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Related Experiment Video

Updated: Apr 26, 2026

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

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

Published on: December 9, 2012

A closed-loop multi-agent swarm framework for synergistic location-routing optimization in municipal solid waste

Yili Liu¹, Zhihong Lin¹, Wangrui Dou¹

¹School of Automobile, Chang'an University, Middle Section of South Second Ring Road, Xi'an, 710064, China.

Journal of Environmental Management

|April 24, 2026

Summary

This study optimizes municipal solid waste collection and transportation using multi-agent swarm modeling and bi-level programming. The integrated approach reduces costs by 10% and enhances urban waste management efficiency.

Keywords:

Facility location Multi-agent Municipal solid waste Vehicle routing Waste collection

Related Experiment Videos

Last Updated: Apr 26, 2026

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

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

Published on: December 9, 2012

Area of Science:

Environmental Engineering
Operations Research
Computer Science

Background:

Waste management faces challenges with diverse vehicle types, complex transport, and fluctuating generation.
Current systems struggle with dynamic optimization for collection and transportation.

Purpose of the Study:

To develop a collaborative optimization method for municipal solid waste (MSW) collection and transportation.
To integrate multi-agent swarm modeling and bi-level programming for synergistic optimization.

Main Methods:

Constructed a bi-level location-routing model with closed-loop feedback.
Designed a hybrid algorithm combining genetic algorithm and large neighborhood search.
Addressed the capacitated vehicle routing problem with time windows.

Main Results:

A retrofit plan for transfer stations reduced total costs by 10%.
Optimal cost for new construction was achieved with four transfer stations.
Scenario simulations analyzed economic and environmental impacts.

Conclusions:

The method provides tools for dynamic multi-vehicle waste collection and transportation.
Establishes a multi-agent simulation framework for smart waste management platforms.
Enhances the efficiency and sustainability of urban solid waste systems.