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Bi-Objective Optimization for Power Emergency Material Distribution Using a Hybrid Genetic Simulated Annealing Algorithm
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DOI：10.1587/transfun.2025EAP1105.png)
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Abstract
En 中文
As the critical role of power systems in emergency response continues to escalate, the efficiency of power emergency material distribution directly influences the speed of grid recovery. To address the challenge of optimizing the distribution paths for emergency power materials, this paper presents a bi-objective optimization model aimed at minimizing both the total distribution delay time and the average unmet demand rate at failure-prone demand points. In the context of disaster relief, real-time road repair data and environmental signals from fault areas, provided by the power Internet of Things (IoT), are incorporated as constraints into the distribution path, thereby formulating a multi-objective path optimization model. To solve this optimization problem, a hybrid algorithm combining Genetic Algorithm (GA) and Simulated Annealing (SA) is proposed. This hybrid approach effectively balances time efficiency and resource utilization within the multi-objective optimization framework, offering a robust and efficient solution for power emergency material distribution. Simulation results indicate that the optimized model significantly enhances material distribution efficiency, reduces delays, and ensures a timely and effective response to grid fault demands.
Keywords:
power mergency material distribution
path optimization
genetic algorithm
simulated annealing
grid emergency response
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