Iterated greedy with variable neighborhood search for a multiobjective waste collection problem

Resumen

In the last few years, the application of decision making to logistic problems has become crucial for public and private organizations. Efficient decisions clearly contribute to improve operational aspects such as cost reduction or service improvement. The particular case of waste collection service considered in this paper involves a set of economic, labor and environmental issues that translate into difficult operational problems. They pose a challenge to nowadays optimization technologies since they have multiple constraints and multiple objectives that may be in conflict. We therefore need to resort to multiobjective approaches to model and solve this problem, providing efficient solutions in short computational times. In particular, we consider four different objectives to model the waste collection problem: travel cost, route length balance, route time balance, and number of routes. We propose an iterated greedy algorithm coupled with a variable neighborhood search to minimize an achievement function to determine a good approximation to the Pareto front. The performance of our method is empirically analyzed on a set of instances (both generated and real), and compared with the well-known NSGA-II and SPEA2 methods. The comparison favors our proposal.

Publicación
Expert Systems with Applications
Jesús Sánchez-Oro
Jesús Sánchez-Oro
Profesor Titular de Universidad

Profesor Titular del Departamento de Informática, siendo uno de los investigadores principales del Grupo de Investigación de Algoritmos para la Optimización GRAFO.

J. Manuel Colmenar
J. Manuel Colmenar
Artificial Intelligence Professor

Mis intereses de investigación se centran en las metaheurísticas aplicadas a problemas de optimización. He trabajado en diferentes problemas de optimización combinatoria aplicando algoritmos trajectoriales como GRASP o VNS. Además, estoy muy interesado en las aplicaciones de la Evolución Gramatical, específicamente en el dominio de los modelos y la predicción, como alternativa a los enfoques de aprendizaje automático.