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TitanQ Use Cases
InfinityQ’s technology is ready to transform multiple industries.
 

Energy Grid Optimization

Problem:

Maximize green score while minimizing cost to source power from various power providers.

Inputs:

Power provider matrix, and interaction matrix between the providers.

Formulation possibilities:

Constrained power maximization problem.

Solution:

Real time grid optimization capabilities for grid rebalancing.

Minimize Total Cost

Maximise Total Green Score

Subject to Max/Min power from each plant

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Supply Chain Optimization

Problem:
Build a robust supply chain that can meet customer deadlines to avoid costly penalties while withstanding potential shocks and minimizing costs.

Inputs:

Supplier Lead Times, Part delivery date, potential schedule disruptions.

Formulation possibilities:

Smart supply chain scheduling, with additional slack between steps and multi-supplier awareness.

 

Solution:

-Initial planning of robust schedule  

-Method to replan given supply chain shock

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Port Optimization

Problem:

Automated ports require advanced dynamic optimization capabilities to reroute cranes & containers effectively.

Inputs:

Crane & container availability for ships, demand expectations for ship arrivals.

Formulation possibilities:

On site/cloud planning of production capabilities using mixed-integer optimization platform.

Solution:

Real-time FPGA/GPU system capable for dynamic crane & container loading planning.

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Portfolio Optimization

Problem:

Produce maximum returns for a given associated risk value.

Inputs:

Asset expected returns and correlation data between assets.

portfolio.png
efficient frontier.png

Formulation possibilities:

Markowitz model-based risk minimization + maximum weight independent set.

Solution:

Direct asset allocation with efficient frontier generation of model.

Middle Mile Delivery

Problem:

Middle Mile Delivery. Set of trucks need to be routed between delivery hubs maximizing total profits while respecting constraints.

Inputs:

Connection graph and conflicts graph. Which routes may be taken, which can’t be taken together.

Formulation possibilities:

Set Cover with Weighted Vertices

Maximum Weight independent Set.

Solution:

Ising Model solver with ability to solve graphs with >10000 vertices.

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