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A Genetic Algorithm for Scheduling Laboratory Rooms: A Case Study

Abstract:

Genetic algorithms (GAs) are a great tool for solving optimization problems. Their characteristics and different components based on the principles of biological evolution make these algorithms very robust and efficient in this type of problem. Many research works have presented dedicated solutions to schedule or resource optimization problems in different areas and project types; most of them have adopted GA implementation to find an individual that represents the best solution. Under this conception, in this work, we present a GA with a controlled mutation operator aiming at maintaining a trade-off between diversity and survival of the best individuals of each generation. This modification is supported by an improvement in terms of convergence time, efficiency of the results and the fulfillment of the constraints (of 29%, 14.98% and 23.33% respectively, compared with state-of-the-art GA with a single random mutation operator) to solve the problem of schedule optimization in the use of three laboratory rooms of the Mechatronics Engineering Career of the International University of Ecuador.