ArticleEnvironmental science and pollution research international2018
SGA: spatial GIS-based genetic algorithm for route optimization of municipal solid waste collection.
Article in Environmental science and pollution research international, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Development and simulation of an innovative autonomous knowledge-based smart waste collection system.Scientific reports · 2026Article
- A significant exploration on meta-heuristic based approaches for optimization in the waste management route problems.Scientific reports · 2024Article
- Artificial intelligence for waste management in smart cities: a review.Environmental chemistry letters · 2023Review
- Novel hybrid evolutionary algorithm for bi-objective optimization problems.Scientific reports · 2023Article
- A multiobjective model for the green capacitated location-routing problem considering drivers' satisfaction and time window with uncertain demand.Environmental science and pollution research international · 2022Article
- Site selection for waste vegetable oil and waste battery collection boxes: a GIS-based hybrid hesitant fuzzy decision-making approach.Environmental science and pollution research international · 2021Article
- COVID-19's lockdown effect on air quality in Indian cities using air quality zonal modeling.Urban climate · 2021Article
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Abstract
Designing optimization models and meta-heuristic algorithms for minimization of traveling routes of vehicles in solid waste collection has been gaining interest in environmental modeling. The computer models and methods are useful to bring out specific strategies for prevention and precaution of possible disasters that could be foreseen worldwide. This paper proposes a new Spatial Geographic Information System (GIS)-based Genetic Algorithm for optimizing the route of solid waste collection. The proposed algorithm, called SGA, uses a modified version of the original Dijkstra algorithm in GIS to generate optimal solutions for vehicles. Then, a pool of solutions, which are optimal routes of all vehicles, is encoded in Genetic Algorithm. It is iteratively evolved to a better one and finally to the optimal solution. Experiments on the case study at Sfax city in Tunisia are performed to validate the performance of the proposal. It has been shown that the proposed method has better performance than the practical route and the original Dijkstra method.
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