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Dynamic routing for efficient waste collection in resource constrained societies.

Marut Priyadarshi1, Meet Maratha1, Mohammad Anish2

  • 1Department of Data Science and Engineering, IISER Bhopal, Bhopal, India.

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This study introduces smart bins and collection vehicles (CV) to optimize waste collection routes in developing nations. The model prioritizes routes based on real-time waste values, improving efficiency and environmental impact.

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Area of Science:

  • Environmental Science
  • Operations Research
  • Computer Science

Background:

  • Developing nations face significant challenges in waste management, including resource limitations and dynamic waste generation.
  • Optimizing collection routes is crucial for efficient waste management but is often hindered by real-time variables.

Purpose of the Study:

  • To develop a model for optimizing waste collection routes using real-time data from smart bins and collection vehicles (CV).
  • To address resource constraints and dynamic waste values in developing countries' waste management systems.

Main Methods:

  • A multi-agent environment was used to model prioritized routes for coordinated CVs.
  • Collection Vehicles (CV) acted as agents, implementing a linear program to maximize waste collection while minimizing travel distance.
  • The model was tested in Chandigarh, India, dividing the city into regions.

Main Results:

  • The implemented model demonstrated significantly improved performance compared to existing methods.
  • Key improvements include increased waste collected, reduced travel distance, and a higher number of total bins covered.
  • The model effectively balances waste collection maximization with distance minimization.

Conclusions:

  • The developed model offers an effective solution for optimizing waste collection in resource-constrained environments.
  • Stakeholders can utilize these outcomes for better resource planning and improved waste collection practices.
  • The study highlights the positive environmental impact of optimized waste management strategies.