Research into waste management started a few decades ago. However, there has been an increase in the number of publications since 2000 (Beliën et al., 2014). Researchers have examined solid waste management with a particular focus on vehicle routing. Nuortio et al. (2006) worked on optimizing vehicle routes and schedules for municipal solid waste collection in Eastern Finland and found that a significant reduction in waste collection costs could be obtained. Buhrkala et al. (2012) studied the routing of waste collection vehicles to find a cost-effective and optimal route for collection trucks, considering that all bins have to be emptied within a specified time window to satisfy customer demand.
Other researchers have focused on assessing the efficiency of the waste collection process. For instance, Guerrini et al. (2017) examined the effect of many key variables on the efficiency of the municipal waste collection services in the Province of Verona, Italy. The team collected data for five years between 2008 and 2012 and compared the efficiency of different municipalities. They found that properly organizing collection routes and frequencies, with a suitable allocation of trucks for a specific route, could improve the efficiency of the operations.
On the other hand, Ferreira et al. (2017) focused on assessing and benchmarking various municipal waste collection schemes. They highlighted the efficiency differences between schemes, which may help in improving waste management strategies. The study monitored three performance indicators: effective collection distance, effective collection time, and effective fuel consumption. These indicators were considered crucial for the efficiency and costs-effectiveness of waste collection for each collection scheme.
Many methods have been used to optimize waste collection. Bautista et al. (2008) found a solution for the waste collection problem in the municipality of Sant Boi de Llobregat in Barcelona using ant colony heuristics that reduced operating costs and acoustic contamination. Santos et al. (2008) designed a spatial decision support system that creates routes as a solution for multiple-vehicle routing problems. This decision support system includes a geographical information system (GIS) and heuristics, and incorporates real details such as time constraints, routing constraints, shift durations, and vehicle capacities. After designing the system, the team tested it for collecting waste in Coimbra, Portugal. They concluded that this system can be of significant help in analyzing and solving many complicated vehicle routing problems as well as providing benefits and cost reductions. The system can help in devising a more efficient waste collection scheme.
Optimizing waste collection routes has often been formulated as a traveling salesman problem (TSP). Das and Bhattacharyya (2015) focused on minimizing the length of municipal waste collection routes and proposed a heuristic solution to optimize municipal solid waste collection and transportation using the TSP. The result was a reduction of 30% in the length of the overall waste collection path. Jakubiak (2016) also tried to improve the collection of municipal waste by analyzing four routes used by the Municipal Cleaning Service in Krakow, Poland. The author focused on minimizing the distance covered by collection trucks and used a solution to the TSP to show that it was possible to shorten significantly the distance covered with an optimized routing schedule.
Ombuki-Berman et al. (2007) studied the routing of waste collection vehicles. They incorporated a time window, the multiple disposal trips of real waste collection systems, and staff lunch breaks, which made the problem more challenging. The team presented a multi-objective genetic algorithm for waste collection based on benchmarked data from the real world. Farrokhi-Asl et al. (2018) solved a multi-objective sustainable waste collection problem. They formulated three objective functions that included both operational and social costs. The model was used to evaluate fuel consumption, CO2 emissions, and the impact on the environment. These have been some one of the few researchers to have considered the impact of waste collection on the environment.
Furthermore, other researchers have looked at the impact of using information technology, the IoT, and sensors. Milić and Jovanović (2011) considered waste collection as a dynamic vehicle routing problem. Not all information relevant to vehicle routing is known initially, and routes can be changed as more information becomes available. The system used mobile technology to monitor the current load of a collection truck in real time. The data collected were used to identify better routes to enhance collection efficiency. This dynamic collection methodology is more flexible for routing, providing better collection solutions that can accommodate instant changes.
IoT-enabled systems have been modeled to find optimal policies. For instance, Rovetta et al. (2009) implemented a network of waste bins equipped with sensors all linked to a data management system in Pudong, China, to monitor the overall and the bin-specific amount of waste generated and to identify the types of waste material. This helped to identify potentially hazardous materials and the data collected were used to optimize truck collection routes to minimize the costs of collection.
Mes et al. (2014) developed an IoT-enabled collection policy for underground containers equipped with sensors. They proposed heuristics with various parameters that were tuned depending on the requirements using optimal learning techniques and a simulation. An important part of their work was that they divided the containers into three different groups based on the level of waste: MustGo containers, MayGo containers, and NoGo containers. As the name indicates, a MustGo container needs to be emptied as soon as possible and it should be incorporated in that day’s routing plan. A MayGo container can be emptied if it is on the MustGo route plan or nearby, and it holds a sufficient amount of waste. A NoGo container is not incorporated into that day’s routing plan. They tested their solution using real-world data from a company in the Netherlands and found that with optimized parameters the cost savings could be as high as 40%.
Faccio et al. (2011) introduced an innovative vehicle routing model combined with real-time traceability data to find an optimized solid waste collection system. The real-time data were collected using different technologies, such as volumetric sensors, RFID, and weighing systems. The research had three objectives: minimize the number of vehicles per fleet, minimize travel times, and minimize the total distance covered. The authors conducted an economic feasibility study, which proved that the benefits of using the optimized routing method covered the costs of implementing the technology.
Anagnostopoulos et al. (2015) introduced a new approach by defining high-priority bins in predetermined critical areas in Saint Petersburg in Russia. Such areas require time-critical waste collection and could be hospitals, tourist sites, or the town hall. The authors developed four different models to ensure the speedy collection from these high-priority bins: the dedicated truck model, the detour model, the minimum distance model, and the reassignment model. The aim was to minimize the time needed for waste collection to reduce the possible negative effects of overfilled bins on citizens. The models were compared and a summary of the cases for which each model performs best was presented.
Sharmin and Al-Amin (2016) developed a cloud-based system that uses the ant colony optimization method to find an optimal waste collection route. They used sensors to monitor the waste level in bins and to establish a usage pattern to improve the planning of waste collection. The system is flexible and dynamic and can handle changes in waste generation patterns or road traffic. Johansson (2006) examined the effect of different scheduling and routing strategies for solid waste collection. He assessed four collection policies: (1) static scheduling and static routing, (2) dynamic scheduling and dynamic routing to full containers, (3) dynamic scheduling and dynamic routing to almost full containers, and (4) static scheduling and dynamic routing to almost full containers. The study concluded that the dynamic scheduling and routing policies have lower operating costs and shorter collection distances compared with the static policies.
Blazquez and Paredes-Belmar (2020) worked on designing a domestic waste collection system that is composed of two stages. The first stage is a location-allocation problem solved using MILP and the second is a VRP in which Large Neighborhood Search heuristics are used to determine efficient collection routes. A case study for the commune of Renca in Santiago, Chile was analyzed and the researchers compared the designed bin to bin collection system with the existing door to door collection system and found that the former performed more efficiently in terms of the total daily distance traveled and the average work shift duration.
Vu et al. (2020) modeled a waste collection system in Texas and studied the inter-relationships of its parameters. The inter-dependency between collection frequency, collection type, waste composition, and truck compartment configurations was investigated using 48 scenarios. They found that travel time and distance can be saved by increasing the waste density and its collection frequency. Moreover, the use of dual compartments trucks were proven to be beneficial in reaching a more efficient collection system.
The objectives of most of the reviewed papers were to find optimal routes to collect the domestic waste at the least possible cost. While the majority focused on optimizing the process from an economic perspective, few researches also included the impact of this process on the environment. The contribution of this paper resides in: (1) quantifying the environmental impact of the proposed waste collection policies, (2) measuring the impact on the citizen satisfaction which was overlooked in previous papers, and (3) assessing these policies based on the three performance measures: economic, environmental, and citizen satisfaction.