ارزيابي و بهينه سازي سيستم جمع آوري و حمل پسماند شهر اروميه با تركيب روش سطح پاسخ و شبكه عصبي مصنوعي

Evaluation and Optimization of Waste Collection and Transportation System in Urmia by Combining the Response Surface and Artificial Neural Network

زﻣﯿﻨﻪ و ﻫﺪف : ﺑﻬﯿﻨﻪ ﺳﺎزي ﺳﯿﺴﺘﻢ ﺟﻤﻊ آوري و ﺣﻤﻞ ﻣﻮاد زاﯾﺪ ﺷﻬﺮي ﺑﯿﺶ ﺗﺮﯾﻦ ﺳﻬﻢ ﻫﺰﯾﻨﻪ ﻫﺎي ﻣﺪﯾﺮﯾﺖ ﻣﻮاد زاﯾﺪ را از آن ﺧﻮد ﮐﺮده اﺳﺖ. ﺑﻨﺎﺑﺮاﯾﻦ ﺑﻬﺒﻮد اﯾﻦ ﺳﯿﺴﺘﻢ و ﮐﺎﻫﺶ ﻫﺰﯾﻨﻪ ﻫﺎي ﻋﻤﻠﯿﺎﺗﯽ آن ﺑﻪ ﻋﻨﻮان ﯾﮏ ﺿﺮورت در ﻣﺪﯾﺮﯾﺖ ﭘﺴﻤﺎﻧﺪ ﺷﻬﺮي ﻫﻤﻮاره ﻣﻮرد ﺗﻮﺟﻪ ﻗﺮار ﮔﺮﻓﺘﻪ اﺳﺖ. روش ﺑﺮرﺳﯽ: ﺑﻪ ﻣﻮﺟﺐ ﺑﺎﻻ ﺑﻮدن ﻧﻮﺳﺎن، ﺗﻐﯿﯿﺮ در اﻧﺪازه ﭘﺴﻤﺎﻧﺪ ﻫﺎ، ﺗﻐﯿﯿﺮات آب و ﻫﻮاﯾﯽ و ﺑﺎﻓﺖ ﻫﺎي ﺟﻤﻌﯿﺘﯽ و زﯾﺮ ﺳﺎﺧﺘﯽ اﺳﺘﻔﺎده از ﺳﯿﺴﺘﻢ ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﻣﺼﻨﻮﻋﯽ )ANN( ﯾﮏ روش ﻣﻨﺎﺳﺐ ﺑﺮاي ﭘﯿﺶ ﺑﯿﻨﯽ اﻧﺪازه ﭘﺴﻤﺎﻧﺪ ﺗﻮﻟﯿﺪي ﻣﯽ ﺑﺎﺷﺪ و از ﻃﺮﻓﯽ ﺑﺮاي ﺑﻬﯿﻨﻪ ﺳﺎزي ﺳﯿﺴﺘﻢ ﻣﺪﯾﺮﯾﺘﯽ اﯾﻦ ﭘﺴﻤﺎﻧﺪﻫﺎ ﻧﯿﺰ از روش ﺳﻄﺢ ﭘﺎﺳﺦ )RSM( اﺳﺘﻔﺎده ﻣﯽ ﮔﺮدد. ﯾﺎﻓﺘﻪ ﻫﺎ: ﻧﺘﺎﯾﺞ ﺣﺎﺻﻞ از اﯾﻦ روش ﺗﺮﮐﯿﺒﯽ ﻧﺸﺎن ﻣﯽ دﻫﺪ ﮐﻪ ﺑﻬﺘﺮﯾﻦ ﺗﺮﮐﯿﺐ از ﻋﻮاﻣﻞ ﺗﺎﺛﯿﺮﮔﺬار در ﺳﯿﺴﺘﻢ ﺣﻤﻞ زﺑﺎﻟﻪ ﺷﻬﺮي ﺗﻮﺳﻂ RSM ﺑﺎ در ﻧﻈﺮ ﮔﺮﻓﺘﻦ ﺑﯿﺶ ﺗﺮﯾﻦ ﺑﺎر ﺣﻤﻞ ﺷﺪه ﺑﺎ ﺣﺪود 26 ﮐﺎرﮔﺮ، 10 واﻧﺖ و 6 ﮐﺎﻣﯿﻮن ﭘﯿﺸﻨﻬﺎد ﺷﺪ. اﯾﻦ ﺗﺮﮐﯿﺐ ﻗﺎدر ﺑﻪ ﺣﻤﻞ ﺑﺎر ﺣﺪود 34836 ﺗﻦ ﺑﺎ ﻫﺰﯾﻨﻪ 596696000 رﯾﺎل ﻣﯽ ﺑﺎﺷﺪ، ﮐﻪ ﻧﺴﺒﺖ ﺑﻪ ﻣﻘﺎدﯾﺮ واﻗﻌﯽ ﮐﺎراﯾﯽ ﺑﺎﻻﯾﯽ را ﻧﺸﺎن ﻣﯽ دﻫﺪ. ﻫﻤﭽﻨﯿﻦ ﺑﺮاي ﭘﯿﺶ ﺑﯿﻨﯽ ﺑﺎر ﺣﻤﻞ ﺷﺪه اﻟﮕﻮرﯾﺘﻢ ﭘﺲ اﻧﺘﺸﺎر BP)( ﺑﺎ 9 ﻧﺮون در ﻻﯾﻪ ﭘﻨﻬﺎن ﺑﻪ ﻋﻨﻮان ﺑﻬﺘﺮﯾﻦ ﻣﺪل ﺑﺎ ﻗﺪرت ﭘﯿﺶ ﺑﯿﻨﯽ 99/19% در ﭘﯿﺶ ﺑﯿﻨﯽ وزن و 96/62% در ﭘﯿﺶ ﺑﯿﻨﯽ ﻫﺰﯾﻨﻪ اﻧﺘﺨﺎب ﺷﺪ. ﺑﺤﺚ و ﻧﺘﯿﺠﻪ ﮔﯿﺮي: ﻧﺘﺎﯾﺞ ﻧﺸﺎن داد ﮐﻪ ﺑﺎ اﺳﺘﻔﺎده از ﺗﺮﮐﯿﺐ دو روش ﺳﻄﺢ ﭘﺎﺳﺦ ﺑﻪ ﻋﻨﻮان ﯾﮏ روش آﻣﺎري و ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﻣﺼﻨﻮﻋﯽ ﺑﻪ ﻋﻨﻮان ﯾﮏ روش رﯾﺎﺿﯽ ﻣﯽ ﺗﻮان ﺑﻪ ﻧﺘﺎﯾﺞ ﻣﻨﺎﺳﺒﯽ ﺑﺮاي ارزﯾﺎﺑﯽ و ﺑﻬﯿﻨﻪ ﺳﺎزي ﺳﯿﺴﺘﻢ ﺟﻤﻊ آوري و ﺣﻤﻞ ﭘﺴﻤﺎﻧﺪ

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.