A genetic optimization algorithm and perceptron learning rules for a bi-criteria parallel machine scheduling

Abstract

This work considers scheduling problems minding the setup and removal times of jobs rather than processing times. For some production systems, setup times and removal times are so important to be considered independent of processing times. In general, jobs are performed according to the automatic machine processing in production systems, and the processing times are considered to be constant regardless of the process sequence. As the human factor can influence the setup and removal times, when the setup process is repetitive the setup times decreases. This fact is considered as learning effect in scheduling literature. In this study, a bi-criteria m-identical parallel machines scheduling problem with learning effects of setup and removal times is considered. The learning effect is proposed using a perceptron neural network algorithm. The objective function of the problem is minimization of the weighted sum of total earliness and tardiness. A mathematical programming model is developed for the problem, which is NP-hard. Results of computational tests show that the LINGO 9 software is effective in solving problems with up to 25 jobs and five machines. Therefore, for larger sized problems, a genetic algorithm for optimization is developed.