Background and Objective: Optimization of urban waste collection and transportation system has the largest part of waste management costs. Therefore, improving this system and reducing its operating costs as a necessity in urban waste management has always been consider
More
Background and Objective: Optimization of urban waste collection and transportation system has the largest part of waste management costs. Therefore, improving this system and reducing its operating costs as a necessity in urban waste management has always been considered. Method: Due to the high volatility, changes in the size of the waste, climate change and demographic and substructure tissue, the use of artificial neural network system (ANN) is a suitable method for predicting the production waste size, and on the other hand, for The optimization of the management system of these wastes is also used by the surface response method (RSM). Findings The results of this combined method show that the best combination of factors affecting urban waste transport system was proposed by RSM considering the largest loaded pack with about 26 workers, 10 pickups and 6 trucks. This combination is capable of carrying around 34836 tons of cargo at a cost of 596696000 Rials, which represents a high efficiency over actual values. Also, to predict load, the back propagation algorithm (BP) with 9 neurons in the hidden layer was selected as the best model with a predictive power of 99/19% in prediction of weight and 96/62% in cost prediction. Discussion and Conclusion: The results showed that using the combination of two methods of surface response as a statistical method and artificial neural network as a mathematical method, we can find suitable results for evaluation and optimization of waste collection and transportation system.
Manuscript profile
