The upsurge in urban population correlates with a heightened demand for municipal services, placing additional stress on waste management facilities. The increase in city residents invariably leads to a proportional rise in the generation of municipal solid waste, prompting the need for the development of supplementary waste disposal sites to accommodate the amplified volume ^[1].

The environmental consequences of mounting municipal solid waste (MSW) are substantial, playing a significant role in the exacerbation of climate change ^[2]. Inadequate waste disposal practices lead to the release of significant quantities of greenhouse gases, particularly methane, as organic waste decomposes in landfills. Moreover, the energy-intensive processes involved in waste treatment and disposal contribute to an increased carbon footprint, intensifying the overall environmental impact. Addressing these challenges through the adoption of sustainable waste management practices is imperative to mitigate adverse environmental effects and reduce the contribution to climate change.

Typical waste collection processes in metropolitan cities involve several key stages ^[3]. Residents generate waste through daily activities, resulting in the production of household waste comprising various materials. Waste bins or containers are strategically placed in residential areas, commercial districts, and public spaces to facilitate convenient waste disposal. Waste collection schedules are established, specifying the days and times when waste collection vehicles will visit designated areas for pickup. Specialized waste collection vehicles, such as garbage trucks, move through predetermined routes to collect waste from designated bins. In some cases, residents are required to separate their waste into different categories, such as recyclables and non-recyclables. Waste collectors may also perform additional sorting. Collected waste is transported to transfer stations or intermediate facilities, where it may undergo further sorting and processing. Waste is then transported to landfills or recycling facilities, depending on its nature. Recyclables are sent to recycling plants, while non-recyclables may be disposed of in landfills. The final step involves the proper disposal of waste, adhering to environmental regulations and waste management policies. Overall, an effective waste collection process in metropolitan cities integrates strategic planning, scheduled collections, sorting, and proper disposal to ensure the efficient management of diverse types of waste generated by a densely populated urban environment ^[4].

Advancements in sensors and wireless communications have paved the way for the integration of Internet of Things (IoT) networks in numerous smart city applications. Waste management can also harness the power of IoT to ascertain the quantity and composition of waste, transmit these data to waste collection organizations, and formulate optimized routes for efficient waste collection. Indeed, IoT can automate and improve the entire waste collection process ^[5].

In the context of MSW, the predominant use of road transport emerges as a notable environmental issue, accounting for a substantial portion exceeding 95% in both oil and fossil fuel consumption. This results in a considerable carbon dioxide (CO₂) footprint, marking it as a significant contributor to the emission of greenhouse gases. According to ^[6], In Europe, the transportation sector contributes to over 27% of the overall CO₂ emissions. Specifically, within this sector, road transport stands out as the most significant source of pollution. This alarming statistic underscores the pivotal role of road transport in environmental pollution, with tangible health implications such as lung cancer, asthma, allergies, and various respiratory problems, highlighting the pressing necessity for the adoption of sustainable waste management practices. Addressing this challenge in urban planning is imperative, necessitating strategic measures to effectively handle growing municipal waste and minimize adverse environmental effects ^[1].

Numerous studies have investigated both cost and route optimization problems to enhance the performance of operational efficiency. Cost optimization in solid waste management involves identifying and implementing strategies to minimize expenses while maintaining effective waste collection and disposal services. The first step is designing collection routes that minimize travel distances, reduce fuel consumption, and optimize the use of collection vehicles. Next, optimal allocating resources are required, such as personnel, vehicles, and equipment, effectively to enhance operational efficiency and reduce idle time. Another important factor in cost optimization is the implementation of technology, such as route optimization software, sensors, and monitoring systems, to streamline operations and identify areas for cost savings. Waste can also be efficiently sorted at the residential areas so that waste collection and processing cost can be reduced.

Vehicle route optimization is important part of solid waste management and requires the designing of efficient and cost-effective paths for waste collection. There are many important factors that should be considered while designing vehicle routes for waste collection. These include consideration of geographical layout of the city area, population of the area, traffic density, and current distribution of waste bins around the city ^[7]. Furthermore, vehicle routes should be dynamic and change with real-time data to maximize the waste collection efficiency. The optimization process extends to vehicle capacity planning, ensuring that collection vehicles are assigned routes aligning with their capacity to minimize unnecessary trips and vehicle idling. Predictive analytics play a crucial role by anticipating changes in waste generation, allowing for proactive adjustments to collection routes and schedules. Furthermore, the integration of advanced technology, including GPS tracking, sensors, and communication systems on collection vehicles, facilitates ongoing monitoring of route performance, vehicle status, and adherence to planned routes. Valuable insights are also drawn from customer feedback, enabling the identification of areas for route adjustments based on shifts in waste generation or specific collection requirements. This holistic approach to route optimization aims to enhance the overall effectiveness, efficiency, and sustainability of waste collection operations.

2. Internet of Things-Assisted Vehicle Route Optimization for Municipal Solid Waste Collection

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]^[8][9][10]. 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]^[11]. To address these challenges, numerous studies [12,14,15,16]^{[10][12][13][14]} 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]^[12]. GIS solutions utilize topological information as well as waste-related data, such as amount of waste and size of available bins. Authors in [12]^[10] utilized GIS-based analysis of land elevation and slope for route optimization to achieve fuel savings, and [17]^[15] applied GIS in the urban setting for transport route optimization. Stochastic vehicle routing, as applied by [18,19]^[16][17] 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]^[18]. 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]^[19]. 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]^[20]. 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]^[21], the Genetic Algorithm (GA), the Nearest Neighbourhood Search Algorithm [24]^[22], and Artificial Neural Networks (ANN) [25]^[23]. Researchers highlight the benefits of efficient MSW collection vehicle routing through the development of mathematical algorithms to address optimization challenges. The authors in [15]^[13] employed a mixed-integer programming model, reducing the total collection system distance by 23.47 percent. The work in [26]^[24] 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]^[25], 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. Similarly, the study in [28]^[26] addresses a complex recyclable waste collection problem, extending the vehicle routing problems with intermediate facilities. It introduces a unique combination of a fixed fleet, flexible depot assignment, and various constraints inspired by a real-world application. The proposed MILP model with enhanced valid inequalities faces challenges for larger instances, prompting the development of a multiple neighborhood search heuristic. The heuristic proves effective, achieving optimality on small instances, competitive performance for special cases, and significant savings in practical applications, notably in waste collection in Geneva, Switzerland. In [29]^[27], the pressing challenges of municipal solid waste management are addressed in rapidly urbanizing and developing markets. Introducing a coordinated framework for the vehicle routing problem, the research incorporates financial, environmental, and social considerations to achieve sustainability goals. The adaptive memory social engineering optimizer is introduced as a novel and superior optimization tool, outperforming simulated annealing and the social engineering optimizer. The findings emphasize practical solutions aligned with sustainability objectives in coordinated solid waste management, highlighting potential cost savings through increased recycling across multiple logistics echelons. The work in [30]^[28] addresses a real-life waste collection vehicle routing problem with time windows (VRPTW), incorporating multiple disposal trips and drivers’ lunch breaks. The extended Solomon’s insertion algorithm aims not only to minimize the number of vehicles and total traveling time, but also emphasizes route compactness and workload balancing—critical aspects in practical applications. To enhance these factors, a capacitated clustering-based waste collection VRPTW algorithm is introduced and successfully implemented in real-life waste collection problems. The study also provides a set of waste collection VRPTW benchmark problems.