This article reviews the literature surrounding innovative computational tools for waste management within smart cities. With the rise of urbanization and the increasing challenges of waste management, innovative technologies play a pivotal role in optimizing waste collection, sorting, recycling, and disposal processes. Leveraging computational tools such as artificial intelligence, Internet of Things, and big data analytics, smart waste management systems enable real-time monitoring, predictive modeling, and optimization of waste-related operations. These tools empower authorities to enhance resource efficiency, minimize environmental impact, and improve the overall quality of urban living. Through a comprehensive review of recent research and practical implementations, this article highlights the key features, benefits, and challenges associated with the development of cutting-edge computational tools for waste management. Emerging trends and opportunities for research and development in this rapidly evolving field are identified, emphasizing the importance of integrating technological innovations for building sustainable and resilient waste management in smart cities.

This paper proposes a real mathematical constraint satisfaction model which defines the timetabling problem in the Faculty of Chemical Sciences and Engineering (FCSE) at the Autonomous University of Morelos State, Mexico. A Constructive Approach Algorithm (CAA) is used to obtain solutions in the proposed model. A comparison is made between the CAA’s results and the schedule generated by the FCSE administration. Using the constraint satisfaction model, it is possible to improve the allocation of class hours in the FCSE so that classroom use is more efficient.

Planning corridors for new facilities such as pipeline or transmission lines through geographical spaces is a topographical constraint optimization problem. The corridor planning problem requires finding an optimal route or a set of alternative paths between two locations. This article presents a simulated-annealing-based (SA) approach applying a variable neighborhood strategy in a continuous space to generate competitive and different alternative paths to solve the corridor planning problem. The variable neighborhood method randomly selects two points from a variable interval of the current solution generated by SA creating pseudo-random paths inside a corridor and finding spatially different alternatives. The proposed approach is evaluated with three practical problems using real topographic data from the Veracruz Basin in Mexico. The experimental results show that this approach obtains efficient and competitive solutions with improvements above 18% over those gotten by the compared method.