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Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization.

Mahmuda Akhtar1, M A Hannan2, R A Begum3

  • 1Dept of Civil & Structural Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.

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|February 4, 2017
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Summary
This summary is machine-generated.

Optimizing waste collection routes using a smart bin system and a modified Backtracking Search Algorithm significantly reduces travel distance and environmental impact. This approach enhances efficiency and lowers costs by emptying bins only when they reach an optimal threshold waste level (TWL).

Keywords:
BSACVRP modelRoute optimizationThreshold waste levelWaste collection

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Area of Science:

  • Operations Research
  • Environmental Science
  • Computer Science

Background:

  • Waste collection is a critical component of waste management, presenting environmental, economic, and social challenges.
  • Optimizing collection routes can lead to substantial reductions in operational costs and environmental emissions.

Purpose of the Study:

  • To present a modified Backtracking Search Algorithm (BSA) integrated with the smart bin concept for optimizing waste collection routes within capacitated vehicle routing problem (CVRP) models.
  • To minimize the total distance traveled for waste collection by introducing a threshold waste level (TWL) for smart bins.

Main Methods:

  • A modified Backtracking Search Algorithm (BSA) was applied to capacitated vehicle routing problem (CVRP) models.
  • The concept of threshold waste level (TWL) was introduced to optimize bin emptying frequency.
  • A scheduling model was developed to compare the proposed system against conventional methods.

Main Results:

  • The optimal threshold waste level (TWL) was identified between 70% and 75% fill level.
  • A 36.80% reduction in travel distance was achieved for 91.40% of total waste collection.
  • Significant reductions were observed in fuel consumption (50%), fuel cost (47.77%), and CO2 emissions (44.68%), with a 36.78% increase in collection efficiency.

Conclusions:

  • The proposed optimization model, utilizing smart bins and a modified BSA, offers a viable strategy for optimizing waste collection routes.
  • This approach effectively reduces both economic costs and environmental impacts associated with waste management.