A Genetic Algorithm for Flexible Job Shop Scheduling Problem with Scarce Cross Trained Setup Operators

Abstract

Many researchers developed algorithms for a dual-resource constrained flexible job shop (DRC-FJSP) where both machines and workers need to be simultaneously scheduled. In those models and algorithms in the literature, the authors assumed that workers are machine operators responsible for performing the production process steps from the beginning to the end of the operation. However, because of increased automation and the adoption of numerically controlled machines, workers become machine tenders and should not be bottleneck and constraining resources. On the other hand, skilled setup operators remain being constraining limited resources in industries. Unlike machine tenders, a setup operator can leave the machine once setup is completed and take on another setup operation on another machine. In this paper, for the first time, we develop a genetic algorithm for a new DRC-FJSP where setup operators and machine tools are constraining resources. Numerical examples of varying problem sizes are presented to show the performance of the algorithm.