Using applied operations research and geographical information systems to evaluate effective factors in storage service of municipal solid waste management systems

Solving the bin location-allocation problem for household and recycle waste generated in the commune of Renca in Santiago, Chile

In the commune of Renca in Santiago, Chile, the household waste is currently collected on the kerbside and then thrown in the rear of a compactor truck. This system becomes inefficient when not all citizens are always serviced, yielding high overall collection costs and negative impacts on the environment and the society. Recently, recyclable waste collection sites have been situated throughout the commune, which need to be examined with respect to population coverage and average travel distances to these sites. This study employs mixed-integer linear programming models and geographic information systems to solve the bin location-allocation problem for household and recyclable waste separately. The results are shown for different values of waste generation, bin capacities, and travel distances, in addition to considering the users' and municipality's preferences in the decision-making process. The proposed recyclable waste bin locations present a more efficient solution than the existing collection sites in the commune of Renca since more users are serviced within a shorter travel distance to dispose of their recyclable waste.

Network design of a household waste collection system: A case study of the commune of Renca in Santiago, Chile

This study proposes a design of a household waste collection system based on a two-stage procedure. First, the bin location-allocation problem is solved by selecting collection sites from a set of potential sites, and determining the type and number of bins at each selected collection site. Second, bin-to-bin waste collection routes are obtained for a fleet of homogeneous vehicles that are restricted by either work shift duration or vehicle capacity. Mixed integer linear programming (MILP) models are proposed for both stages, considering the particular characteristics of the problem. The models are applied to a real-world instance in the commune of Renca in Santiago, Chile. The results of first stage indicate an important preference for small bins since they have a lower unitary cost. Due to the large size of the real instance, a Large Neighborhood Search (LNS) heuristic is used in the second stage to find good feasible vehicle routing solutions in a reasonable period of time. The results for the routing phase suggest a larger number of routes in the morning work shift since these routes have shorter distances. The LNS heuristic presents a satisfactory behavior when compared to the MILP model with small instances. The proposed bin-to-bin household waste collection vehicle routing presents a more efficient solution than the existing door-to-door waste collection in the commune of Renca with respect to the total daily traveled distance and the average work shift duration. Finally, a sensitivity analysis is presented and discussed for both models.

Exact and heuristic approaches for multi-objective garbage accumulation points location in real scenarios

Municipal solid waste management is a major challenge for nowadays urban societies, because it accounts for a large proportion of public budget and, when mishandled, it can lead to environmental and social problems. This work focuses on the problem of locating waste bins in an urban area, which is considered to have a strong influence in the overall efficiency of the reverse logistic chain. This article contributes with an exact multiobjective approach to solve the waste bin location in which the optimization criteria that are considered are: the accessibility to the system (as quality of service measure), the investment cost, and the required frequency of waste removal from the bins (as a proxy of the posterior routing costs). In this approach, different methods to obtain the objectives ideal and nadir values over the Pareto front are proposed and compared. Then, a family of heuristic methods based on the PageRank algorithm is proposed which aims to optimize the accessibility to the system, the amount of collected waste and the installation cost. The experimental evaluation was performed on real-world scenarios of the cities of Montevideo, Uruguay, and Bahía Blanca, Argentina. The obtained results show the competitiveness of the proposed approaches for constructing a set of candidate solutions that considers the different trade-offs between the optimization criteria.

Statistical analysis of effective variables on the performance of waste storage service using geographical information system and response surface methodology

Waste storage service (WSS) of Municipal Solid Waste Management (MSWM) systems is a functional element that residents encounter system directly, so related authorities should consider a trade-off between social consideration and system performance at this stage. Not only should they pay attention to the efficiency and effectiveness of the MSWM system, but also take account of social welfare resulting in public engagement. This study introduces three important factors including number of waste stations, maximum allowed walking distance and container capacity devoted to each station having effect on the performance of waste storage service. To investigate how these variables affect the performance of WSS, geographical information system (GIS) and response surface methodology (RSM) were applied. First, according to these three variables, fifteen experiments were designed by Box Behnken Design (BBD), then, all the experiments were modeled by maximized capacitated coverage (MCC) in GIS environment, and the parameters evaluating the performance of WSS were measured. The final response was achieved through integration of effective parameters by two different MADM methods, TOPSIS and OWA. The results showed negative effects of the number of stations and container capacity of each station on the final response, whereas increase in the maximum allowed walking distance improved the performance of WSS.