Dr. Elisabete Alberdi Celaya

University of the Basque Country, Bilbao, Spain.

Two optimization problems will be analyzed and solved using evolutionary algorithms.

In the first, the problem of municipal waste collection is modeled using a simple but efficient and especially easy to maintain the solution. Real data have been used, and it has been solved using a Genetic Algorithm (GA). Computations have been done in two different ways: using a complete random initial population, and including a seed in this initial population. Three problems of different sizes have been solved and, in all cases, a significant improvement has

been obtained.

In the second work, a cost-benefit mathematical model for maintenance is developed.

Maintenance has always been a key activity in the manufacturing industry because of its economic consequences. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, even though often these programs are complex to manage and understand. The developed model considers the evolution in quality and production speed, along with condition-based, corrective and preventive maintenance. The cost-benefit optimization is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case.