Tandem Workshop Scheduling Based on Sectional Coding and Varying Length Crossover Genetic Algorithm

Abstract

For the tandem workshop scheduling problem, the objective of optimization is to obtain minimum total distribution time. To achieve that goal, we propose an optimization model, considering the rated load of automated guided vehicles (AGV) and the different regional transportation speeds. This model has three features. First, the sectional coding rules are adopted because materials need to be transported in batches between machines. Second, the crossover operation with varying length is used because the superior characteristics of the previous generation population could be better passed down to the offspring, thus accelerating the convergence rate of the population. Finally, the mutation operation combining insertion and reverse can maintains the diversity of the population and improve the local search ability of the algorithm. The tandem workshop scheduling problem can apply our algorithm, and the effectiveness of the improvement is demonstrated.