University of Las Palmas de Gran Canaria
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Modeling and solving an integrated periodic vehicle routing and capacitated facility location problem in the context of solid waste collection
Few activities are as crucial in urban environments as waste management. Mismanagement
of waste can cause significant economic, social, and environmental damage. However,
waste management is often a complex system to manage and therefore where computational
decision-support tools can play a pivotal role in assisting managers to make faster and better
decisions. In this sense, this article proposes, on the one hand, a unified optimization model
to address two common waste management system optimization problem: the determination
of the capacity of waste bins in the collection network and the design and scheduling of
collection routes. The integration of these two problems is not usual in the literature since
each of them separately is already a major computational challenge. Two improved exact formulations
based on mathematical programming and two metaheuristic methods are provided
to solve this proposed unified optimization model. It should be noted that the metaheuristics
consider a mixed chromosome representation of the solutions combining binary and integer
alleles, in order to solve realistic instances of this complex problem. Different parameters of
the metaheuristics considered – a Genetic Algorithm and a Simulated Annealing algorithm
– have been tested to study which combination of them obtained better results in execution
times on the order of that of the exact solvers. The achieved results show that the proposed
metaheuristic methods perform efficient on large instances, where exact formulations are not
applicable, and offer feasible, high-quality solutions in reasonable calculation times.371,0194,4Q1Q1SCIE11,
¿De verdad llegará el día en que nos atiendan auténticos robots camareros?
Estamos asistiendo a un progresivo crecimiento de la inteligencia artificial (IA). Bueno, en realidad, lo que ha crecido y se ha desplegado masivamente desde noviembre de 2022 es una de sus ramas: la IA generativa (IAGen).
Esta inteligencia artificial es capaz de generar textos, imágenes y vídeos, adaptados al formato y para el motivo que queramos. Por ejemplo, textos para promocionar productos en redes sociales, vídeos a partir de una idea e imágenes basadas en otras imágenes. ChatGPT (textos), MidJourney (imágenes) y Sora (vídeos) son tres de los ejemplos más representativos de esta generación de herramientas. Quizás por su auge, cada vez son más frecuentes las predicciones sobre la automatización del trabajo en el ámbito de los servicios. Una de sus consecuencias sería el reemplazo de trabajadores por herramientas de IA y robótica. Esta automatización sería total, si la tecnología reemplaza al trabajador, o parcial, cuando el reemplazo se produce solo en algunas actividades del puesto