CONICET | Buscador de Institutos y Recursos Humanos

In a context of increasingly population concentration in limited areas the pressure over governments to find intelligent and efficient solutions to provide good quality services is at its peak. In this sense the extit{Smart cities} paradigm are known for using Information and Communication Technologies (ICT) to enhance the life quality of citizens. Therefore, several urban activities have been addressed with ICTs (see Neirotti et al., 2014). Among them, it is Municipal Solid Waste (MSW) (see, e.g., Nesmachnow et al, 2018; Toutouh et al., 2019), where ICT can help to mitigate the environmental and economic problems associated with MSW management. These problems are more serious in developing countries, such as Argentina, due to budgetary and technological restrictions.For having an efficient MSW system several activities have to be performed adequately: generation, collection, treatment, and recovery or final disposal. This work aims to revise some works that have been performed regarding the initial stages of the reverse logistic chain of MSW, i.e., generation and collection. In the generation stage we find works that aim to locate Garbage Accumulation Points (GAPS), i.e., special places where waste bins are installed so citizens can carry their waste, in proper locations. This is a relevant problem to guarantee a good service to citizens, because a paltry spatial distribution of waste bins in the city may lead to not fulfilling the needs of residents and/or affecting the quality of the service (e.g., people must walk long distances for garbage disposal, or certain waste bins fill quickly while others remain empty). Thus, this problem has a multiobjective nature where both cost for the authorities and Quality of Service (QoS) provided to the citizens have to be taken into account. Finding appropriate locations for waste bins is a variation of the Facility Location Problem, which is proved to be NP-hard. Therefore, both exact (Rossit et al., 2018) and heuristics algorithms (Toutouh et al, 2019) are proposed. These algorithms were tested on different real world scenarios of Bah´{i}a Blanca, Argentina, and Montevideo, Uruguay. Then also the collection of waste accumulated in the bins was addressed. Being again an NP-hard problem, a Multiobjective Evolutionary Algorithm (MOEA) is proposed in order to achieve both, a reduction in the cost of the collection plan and a maximization in the QoS. Again the algorithm was tested in real world scenarios of Montevideo, where it outperformed the current routing strategy of the City Hall.Finally, also new lines of study are depicted in this work. These involve improving the exact method when dealing with the multiobjective GAP location problem with the introduction of a novel variation to the augmented $arepsilon$-constraint method. Additionally, initial attempts to include uncertainty in MOEAs for waste collection are also presented.