Emergency Response planning involves making optimal use of limited resource
Consider an emergency response use case: snowfall. There is a limited number of snowplows available and a large area to cover over a limited time period. The city manager needs to know the quickest way to provide response to create traffic flow and, in some cases, provide emergency vehicles to save lives. The operator needs to have a view of all the operational plows and trucks to be able to dispatch them in the most optimized way. Using data science and optimization algorithms, the model needs to take in the resource constraints and unique conditions as input, leveraging past data and patterns and output recommendations of most optimal plan. By gaining key insights and optimize the routes and deployment of the snowplows, the city is able to save money, respond in a timely fashion, and even save lives.
Emergency Response: The Benefits of Machine Learning and Decision Optimization Working Together
While machine learning can take into account all available data and past history to predict the demand by time period and location for plowing assets, decision optimization (DO) can take it a step further and generate an optimal asset relocation plan, subject to meeting demand and other constraints and dependencies (e.g. initial asset location, time needed to relocate assets), and optimization metrics (minimizing total relocation cost, maximizing customer satisfaction/road usage during storms, minimizing traffic and accidents on the road). Not only does it offer us valuable insights, but it also generates an actionable plan. A storm operations manager equipped with a powerful solution based on Emergency Response Accelerator will be able to advise on which assets to relocate and when such that the impact of the snow emergency is minimized, while also minimizing the relocation effort (number of trucks and distance).
- A structured business glossary of business terms.
- Sample data science assets
How does it work?
The glossary provides the information architecture that you need to govern the data about your equipment. And your data scientists can use the sample notebooks, predictive models and decision optimization model to accelerate data preparation, machine learning modeling and finding an optimal solution. The Machine Learning and Decision Optimization models are deployed for use in production.
The accelerator includes a Sample application which shows an example of how thedeployed models can be embedded into an Emergency Response Planners tooling.
A sample of the Emergency Response accelerator deployments integrated in an Emergency Planning application
Required services: To use the industry accelerators, you must install one or more of the following services on IBM® Cloud Pak for Data
Importing the accelerator
To use this accelerator on Cloud Pak for Data v126.96.36.199, complete the following steps:
- Download the emergency-response-industry-accelerator.tar.gz file.
- Extract the contents of the package.
- Follow the instructions in the README.pdf.
This accelerator has been verified on:
- Cloud Pak for Data v188.8.131.52
About the developer:
Terms and Conditions
The terms under which you are licensing IBM Cloud Pak for Data also apply to your use of the Industry Accelerators. Before you use the Industry Accelerators, you must agree on these additional terms and conditions that are set forth here. This information contains sample modules, exercises, and code samples (the code may be provided in source code form ("Source Code")) (collectively "Sample Materials").
License: Subject to the terms herein, you may copy, modify, and distribute these Sample Materials within your enterprise only, for your internal use only; provided such use is within the limits of the license rights of the IBM agreement under which you are licensing IBM Cloud Pak for Data. The Industry Accelerators might include applicable third-party licenses. Review the third-party licenses before you use any of the Industry Accelerators. You can find the third-party licenses that apply to each Sample Material in the notices.txt file that is included with each Sample Material.
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