Advanced planning and scheduling with a learning effect in the flexible job shop manufacturing system

Advanced planning and scheduling is a production management process in which the resources and production capacities are optimally assigned to the customers’ demands. This approach can particularly be applicable in the complex environments. A common assumption in the advanced planning and scheduling problems is that the processing time of a given product is constant and independent of its position in the production sequence. However, in the real-world situations, an operator’s skill may continuously be improved when the production time is passing which is known as the learning effect phenomenon. In this article, with regard to the learning effect, an extended multi-product optimization framework for the advanced planning and scheduling problem of a typical flexible production environment is developed to provide a more ability to address the actual situations. Due to the high computational complexity of the proposed model, a multi-stage genetic solution algorithm is also presented. Numerical results confirm that the proposed algorithm can obtain the optimum/near optimum solutions in much less computational times compared to the exact solutions.