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Updated: Jun 5, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Multi-period emergency resource allocation problem with a hybrid ant colony optimization and deep Q-network

Jingke Zhou¹, Yingzhen Chen²

¹School of Mathematics and Statistics, Hubei University of Arts and Science, Xiangyang, Hubei, China.

Science Progress

|June 4, 2026

Summary

This summary is machine-generated.

This study introduces a new algorithm (ACO-DQN) to optimize emergency resource allocation, considering interdependencies between areas to minimize losses and maximize fairness during disasters. The approach enhances decision-making for improved disaster resilience.

Keywords:

ant colony optimization deep reinforcement learning emergency resource allocation interdependence

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Last Updated: Jun 5, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Operations Research
Disaster Management
Artificial Intelligence

Background:

Emergency resource shortages lead to significant losses, exacerbated by interdependencies between affected areas.
Existing models often fail to capture cascading impacts, hindering effective resource allocation.
Optimizing resource distribution requires considering both efficiency and equity.

Purpose of the Study:

To develop a multi-period resource allocation model that accounts for systemic interdependencies and cascading losses.
To design a novel hybrid algorithm (Ant Colony Optimization-Deep Q-Network) for efficient problem-solving.
To minimize losses and maximize fairness in emergency resource distribution.

Main Methods:

A systemic loss metric model was developed to quantify cascading impacts.
An emergency resource allocation model was constructed to optimize loss minimization and fairness maximization.
A hybrid Ant Colony Optimization-Deep Q-Network (ACO-DQN) algorithm was designed, integrating ACO's pheromone mechanism with DQN for enhanced convergence and stability.

Main Results:

The proposed ACO-DQN algorithm demonstrated superior solution quality, faster convergence, and greater robustness compared to the standard Deep Q-Network (DQN).
Numerical experiments validated the effectiveness of the hybrid approach in resource allocation problems.
A case study based on the Wenchuan earthquake illustrated the practical benefits of considering interdependencies.

Conclusions:

Considering interdependencies in resource allocation is crucial for balancing efficiency and fairness during emergencies.
The ACO-DQN algorithm offers an effective tool for optimizing emergency resource distribution and improving post-disaster recovery.
The findings provide valuable decision support for enhancing overall disaster resilience.