# Internet of Things-Assisted Vehicle Route Optimization for Municipal Solid Waste Collection
## 2 Related Work  
Several works in the literature have worked on the problem of optimizing the route of vehicles for municipal waste collection. The primary objective is to devise optimal route designs for vehicle fleets, minimizing the total travel distance and, consequently, reducing overall driving costs [9; 10]. Effective routing is pivotal in the planning and definition of paths for waste collection trucks, and the absence of technology in route selection can result in inefficient and costly collection systems [10; 11; 12]. In developing countries, the scheduling of trucks for the collection of waste often lacks organization and systematic planning, relying on intuitive methods and practical experiences with several negative consequences, as pointed out by [13]. To address these challenges, numerous studies [12; 14; 15; 16] have delved into optimizing solid waste collection through the application of Geographical Information System (GIS)-based techniques. These approaches aim to ensure better management of resources and reduce negative impacts on the environment. GIS emerges as a valuable tool for determining cost-effective solid waste collection solutions, as emphasized by [14]. GIS solutions utilize topological information as well as waste-related data, such as amount of waste and size of available bins. Authors in [12] utilized GIS-based analysis of land elevation and slope for route optimization to achieve fuel savings, and [17] applied GIS in the urban setting for transport route optimization. Stochastic vehicle routing, as applied by [18; 19] models the problem considering randomness in the network. In the context of MSW collection, the stochastic approach does not consider various critical parameters, such as user numbers for a bin, social behaviors, demographic contexts influencing waste generation, and other pertinent factors. On the other hand, capacitated vehicle routing imposes constraints based on vehicle capacity in terms of weight and volume of waste and considers it for vehicle trip planning [20]. Additionally, the vehicle routing with time windows introduces defined time periods or windows during which waste should be collected. Time windows are crucial in ensuring that vehicles adhere to a fixed schedule with predetermined start and finish timings during the workday [21]. Studies have also explored optimal routing for solid waste collection trucks, employing sensors such as a Global Positioning System (GPS) sensor, a volume and flow sensor, and a Radio Frequency Identification (RFID) sensor. GIS, a widely adapted software, facilitates automated collection by tracking bin and vehicle locations and collection times. However, it falls short in estimating bin statuses and waste levels [22]. Further research has focused on mathematical programming using heuristic approaches to address the complexity of conventional methods. Such approaches include Particle Swarm Optimization (PSO) [23], the Genetic Algorithm (GA), the Nearest Neighbourhood Search Algorithm [24], and Artificial Neural Networks (ANN) [25]. Researchers highlight the benefits of efficient MSW collection vehicle routing through the development of mathematical algorithms to address optimization challenges. The authors in [15] employed a mixed-integer programming model, reducing the total collection system distance by 23.47 percent. The work in [26] formulated the MSW collection route problem into a mixed-integer program, achieving a reduction of over 30% in the overall length of the garbage collecting path. In [27], a waste collection problem incorporating a midway disposal pattern is addressed. A hybrid artificial bee colony (ABC) algorithm is proposed, demonstrating superior optimum-seeking performance compared to other metaheuristics. The study validates the effectiveness of the hybrid approach, emphasizing that the midway disposal pattern reduces carbon emissions by up to 7.16% in practical instances.