Simulation and optimization of dynamic waste collection routes

Abstract

Smart waste collection strategies have been developed to replace conventional fixed routes with dynamic systems that respond to the actual fill-level of waste bins. The variation in waste generation patterns, which is the main driver for the profit of smart systems, is exacerbated in the United Arab Emirates (UAE) due to a high expatriate ratio. This leads to significant changes in waste generation during breaks and seasonal occasions. The present study aimed to evaluate a geographic information system (GIS)-based smart collection system (SCS) compared to conventional practices in terms of time, pollution, and cost. Different scenarios were tested on a local residential district based on variable bin filling rates. The input data were obtained from a field survey on different types of households. A knowledge-based decision-making algorithm was developed to select the bins that require collection based on historical data. The simulation included a regular SCS scenario based on actual filling rates, as well as sub-scenarios to study the impact of reducing the waste generation rates. An operation cost reduction of 19% was achieved with SCS compared to the conventional scenario. Moreover, SCS outperformed the conventional system by lowering carbon-dioxide emissions by between 5 and 22% for various scenarios. The operation costs were non-linearly reduced with the incremental drops in waste generation. Furthermore, the smart system was validated using actual waste generation data of the study area, and it lowered collection trip times by 18 to 42% compared to the conventional service. The present study proposes an integrated SCS architecture, and explores critical considerations of smart systems.

Optimization of Vehicle Routing for Waste Collection and Transportation

“…The traditional method of waste collection and transportation refers to taking all the waste bins and transport waste to the disposal station by trucks along the settled routes [ 38 ]. This process involves labor costs, fuel costs, maintenance costs, etc., so the cost is very high, accounting for most of the SWM spending [ 38 ].…”

“…The traditional method of waste collection and transportation refers to taking all the waste bins and transport waste to the disposal station by trucks along the settled routes [ 38 ]. 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 ].…”

“…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, 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. Mamun et al [ 17 ] presented a waste bin monitoring system, which is supported by a sensor technology and interaction system and the experimental results showed that the system can assist in an optimization model for route optimization.…”

A Chance-Constrained Vehicle Routing Problem for Wet Waste Collection and Transportation Considering Carbon Emissions

“…The research results show that the optimal threshold waste level of waste bin is between 70% and 75%, and the improved model and algorithm perform better in path optimization. Some researches apply smart waste bins in real cases, including glass bins in Geneva, Switzerland [18] and residential waste bins in the UAE [20]. Maurizio et al [11] regarded the amount of waste generated as a random variable.…”