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Updated: May 16, 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 dynamic optimization model for solid waste recycling.

Davide Anghinolfi¹, Massimo Paolucci, Michela Robba

¹DIBRIS - Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Via Opera Pia 13, 16145 Genova, Italy. davide.anghinolfi@unige.it

Waste Management (New York, N.Y.)

|November 20, 2012

Summary

This study introduces a dynamic model for optimizing recycling collection routes and schedules. The findings show that optimized waste collection can significantly increase net benefits compared to current policies.

Related Experiment Videos

Last Updated: May 16, 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 Science
Operations Research
Waste Management

Background:

Recycling is a critical component of sustainable waste management, encompassing environmental, economic, and social factors.
Existing research often overlooks the dynamic optimization of material collection logistics within recycling management.

Purpose of the Study:

To develop and present a dynamic decision model for optimizing recycling collection.
To integrate this model into a Geographic Information System (GIS)-based Decision Support System (DSS).
To evaluate the economic effectiveness of the optimized collection strategy through a case study.

Main Methods:

A dynamic decision model was formulated using state variables (daily waste quantities per bin) and control variables (collection quantities and vehicle routes).
The objective function was designed to minimize total costs minus benefits.
The model was implemented within a GIS-based DSS and tested using a case study of the Cogoleto municipality.

Main Results:

The optimized recycling collection strategy demonstrated a significant increase in net benefits.
Net benefits achieved through the optimized model were approximately 2.5 times greater than those of the current collection policy.
The case study validated the practical effectiveness of the proposed dynamic decision model.

Conclusions:

Dynamic optimization of recycling collection routes and schedules offers substantial economic advantages.
The developed GIS-based DSS provides an effective tool for improving waste management efficiency.
Implementing optimized collection strategies is crucial for maximizing the economic benefits of recycling programs.