Reconfiguration of garbage collection system based on Voronoi graph theory: a simulation case of Beijing region

Xin, Chunlin; Liang, Shuo; Feng-wu, Shen

doi:10.1007/s10878-020-00614-z

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…Statistics can be calculated for each route that includes the travelled distance, travelling time, collection time, and fuel consumption. An objective function described in (10) can be used to calculate the fitness of each route which is made up of the mentioned normalized statistics.…”

Section: Optimal Route Simulationsmentioning

confidence: 99%

“…These values were also supplied by the City of Oshawa and were approximated based on field data that was previously collected from the waste collection vehicles. In (10), d ij is the distance of edge (v i , v j ), d ij,global is the set of all edges' distances in the road network, c ijf is the fuel consumption coefficient on edge (v i , v j ), (c ijf d ij ) global is the set of fuel consumed on each edge in the road network, speed ijs is the speed limit on edge (v i , v j ), (d ij /speed ijs ) global is the set of traveling time needed on each edge in the road network, du t is the time needed to collect waste from stop location t, and du t,global is the set of collection time needed at every stop in the city.…”

Section: Optimal Route Simulationsmentioning

confidence: 99%

“…This study provided improved results when compared to using only the K-means clustering approach. Another study used Voronoi graph theory and a clustering algorithm (weighted K-means) to optimize the relocation of domestic waste disposal facilities with respect to the surrounding waste pickup locations [10]. A similar study used the Thiessen polygon method (also known as the Voronoi polygon method) to zone a given city into several collection zones for several collection locations, and according to their research, this is the first time Voronoi polygons have been used for collection zone generation for waste collection [11].…”

Section: Introductionmentioning

confidence: 99%

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Waste Collection Area Generation Using a 2 Stage Cluster Optimization Process and GIS Data

2023

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The division of a city into several waste collection areas can have a large influence on the workload distribution put in place for the waste collection personnel. Additionally, areas with rapid urban development can benefit from a systematic way to automate the collection area division given that there is geographical information containing stop locations with an approximate number of dwelling units. This paper proposes a 2-stage collection area optimization process using the weighted K-means algorithm paired with differential evolution to minimize the standard deviation of dwelling units across each collection area. Results from a case study in The City of Oshawa, Canada prove that the proposed clustering techniques can yield a set of collection areas with 87.75% improvement compared to the current arrangement in terms of balancing the dwelling units. Additionally, the same clustering techniques can be used to assign collection routes for the vehicles in each area. A combination of Dijkstra's and Hierholzer's algorithms is applied to generate a route simulation with accompanying statistics regarding the total distance travelled, collection time, travel time, and fuel consumed. Specific to the case study in The City of Oshawa, each day of the week has 2 collection areas, and a genetic algorithm is used to find the optimal collection area pairs. Results from the collection area pairing show that there is a 38.04% and 37.54% improvement of simulated statistics for Week 1 and 2 collections, respectively.

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Section: Optimal Route Simulationsmentioning

confidence: 99%

Section: Optimal Route Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Waste Collection Area Generation Using a 2 Stage Cluster Optimization Process and GIS Data

2023

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“…Qiao et al proposed a comprehensive benefit maximization scheme balancing economic, environmental, and social factors by a two-phase meta-heuristic algorithm combining particle swarm optimization and tabu search (Qiao et al, 2020 ), but it was not applied in the actual case. Besides, the p-median method (Lv et al, 2020 ), LCA (Taşkin & Demir, 2020 ), saved footprint (Peri et al, 2018 ), voronoi graph (Xin et al, 2020 ) were also used for C&T network optimization. Furthermore, GIS is often applied to optimize sustainable C&T network in combination or alone (Chabok et al, 2020 ; Ferronato et al, 2020 ), because it can store, retrieve, analyze and apply large amounts of data, as well as the output visualization function with response time (Nguyen-Trong et al, 2017 ).…”

Section: Collection and Transportation Of Mswmentioning

confidence: 99%

Source separation, transportation, pretreatment, and valorization of municipal solid waste: a critical review

Zhang

Liu

Chen

et al. 2021

Environ Dev Sustain

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A survey of smart dustbin systems using the IoT and deep learning

Arthur,

Shoba,

Pandey

2024

Artif Intell Rev

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With massive population growth and a shift in the urban culture in smart cities, the constant generation of waste continues to create unsanitary living conditions for city dwellers. Overflowing solid waste in the garbage and the rapid generation of non-degradable solid waste produce a slew of infectious illnesses that proliferate throughout the ecosystem. Conventional solid waste management systems have proved to be increasingly harmful in densely populated areas like smart cities. Also, such systems require real-time manual monitoring of garbage, high labor costs, and constant maintenance. Monitoring waste management on a timely basis and reducing labor costs is scarcely possible, realistically, for a municipal corporation. A Smart Dustbin System (SDS) is proposed that is to be implemented in densely populated urban areas to ensure hygiene. This paper undertakes a comprehensive analysis of the application of smart dustbin systems, following an extensive literature review and a discussion of recent research that is expected to help improve waste management systems. A current SDS used in real-time is implemented with the most recent advances from deep learning, computer vision, and the Internet of Things. The smart dustbin system used in day-to-day life minimizes the overloading of bins, lowers labor costs, and saves energy and time. It also helps keep cities clean, lowering the risk of disease transmission. The primary users of the SDS are universities, malls, and high-rise buildings. The evolution of the SDS over the years with various features and technologies is well analyzed. The datasets used for Smart Waste Management and benchmark garbage image datasets are presented under AI perception. The results of the existing works are compared to highlight the potential limitations of these works.

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Reconfiguration of garbage collection system based on Voronoi graph theory: a simulation case of Beijing region

Cited by 4 publications

References 19 publications

Waste Collection Area Generation Using a 2 Stage Cluster Optimization Process and GIS Data

Waste Collection Area Generation Using a 2 Stage Cluster Optimization Process and GIS Data

Source separation, transportation, pretreatment, and valorization of municipal solid waste: a critical review

A survey of smart dustbin systems using the IoT and deep learning

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