The impact of an efficient collection sites location on the zoning phase in municipal solid waste management

Ghiani, Gianpaolo; Manni, Andrea; Manni, Emanuele; Toraldo, Massimiliano

doi:10.1016/j.wasman.2014.05.026

Cited by 52 publications

(39 citation statements)

References 24 publications

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“…We call it the threshold distance. This concept has been used regularly in the bibliography (Coutinho-Rodrigues et al, 2012;Di Felice, 2014a;Ghiani et al 2014b;Ghiani et al, 2012;Rossit et al, 2015;Tralhão et al, 2010) and is relevant in the design of a collection system accessible to users. The sets, parameters and variables used in this model are as follows, The IP model determining the collection sites to be opened and the location of the containers is presented in Eqs.…”

Section: Methodsmentioning

confidence: 99%

“…These problems intensify in developing countries, due mainly to budgetary and technological restrictions. In order to reduce these impacts and provide an efficient MSW management, there are some activities that must be adequately performed: collection, transformation and disposal (Ghiani et al, 2014b). The potential contribution of Operations Research to run more efficiently these operations is enormous.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An application of the augmented ε-constraint method to design a municipal sorted waste collection system

Rossit¹,

Tohmé²,

Frutos³

et al. 2017

10.5267/j.dsl

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The separation at the source of Municipal Solid Waste (MSW) is an initiative that facilitates the subsequent recycling work and contributes to palliate the negative impacts of the traditional unsorted collection system. This paper presents a multi-objective integer linear programming model of the determination of the optimal location of assorted waste bins in an urban area. We consider, jointly, the objectives of minimizing the investment cost and the average distance from the dwellings to the bins. The model was applied in simulated instances of an Argentinian medium-size city, contributing to the transition from the current door-to-door based system to a community bins system. To solve this problem, we apply both the weighting method, which has been used to solve similar problems in the literature, and a novel version of the augmented ε-constraint method (AUGMECON2). The results over simulated scenarios show that, in general, AUGMECON2 has a better performance, yielding a larger number of efficient solutions at lower computation times.Growing Science Ltd. All rights reserved. 7

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An application of the augmented ε-constraint method to design a municipal sorted waste collection system

Rossit¹,

Tohmé²,

Frutos³

et al. 2017

10.5267/j.dsl

View full text Add to dashboard Cite

show abstract

“…A similar problem was addressed by Ghiani et al [14], using a constructive heuristic for solving large instances in Nardò, Italy, that cannot be properly handled by exact methods implemented in CPLEX. Later [15], the heuristic was modified to bound posterior routing costs, e.g., not allowing the installation in a same GAP of bins that require different type of collecting vehicles. Di Felice [12] proposed a two-phase heuristic for the problem, for a real case in L'Aquila, Italy.…”

Section: Related Workmentioning

confidence: 99%

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

Toutouh

Rossit

Nesmachnow

2019

Ann Math Artif Intell

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This article describes the application of soft computing methods for solving the problem of locating garbage accumulation points in urban scenarios. This is a relevant problem in modern smart cities, in order to reduce negative environmental and social impacts in the waste management process, and also to optimize the available budget from the city administration to install waste bins. A specific problem model is presented, which accounts for reducing the investment costs, enhance the number of citizens served by the installed bins, and the accessibility to the system. A family of single-and multi-objective heuristics based on the PageRank method and two mutiobjective evolutionary algorithms are proposed. Experimental evaluation performed on real scenarios on the cities of Montevideo (Uruguay) and Bahía Blanca (Argentina) demonstrates the effectiveness of the proposed approaches. The methods allow computing plannings with different trade-off between the problem objectives. The computed results improve over the current planning in Montevideo and provide a reasonable budget cost and quality of service for Bahía Blanca.

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“…Collection patterns for routing and scheduling trucks to collect waste around cities, accounting for the time needed to collect waste as well as the selection of collection days, are discussed in Ghiani et al [17,18] and Ghose et al [19]. Districting decisions, in which the region to be served is divided into zones such that the total waste flow in each zone is less than the capacity of the trucks serving it, are presented in Ghiani et al [17,18] and Hanafi et al [20]. One previous study which considered frequency of service was Katragjini et al [21], although this paper only considered a fixed set of collection locations, with receptacles of fixed size, and required that all potential collection timetables be identified a priori.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints (13), (14), (15), (16), and (17) are linear constraints used to model dumpster collection costs through use of a piecewiselinear representation by computing the number of weekly services for dumpsters at collection point based upon the total number of days receiving dumpster service at this point. Constraint (18) then ensures that all type dumpsters at collection point are charged for service based on this number of weekly dumpster services at collection point . Constraint (19) ensures that the volume of waste assigned to compactors of type at collection point on day does not exceed the available capacity.…”

Section: Objective Functionmentioning

confidence: 99%

Planning Solid Waste Collection with Robust Optimization: Location-Allocation, Receptacle Type, and Service Frequency

Mehr

McGarvey

2017

Advances in Operations Research

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Consider the problem faced by a purchaser of solid waste management services, who needs to identify waste collection points, the assignment of waste generation points to waste collection points, and the type and number of receptacles utilized at each collection point. Receptacles whose collection schedule is specified in advance are charged a fixed fee according to the number of times the receptacle is serviced (emptied) per week. For other receptacles, the purchaser pays a fee comprised of a fixed service charge, plus a variable cost that is assessed on a per-ton-removed basis. We develop a mathematical programming model to minimize the costs that the purchaser pays to the waste management provider, subject to a level of service that is sufficient to collect all of the purchaser’s required waste. Examining historical data from the University of Missouri, we observed significant variability in the amount of waste serviced for nonscheduled receptacles. Because this variability has a significant impact on cost, we modified our model using robust optimization techniques to address the observed uncertainty. Our model’s highly robust solution, while slightly more expensive than the nonrobust solution in the most-optimistic scenario, significantly outperforms the nonrobust solution for all other potential scenarios.

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The impact of an efficient collection sites location on the zoning phase in municipal solid waste management

Cited by 52 publications

References 24 publications

An application of the augmented ε-constraint method to design a municipal sorted waste collection system

An application of the augmented ε-constraint method to design a municipal sorted waste collection system

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

Planning Solid Waste Collection with Robust Optimization: Location-Allocation, Receptacle Type, and Service Frequency

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