Professor Morillo has worked in the last years on the optimization of scheduling problems. They consist, in a general way, of determining the optimal allocation of resources to different activities that make up a project to achieve a specific objective. For instance, the minimization of the total duration of the project. The activities are subject to resource consumption restrictions and precedence relationships. To solve this problem, Professor Morillo proposed a hybrid Branch and Bound algorithm that includes various heuristic rules to bound the search space. Later, he proposed a genetic algorithm whose main contribution was a new mutation operator. In addition, this research studied the effect of an unwanted phenomenon in the search called the generation of redundant solutions and demonstrated how the mutation operator can be used to combat it. Afterwards, he proposed an extension to the multimodal resource-constrained project scheduling problem, which included two major contributions: first, a component of energy consumption was included in the activities; and second, a new objective function was proposed, which considered both the minimization of project time and the minimization of energy consumption, but without establishing a multi-objective criterion. Additionally, a library of test problems was proposed to evaluate future solution methods. Finally, Professor Morillo proposed a new evolutionary algorithm that improves the search by focusing on generating neighbourhoods of solutions based on the activity execution modes.
Honors and Memberships
Operations Management and Modeling Group (MGO); Inteligencia Artificial, Planificación y Scheduling (AI-GPS).