Genetic Algorithm with Local Search for Advanced Planning and Scheduling

In this paper, a genetic algorithm approach with a novel mutation operator based on perturbation and local search has been proposed to solve an advanced planning and scheduling (APS) model in manufacturing supply chain, in which each customer order has a due date, each operation could be performed on alternative machines. The objective is to minimize the makespan of each customer order while ensuring the due date constraints. Various sizes of numerical experiments were carried out to demonstrate the efficiency of the proposed GA and the results indicate that the presented algorithm performs much better than previous work especially in large size problems.