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 Bahia 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 Bahia Blanca.
A similar problem was addressed by @cite_27 , using a constructive heuristic for solving large instances in Nard o , Italy, that cannot be properly handled by exact methods implemented in CPLEX. Later @cite_13 , 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 @cite_29 proposed a two-phase heuristic for the problem, for a real case in LAquila, Italy. The first phase solved the location of the GAPs thorough a constructive heuristic, while the second determined the quantity and size of bins needed for each GA,P according to the number of waste generators served by that GAP. A similar heuristic was applied by Boskovic and Jovici @cite_31 for Kragujevac, Serbia, using the ArcGIS Network Analyst. Since bins location is a problem that uses spatial information, other authors relied on Geographic Information Systems to gather and analyze data. For example, @cite_33 used a constructive heuristic to establish the GAPs sequentially according to some priorities in order to cover an studied area in Dundas, Canada.
@cite_33 @cite_29 @cite_27 @cite_31 @cite_13
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A location-allocation model contained within a geographic informa­ tion systems (GIS) software package was used to design a recycling depot scheme for a community of 22,000 people. Considered a less expensive alternative to curb­ side recycling, the depot scheme would receive a variety of recyclable materials from the public on a voluntary basis. Depot sites were located using a model that maximized the coverage of a depot site, with constraints based on projected "re­ cycler behavior." Shopping centers, municipal parking lots, and roadside sites were candidate locations for recycling depots in the two modeled cases. A GIS-based approach is shown to be useful for determining the number and location of material recycling depots for use within an integrated municipal solid waste management system. Abstract The paper reports about a pilot study that gives a numerical solution to the solid waste accumulation problem (SWAP). The purpose is to show both a simple and effective way to implement the theory using the technology of the Spatial DataBase Management Systems (SDBMSs), and the versatility of the proposed solution from the point of view of those responsible for the MSW management who, in fact, are offered a dual-mode display of the results: one tabular (typical of relational databases) and the other based on geographical maps, the latter particularly useful to highlight the spatial component of the data of the SWAP. Abstract Urban waste management is becoming an increasingly complex task, absorbing a huge amount of resources, and having a major environmental impact. The design of a waste management system consists in various activities, and one of these is related to the location of waste collection sites. In this paper, we propose an integer programming model that helps decision makers in choosing the sites where to locate the unsorted waste collection bins in a residential town, as well as the capacities of the bins to be located at each collection site. This model helps in assessing tactical decisions through constraints that force each collection area to be capacitated enough to fit the expected waste to be directed to that area, while taking into account Quality of Service constraints from the citizens’ point of view. Moreover, we propose an effective constructive heuristic approach whose aim is to provide a good solution quality in an extremely reduced computational time. Computational results on data related to the city of Nardo, in the south of Italy, show that both exact and heuristic approaches provide consistently better solutions than that currently implemented, resulting in a lower number of activated collection sites, and a lower number of bins to be used. This paper concerns the development of a methodology aimed at determining the optimal number of waste bins as well optimizing the location of collection points. The methodology was based on a geographic information system, which handled different sets of information, such as street directions, spatial location of objects and number of inhabitants, location of waste bins, and radius of their coverage. The study was conducted in a district in the central area of the city of Kragujevac. Due to a lack of information about the existing situation, all necessary data was collected by fieldwork and by using GPS equipment. By using the developed methodology, the results indicated a reduction of 24 in the number of collection points and 33.5 in the number of waste bins, without reducing the quality of the provided services. It has led to cost and time savings for waste collection and environmental benefits. All users of the services were covered within a 75-m radius, and the usage of bins is more efficient. According to the reduction in the number of waste bins, a total amount of €26,000 may be achieved. In addition, the time for waste collection was reduced, resulting in a €1700 saving per year in fuel costs, as well as 4.5 tons of emitted CO2 into the atmosphere. Abstract In this paper, we study two decisional problems arising when planning the collection of solid waste, namely the location of collection sites (together with bin allocation) and the zoning of the service territory, and we assess the potential impact that an efficient location has on the subsequent zoning phase. We first propose both an exact and a heuristic approach to locate the unsorted waste collection bins in a residential town, and to decide the capacities and characteristics of the bins to be located at each collection site. A peculiar aspect we consider is that of taking into account the compatibility between the different types of bins when allocating them to collection areas. Moreover, we propose a fast and effective heuristic approach to identify homogeneous zones that can be served by a single collection vehicle. Computational results on data related to a real-life instance show that an efficient location is fundamental in achieving consistent monetary savings, as well as a reduced environmental impact. These reductions are the result of one vehicle less needed to perform the waste collection operations, and an overall traveled distance reduced by about 25 on the average.