Influence of the Municipal Solid Waste Collection System on the Time Spent at a Collection Point: A Case Study

Carlos, Mar; Gallardo, Antonio; Edo-Alcón, Natalia; Abaso, Juan Ramón

doi:10.3390/su11226481

Cited by 16 publications

(9 citation statements)

References 12 publications

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“…e technology reduces travel distance significantly as compared to the previous approach, resulting in a more efficient garbage collecting procedure and lower costs [19]. Finally, the authors think that their work has contributed to waste management since it is a valuable tool for technical personnel who may develop and enhance a town's garbage collecting system [20][21][22]. e authors suggested a unique municipal solid waste (MSW) collection optimization model based on the actual real-world trash-collecting scenario in Sfax and then used the new innovative routing method for MSW collection.…”

Section: Literature Reviewmentioning

confidence: 95%

[Retracted] Machine Learning and IoT‐Based Waste Management Model

Khan

Kumar

Srivastava³

et al. 2021

Computational Intelligence and Neuroscience

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A rapid rise in inhabitants across the globe has led to the inadmissible management of waste in various countries, giving rise to various health issues and environmental pollution. The waste-collecting trucks collect waste just once or twice in seven days. Due to improper waste collection practices, the waste in the dustbin is spread on the streets. Thus, to defeat this situation, an efficient solution for smart and effective waste management using machine learning (ML) and the Internet of Things (IoT) is proposed in this paper. In the proposed solution, the authors have used an Arduino UNO microcontroller, ultrasonic sensor, and moisture sensor. Using image processing, one can measure the waste index of a particular dumping ground. A hardware prototype is also developed for the proposed framework. Thus, the presented solution for the efficient management of waste accomplishes the aim of establishing clean and pollution-free cities.

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Section: Literature Reviewmentioning

confidence: 95%

[Retracted] Machine Learning and IoT‐Based Waste Management Model

Khan

Kumar

Srivastava³

et al. 2021

Computational Intelligence and Neuroscience

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“…A number of loaders-This is one of the factors considered in the literature [9,19]. Data were collected as part of Dataset 1.…”

Section: Data Collectionmentioning

confidence: 99%

“…In some papers, a collection time is divided into the driving time and the time spent at a waste collection point (WCP) only [4][5][6]. Time spent at a WCP can also be included more detailed by considering the time of picking up/putting down the container [7,8] and the time of inactivity (employees rest) [9], as well as the time for lunch break [2]. As a part of driving time, some authors consider the turns on the road [10,11], while others consider the time of stopping at lights [12,13].…”

Section: Introductionmentioning

confidence: 99%

“…The literature has already emphasized that determining the time spent at WCP by garbage trucks is problematic. For this reason, the authors of Reference [9] proposed a methodology for measuring all the time types related to the waste collection process. In terms of time spent at a WCP, they emphasized that this time depends on many features, such as the container's size, the type of vehicle, or the occurrence of overfilling of the containers.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Time Spent at the Waste Collection Point by A Garbage Truck with A Multiple Regression Model

Giel

Dąbrowska

2021

Sustainability

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The planning of the garbage trucks’ routes is an essential process in waste collection companies. The main issues in garbage truck routing are determining the optimal routes, minimizing time, decreasing the costs, and reducing the pollution’s emission. In the literature, the time spent at a waste collection point (WCP) is considered as the average time, or it is not included at all. Time spent at a WCP is determined by the processes of picking up, emptying, and putting down the waste containers and the factors specific for different WCPs. Those factors impact the time spent at WCP significantly. Excluding time spent at a WCP or taking the average of that in the planning approach may lead to the inaccurate estimation of total collection time. The aim of this article is to present the multiple regression model for estimating time spent at a WCP. We analyzed the impact of the WCP factors (i.e., building type and number of containers) on the time that a garbage truck spends at it. We initially considered seven chosen factors, five categorical and two numerical. Based on this, we developed the multiple regression model based on linear regression use. Later, the proposed model was validated based on data obtained from the municipal company operating in Wroclaw city, Poland. The study confirmed that the defined factors significantly affect garbage truck’s time spent at a WCP and should be taken into account during waste collection planning processes’ performance.

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“…Transportation is an important process in the MSW supply chain. The optimization of waste transportation mostly has the goal of minimum economic cost [30] or transportation distance [31][32][33]. However, the above studies only optimized waste transportation under a single objective.…”

Section: The Optimization Of Waste Transportation Routesmentioning

confidence: 99%

Redesigning the Municipal Solid Waste Supply Chain Considering the Classified Collection and Disposal: A Case Study of Incinerable Waste in Beijing

2021

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The output of municipal solid waste is growing rapidly, which has brought tremendous pressure to urban development. The supply chain of municipal solid waste (MSW) in China mainly contains three processes: collection, transportation, and disposal. The waste is sorted at the collection and disposed of according to the classification. However, it is mixed at the transportation stage. Mixed transportation remixes the separately collected waste, which seriously affects the disposal effect. The supply chain of MSW urgently needs to be redesigned to improve the MSW disposal effect. First of all, on the ground of the waste treatment situation, we redesigned the supply chain of MSW in China. Secondly, combined with the redesign of the MSW supply chain, this paper established the function allocation model for collection stations, making a collection station only gather one type of waste, and built the transportation path planning model for vehicles, reducing the impact of waste storage on residents. Finally, based on the data of Xuanwu District in Beijing, the supply chain redesigning practical example of incinerable waste was given. The supply chain redesigning model in this paper not only makes full use of the existing infrastructure but also improves the disposal effect of waste. The supply chain redesigning model has practical application value.

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Influence of the Municipal Solid Waste Collection System on the Time Spent at a Collection Point: A Case Study

Cited by 16 publications

References 12 publications

[Retracted] Machine Learning and IoT‐Based Waste Management Model

[Retracted] Machine Learning and IoT‐Based Waste Management Model

Estimating Time Spent at the Waste Collection Point by A Garbage Truck with A Multiple Regression Model

Redesigning the Municipal Solid Waste Supply Chain Considering the Classified Collection and Disposal: A Case Study of Incinerable Waste in Beijing

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