Researchers from Carnegie Mellon University and Koç University have developed a quantum-inspired algorithm, the Graver Augmented MultiSeed Algorithm (GAMA), to optimize disaster response. The algorithm addresses the First Responder Network Design Problem (FRNDP), which involves allocating road segments for first responders during disasters while considering evacuation routes for others. GAMA uses a Quadratic Unconstrained Binary Optimization model to explore high-quality solutions. Tests on random graph instances and expected Istanbul earthquake scenarios showed GAMA as a promising alternative to traditional formulations. The research contributes to disaster management preparedness and the emerging field of quantum-inspired computing.
Quantum-Inspired Algorithm for Disaster Response Optimization
A team of researchers from Carnegie Mellon University and Koç University have developed a quantum-inspired algorithm to optimize the response of first responders (FR) during disasters. The algorithm, known as the Graver Augmented MultiSeed Algorithm (GAMA), is designed to tackle the First Responder Network Design Problem (FRNDP).
The First Responder Network Design Problem
The FRNDP is a complex issue that arises during disaster management. It involves the allocation of road segments for exclusive use by first responders, ensuring that they can reach victims promptly without being hindered by traffic congestion. The problem also considers the evacuation routes of other mobile individuals who may be trying to leave the affected area or access medical facilities. The researchers developed a mixed integer nonlinear programming formulation for the FRNDP, which they solved using GAMA.
The Graver Augmented MultiSeed Algorithm
GAMA is a novel hybrid quantum-classical heuristic that uses a Quadratic Unconstrained Binary Optimization (QUBO) model to obtain a partial Graver Bases. This allows for efficient exploration of the solution space for high-quality solutions. The researchers also developed a bilevel nested GAMA within GAMA (GAGA) to further enhance the solution-finding process.
Testing and Results
The researchers tested GAGA on random graph instances of various sizes and instances related to an expected Istanbul earthquake. They found that GAGA offers a promising alternative approach compared to traditional formulations, encouraging further study of quantum-inspired algorithms for complex optimization models.
Disaster Management and Preparedness
The research contributes to the field of disaster management, particularly in the area of preparedness. The proactive approach of pre-disaster planning is essential for a swift and effective post-disaster response. The researchers hope that their work will be beneficial not only in Turkey, which motivated the research but also in other regions worldwide where similar initiatives are being considered.
Quantum-Inspired Computing
The study also adds to the emerging area of quantum and quantum-inspired computing. This field appears promising in tackling complex mixed integer nonlinear program models that capture the FRNDP and other complicated yet critical real-world problems. The researchers anticipate that their quantum-inspired approach will be embedded in an outer loop that studies various scenarios, with the parameters of each scenario determined by experts in geology and practitioners of humanitarian operations.
Related Works
The FRNDP is a variant of the discrete network design problem (DNDP), a well-studied bilevel optimization problem in transportation. The classic DNDP aims to identify an optimal set of candidate links to be adjusted in the network, subject to a budget, taking into consideration users’ reactions to this alteration.
An article titled “A Quantum Inspired Bi-level Optimization Algorithm for the First Responder Network Design Problem” was published on January 22, 2024. The authors of the article are Anthony Karahalios, Sridhar Tayur, Ananth Tenneti, Amirreza Pashapour, Serdar Salman, and Barış Yıldız. The article was sourced from arXiv, a repository of electronic preprints approved for publication after moderation, hosted by Cornell University. The article can be accessed through its DOI reference https://doi.org/10.48550/arxiv.2401.12463.
