# Internet of Things-Assisted Vehicle Route Optimization for Municipal Solid Waste Collection
## 2 Related Work
Similarly, the study in [28] 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], 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] 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. In this investigation, our central focus centers on tackling the route optimization challenge in the realm of solid waste management. Acknowledging the intricate nature of decision-making within this context, the TOPSIS algorithm is employed. TOPSIS proves to be instrumental in scenarios where decision makers grapple with the evaluation of alternatives across multiple criteria, each carrying diverse degrees of significance. This algorithm provides a methodical and quantitative approach to rank and select alternatives, gauging their overall performance. It takes into consideration the preferences and priorities of decision makers, ensuring a comprehensive and well-informed decision-making process. In the specific context of waste collection, TOPSIS is used to optimize routes by meticulously considering a range of factors. These factors encompass the volume of waste slated for collection, the toxicity levels associated with distinct waste types, the time efficiency inherent in each route, and the overall distance covered. By seamlessly integrating TOPSIS into our route optimization framework, our objective is to augment the efficiency of waste collection operations. The algorithm empowers us to make judicious decisions regarding the most efficient routes based on a nuanced understanding of diverse criteria. This involves prioritizing routes that minimize travel distance, maximize the collection of high-volume waste, reduce the time required for collection, and account for the environmental impact of toxic waste.