BIN-CT: Urban waste collection based on predicting the container fill level

Abstract

The fast demographic growth, together with the population concentration in cities and the increasing amount of daily waste, are factors that are pushing to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to optimally manage of the waste collection process, which represents 70% of the operational cost in waste treatment. In this article, we present a free intelligent software system called BIN-CT (BIN for the CiTy), based on computational learning algorithms, which plans the best routes for waste collection supported by past (historical) and future (predictions) data. The objective of the system is to reduction the cost of the waste collection service minimizing the distance traveled by a truck to collect the waste from a container, thereby reducing the fuel consumption. At the same time the quality of service for the citizen is increased, avoiding the annoying overflows of containers thanks to the accurate fill-level predictions given by BIN-CT. In this article we show the features of our software system, illustrating its operation with a real case study of a Spanish city. We conclude that the use of BIN-CT avoids unnecessary trips to containers, reduces the distance traveled to collect a container by 20%, and generates routes 33.2% shorter than the routes used by the company. Therefore it enables a considerable reduction of total costs and harmful emissions thrown up into the atmosphere.

Recyclables Collection Route Balancing Problem with Heterogeneous Fleet

“…The existing substantial literature particularly concentrates on the Vehicle Routing Problem for garbage trucks performing the service of solid waste collection. Practical aspects of application OR-based methods (the most frequently VRPs) for improving decision-making in SWM were studied using cases of different municipalities, e.g., Rio de Janeiro, Brazil [5]; Austin, Texas, USA [6]; Malaga, Spain [7]; Mashhad, Iran [8]; Cracow, Poland [9][10][11][12]; La Palma, Spain [13]; Ipoh City, Malaysia [14]; Wageningen, the Netherlands [15]; and many cities in Portugal [16].…”

Optimization of Vehicle Routing for Waste Collection and Transportation

“…A vehicle starts from the disposal center and goes back to the disposal center after visiting a waste bin, which is imposed by Equations (15)- (17) guarantees that the maximum capacity is respected by all routes. Constraint (18) ensures the collection service for the high priority waste bins while Constraint (19) eliminates sub-tours. Finally, Equation (20) specifies the types of the variables.…”

“…This process involves labor costs, fuel costs, maintenance costs, etc., so the cost is very high, accounting for most of the SWM spending [ 38 ]. For these reasons, a lot of research proposed an approach to collect waste according to the filling levels of waste bins, which are predicted based on either historical data [ 7 , 17 , 39 ] or sensory data [ 18 , 38 ] obtained from waste bins and trucks [ 38 ]. Abdallah et al [ 38 ] developed a selection procedure for waste bins to be collected, which have high filling levels based on historical data.…”

“…For these reasons, wireless sensor networks (WSN) have been deployed in MSW to achieve remote monitoring filling levels of waste bins [17]. In the meantime, waste collection trucks can communicate with waste bins with sensors by the Internet of things system to acquire the data about the status of bins [18][19][20].…”

Random error sampling-based recurrent neural network architecture optimization

“…We tested RESN on four problems: the sine wave, the waste generation prediction problem [9], the coal-fired power plant flame intensity prediction problem [10], and the EUNITE load forecast problem [11]. Also, we proposed four experiments: E1: RESN vs. Gradient-based Architecture Optimization, we compared RESN against (i) a modified version of the same algorithm (i.e., a version that replaced the MRS by the results [13].…”