| A Genetic Algorithm is developed to solve the total weighted tardiness scheduling problem in a job shop. This work implements statistical techniques, such as Fractional Factorial Design and Analysis of Variance to determine the Genetic Algorithms parameters such as population size, and rates at which operators are applied in the generation process of new populations. This Genetic Algorithm creates schedules by treating machines setup times separate from operations processing times. The algorithm is flexible enough to create schedules where different types of resource constraints such as setup crews and machinists are imposed. |
