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June 4, 2026Sustainability0 citationsOpen Access

A Heuristic-Based Methodology for Collecting Irregular Waste in Sustainable Cities

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ADAli Tuna DinçerMYMehmet Yildirim

Key Points

  • The aim is to develop an efficient system for collecting irregular waste in sustainable cities using mobile technology.
  • Developed a mobile application for waste location mapping and notifications to municipalities.
  • Utilized genetic and differential evolution algorithms for vehicle routing and type optimization.
  • Compared efficiencies across four scenarios with varying waste locations and types.
  • Differential evolution method proved to be 0.8% more efficient than the genetic algorithm.
  • Optimizations based on actual road distances were 8.0% more successful than those using Euclidean distances.
  • The mobile-supported system enhances the efficiency of irregular waste collection services.

Abstract

This study develops a mobile-supported system that municipalities can use in their irregular waste collection services within the scope of smart cities. Irregular waste refers to waste that individuals or organizations produce non-periodically, which arises unexpectedly or in an unusual manner. Unlike small-volume household waste collected at routine times, irregular waste is generally large-volume waste such as construction rubble, vegetable oil, mineral oil, and garden waste. In the irregular waste collection system developed in this study, waste locations are marked on the map of an application running on mobile devices, and notifications are sent to the municipality. The Google Distance Matrix API was used for processing and visualizing the notification locations on the map. Daily or 4 h planning is carried out using this data. In this study, a genetic algorithm and a differential evolution algorithm were used for vehicle routing and vehicle type optimization. To compare the efficiency of both methods, four different scenarios were designed with different numbers of waste locations and different types and amounts of waste, and the successes of the methods were compared. Differential evolution is found to be on average 0.8% better. Optimizations performed with actual road distances were found to be 8.0% more successful than optimizations performed with Euclidean distances.

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Cite This Study

Dinçer et al. (2026) studied this question.

synapsesocial.com/papers/6a2116cfd499ed480b16fc46https://doi.org/10.3390/su18115528
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