Smartening the movement path of municipal garbage trucks using genetic algorithm with emphasis on economic-environmental indicators

Nasim Ghadami, Bita Deravian, Hossein Pouresmaeil, Reza Aghlmand and Mohammad Gheibi*

The collection is one of the most important steps in waste management, accounting for 60% of total costs. Therefore, a little improvement in collection operations can have a significant impact on total cost savings. On the other hand, the traffic of heavy vehicles collecting waste causes the air pollution spread and the passages pavement damage in case of excessive loading. Therefore, the issue of vehicle route determining to achieve this goal is very important. This study simulated the routing process of garbage trucks using random routing problems and genetic algorithms. The simulation results showed that the genetic algorithm converges to the optimal response in the 2069th generation and according to the convergence graph, in the 1000th generation onwards, the slope of the graph decreases. On the other hand, the amount of cost function is reduced from 11775.4909 to 1589.6028 by optimizing mentioned model, and the performance result has led to the emergence of the shortest possible path. With the help of the algorithm, all the management parameters of sustainable development, including reducing air pollution, reducing street pavement destruction, and energy (fuel) consumption are achieved. Finally, by integrating ArcGIS software, the output of the algorithm was matched to the map.

Keywords:

Published on: Jun 28, 2021 Pages: 80-85

Full Text PDF Full Text HTML DOI: 10.17352/aest.000041
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