4914. Integrating Learned Risk Assessment into Staffing Optimization Models for a Hybrid Quantum Solver
Invited abstract in session TE-66: Applied Quantum Optimization, stream Quantum Optimization.
Tuesday, 14:15-15:45Room: JUR – Seminar-Raum 31
Authors (first author is the speaker)
| 1. | Mansour Zarrin
|
| D-Wave Systems Inc. | |
| 2. | Matthew Woolway
|
Abstract
This article presents an optimization framework for multi-project staffing using integrated surrogate risk predictions. It allocates fractional full-time equivalents (FTEs) while satisfying capacity, project, and risk budget requirements. Given that project risk is often nonlinear, the approach constructs staffing-based features and embeds a trained multilayer perceptron (MLP) into the model. Optimized by the D-Wave Stride Hybrid Solver, the framework is detailed through mathematical formulations and a practical illustrative example.
Keywords
- Quantum Computing
- Artificial Intelligence
- Programming, Nonlinear
Status: accepted
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