Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm

In this work, we seek to design a model that contributes the study and resolution of multi-objective rescheduling problem in dynamic permutation flow shop contexts. type problem, where objectives can be valued heterogeneous units, difficulty achieving an optimal solution leads finding set non-dominated efficient solutions (also called Pareto front). On other hand, will also consider potential appearance disruptions planned scheduling (such as machine breakdowns or arrival new priority jobs) require rapid re-planning aforementioned scheduling. paper, hybrid sorting genetic II metaheuristic (HDNSGA-II) is proposed find front. The algorithm applied benchmark already tested previous studies, defined by three conflicting objective functions (makespan, total weighted tardiness, stability) different types disruption (machine breakdowns, incorporation jobs, modifications process times). According statistical comparison performed, HDNSGA-II performs better designed environment, especially larger problems.