This analysis compares crossover operators in genetic algorithms for waste collection routing, suggesting PMX enhances efficiency and sustainability.
The Waste Collection Routing Problem (WCRP) presents a significant challenge for municipalities and waste management companies. This study applies Geographic Information System and Genetic Algorithm (GIS-GA) to optimise waste collection routes in WPKL16, Kuala Lumpur. A comparative analysis of crossover operators between Order Crossover (OX) and Partially Mapped Crossover (PMX) reveals that PMX consistently outperforms OX in both solution quality and computational efficiency. GIS-GA is implemented in Python to generate optimised routes, demonstrating substantial improvements over existing waste collection paths. Additionally, reducing waste collection to four days per week significantly decreases the total distance travelled while maintaining operational efficiency. These findings emphasise the importance of crossover operator selection in GA-based optimisation and highlight the potential of strategic scheduling adjustments to enhance cost-effectiveness and sustainability in waste management.
No takes yet. Share an insight, caveat, or question.
Ahmad et al. (2025) studied this question.