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Updated: Jun 29, 2026
11:53
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
Optimization of municipal solid waste (MSW) collection routes in Hengyang City, china using an enhanced genetic
Siyu Zhang1, Xiaowen Zhang1, Yanan Xiao1
1School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China.
Waste Management (New York, N.Y.)
|March 3, 2026
Summary
An Enhanced Genetic Algorithm optimizes waste collection routes, reducing total distance by 31% and costs significantly compared to other methods. This improves efficiency and lowers environmental impact in urban sanitation management.
Area of Science:
- Environmental Science
- Operations Research
- Computer Science
Background:
- Growing populations and urbanization necessitate efficient, low-carbon waste collection for environmental sanitation.
- Traditional Genetic Algorithms (GA) face challenges like slow convergence and computational bottlenecks in complex, real-world waste collection routing.
- Issues such as empty runs and detours plague current waste collection systems.
Purpose of the Study:
- To develop a vehicle routing optimization model for waste collection that minimizes transportation, fixed, and carbon emission costs.
- To address the limitations of traditional algorithms in high-density urban environments.
- To enhance the efficiency and real-world applicability of waste collection route optimization.
Main Methods:
- Developed a vehicle routing optimization model incorporating real road network data via the Amap API.
- Proposed an Enhanced Genetic Algorithm (EGA) with adaptive parameter adjustment, combined mutation, and elitist preservation strategies.
- Integrated population diversity feedback to improve global search, convergence speed, and computational efficiency.
Main Results:
- The Enhanced Genetic Algorithm (EGA) demonstrated superior global search capability, convergence speed, robustness, and computational efficiency.
- Optimized routes achieved a 31% reduction in total distance compared to the standard Genetic Algorithm (GA).
- Significant cost savings were realized: 55% versus Simulated Annealing (SA), 90% versus Particle Swarm Optimization (PSO), and 12% versus Ant Colony Optimization (ACO).
Conclusions:
- The proposed Enhanced Genetic Algorithm (EGA) offers a highly effective solution for waste collection vehicle routing optimization.
- The model provides feasible and efficient solutions for real-world urban sanitation challenges, reducing operational costs and environmental impact.
- EGA significantly outperforms traditional algorithms in terms of distance reduction and cost-effectiveness.
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