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Related Experiment Video

Updated: Jul 11, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Published on: December 9, 2012

An improved genetic algorithm for solving the helicopter routing problem with time window in post-disaster rescue.

Kaidong Yang^1,2, Peng Duan³, Huishan Yu²

¹Shandong Key Laboratory of Optical Communication Science and Technology, Liaocheng University, Liaocheng 252059, China.

Mathematical Biosciences and Engineering : MBE

|November 3, 2023

Summary

This study optimizes helicopter dispatch for disaster rescue by modeling it as a vehicle routing problem with time windows. An improved genetic algorithm significantly reduces helicopter travel distance, enhancing rescue efficiency.

Keywords:

helicopter dispatch improved genetic algorithm post-disaster rescue time window vehicle routing problem

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Area of Science:

Operations Research
Disaster Management
Artificial Intelligence

Background:

The vehicle routing problem (VRP) is critical in post-disaster rescue operations.
Helicopter utilization in disaster response presents significant logistical challenges for efficient dispatch.
Existing methods struggle with the complexity of time-sensitive helicopter routing.

Purpose of the Study:

To address the challenge of efficient helicopter dispatch in post-disaster rescue.
To model helicopter dispatch as a variant of the Vehicle Routing Problem with Time Windows (VRPTW).
To develop an optimized algorithm for solving the VRPTW in disaster scenarios.

Main Methods:

An improved Genetic Algorithm (GA) incorporating local and global search strategies was developed.
A cooperative initialization strategy was employed to generate high-quality, diverse initial populations.
The algorithm was tested using 56 instances derived from Solomon instances.

Main Results:

The proposed GA demonstrated superior performance compared to Tabu Search, Ant Colony Optimization, hybrid GA, and Simulated Annealing.
The algorithm achieved average relative percentage increases in distance traveled that were significantly smaller than competing methods.
Specifically, the proposed algorithm reduced travel distance by 0.178, 0.027, 0.075, and 0.041 times compared to the benchmark algorithms.

Conclusions:

The developed improved GA is an efficient and effective method for solving the VRPTW in post-disaster rescue.
The algorithm successfully reduces helicopter driving distance, thereby improving rescue operation efficiency.
This research offers a valuable tool for optimizing resource allocation in time-critical disaster response scenarios.