Soft computing methods for multiobjective location of garbage accumulation points in smart cities

Toutouh, Jamal; Rossit, Diego Gabriel; Nesmachnow, Sergio

doi:10.1007/s10472-019-09647-5

Cited by 27 publications

(20 citation statements)

References 28 publications

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“…The majority of them have focused on the proposal of optimization tools for locating waste bins. We found different authors that have applied exact approaches (Rossit et al, 2017; Coutinho-Rodrigues et al, 2012 ), and heuristic approaches (Boskovic and Jovicic, 2015; Ghianni et al, 2012; Toutouh et al, 2019). However, few of these authors have proposed comprehensive methodologies for locating disposal points that involve the different stages of designing this network.…”

Section: Methodsmentioning

confidence: 99%

Application of a methodology to design a municipal waste pre-collection network in real scenarios

et al. 2020

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The design of efficient municipal solid waste (MSW) pre-collection networks can contribute to the global efficiency and sustainability of the reverse logistic chain of MSW in modern cities. With this aim, in this paper a comprehensive methodology that involves making decisions in several stages, from waste fraction classification to the final optimization of waste bins’ location, was applied in two real cases of the city of Bahía Blanca, Argentina. This city, does not have much available data about waste generation and, therefore, an important fieldwork had to be performed for applying this methodology, involving estimating population density per block and waste generation rate per inhabitant, identifying the location of commercial and institutional buildings and also estimating its generation rate, as well as performing a characterization of the MSW from similar studies in the literature and surveys performed to make decisions. The modelling of the urban characteristics was performed in a geographic information system. In the bins’ location problem, a mixed-integer optimization model was applied, seeking to minimize the investment costs, given the maximum area available and the capacity of the bins. Different scenarios were analysed, considering different collection frequencies and the maximum distance to be travelled by the user.

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Section: Methodsmentioning

confidence: 99%

Application of a methodology to design a municipal waste pre-collection network in real scenarios

et al. 2020

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“…The waste accumulation and geographical location information of all the waste bins in the region are abstracted into a waste bin distribution map [64], and the shortest traversal path calculated by using the simulated annealing algorithm: this is the best waste removal route. The analysis provides location information of the waste bins that need to be emptied and optimal route to the WCV by the LoRa communication module [65] to start the automatic collection process.…”

Section: Information Interaction Unit Functionmentioning

confidence: 99%

An Intelligent Waste Removal System for Smarter Communities

Zhang

Wan

et al. 2020

Sustainability

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With the continuous progress of urbanization, the discharge of municipal solid waste has profoundly affected human production/living and social ecological health, and waste disposal has become one of the key issues all over the world. In the context of intelligent technology, this paper innovates the way of waste disposal according to the development direction of a smart city and the requirement of sustainable development strategy. An intelligent urban waste removal system is developed using a ROS (Route Operation System) robot operating system and RRT (Rapid Exploration of Random Trees) path planning algorithm. With a background of data management, the entire process of intelligent automatic waste removal is triggered by automatic communication from individual waste bins to a waste collection vehicle (WCV) where the bin needs to be emptied, and the automatic collection and transportation by the WCV in response. In combination with the IoT (Internet of Things), the system provides scientific data support for the intelligent layout of communities and even urban waste bins to greatly enhance the development of intelligence communities and smart cities.

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“…In addition to the study of garbage collection methods, some scholars have also done corresponding work in other areas. [15] proposed a specific problem model, which can reduce investment costs, increase the number of residents who have installed the dustbin service and the accessibility of the system. Summarize related work.…”

Section: Existing Workmentioning

confidence: 99%

WasNet: A Neural Network-Based Garbage Collection Management System

Yang

2020

IEEE Access

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In response to the increasing pollution caused by unseparated garbage, classification systems for garbage separation have become very popular. First, we constructed a complex data augmentation combination for model training. Second, we designed a novel lightweight neural network garbage classification system called WasNet. This proposed network's 1.5 million parameters on the ImageNet dataset are onehalf of mainstream neural networks, while at 3 million floating point operations per second (FLOPs) it is one third of mainstream neural networks that have obtained the best performance among known lightweight neural networks. The accuracy on the ImageNet data set is 64.5%, on the Garbage Classification dataset it is 82.5%, and on the TrashNet dataset it is 96.10%. Furthermore, we transplanted the model to the hardware platform and assembled an intelligent trash can; we developed a garbage recognition application to facilitate users to directly identify and receive platform information; we built a visualization and decision support platform to help managers monitor traffic in real time. We combined the intelligent trash can, application, visualization and decision-making platform into a system, which is the most complete and effective system among the known research works. The results of the test we conducted on our platform using our extended dataset showed that our scheme is very reliable. At the same time, we also open source our extended datasets for use by other researchers.

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Soft computing methods for multiobjective location of garbage accumulation points in smart cities

Cited by 27 publications

References 28 publications

Application of a methodology to design a municipal waste pre-collection network in real scenarios

Application of a methodology to design a municipal waste pre-collection network in real scenarios

An Intelligent Waste Removal System for Smarter Communities

WasNet: A Neural Network-Based Garbage Collection Management System

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