Varela Arias, José Ramiro Rodríguez Vela, María del Camino
Metaheuristics Job Shop Scheduling Problem With Operators Total Flow Time Makespan Genetic Algorithms Memetic Algorithms Local Search
Máster Universitario en Soft Computing y Análisis Inteligente de Datos
The job shop scheduling is a challenging problem that has interested to researchers in the fields of Artificial Intelligence and Metaheuristics over the last decades. In this project, we face the job shop scheduling problem with an additional resource type (operators). This is a variant of the problem, which has been proposed recently in the literature. We start from a genetic algorithm that has been proposed previously to solve this problem and improve it in two different ways. Firstly, we introduce a modification in the schedule generation scheme in order to control the time of inactivity of the machines. Secondly we define a number of neighbourhood structures that are then incorporated in a memetic algorithm. In order to evaluate the proposed strategies, we have conducted an experimental study across a benchmark derived from a set of hard instances of the classic job shop problem.