
cTruck | Cost for each container to be transported via Truck | Type: Integer | Yes |
Capcity_mode | The capacity of each mode in the mode specified | Type: Integer | Yes |
Each combination of container and mode | For each container create three columns to specify the modes in that route. For ex. if there are 4 containers then 12 columns need to be specified. First for route 1 then route 2 and finally for route 3 container wise. | Type: Integer | Yes |
specify the tracks in their separate columns | For each container create three columns to specify the modes in that route. For ex. if there are 4 containers then 12 columns need to be specified. First for route 1 then route 2 and finally for route 3 container wise. | Type: Integer | Yes |
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