The flow shop scheduling problem has been widely studied in recent years, but the research on multi-objective flow shop scheduling with green indicators is still relatively limited. It is urgent to strengthen the research on effective methods to solve such interesting problems. To consider the economic and environmental factors simultaneously, the paper investigates the multi-objective permutation flow shop scheduling problems (MOPFSP) which minimizes the makespan and total carbon emissions. Since MOPFSP is proved to be a NP-hard problem for more than two machines. A hybrid cuckoo search algorithm (HCSA) is proposed to solve the problems. Firstly, a largest-order-value method is proposed to enhance the performance of HCS algorithm in the solution space of MOPFSP. Then, an adaptive factor of step size is designed to control the search scopes in the evolution phases. Finally, a multi-neighborhood local search rule is addressed in order to find the optimal sub-regions obtained by the HCSA. Numerical experiments show that HCSA can solve MOPFSP efficiently.

The product quality and production efficiency of a flexible manufacturing system have improved effectively by introducing the computer management and the material transportation system. The flexible manufacturing system performance greatly depends on the performance of the material transportation system. As a mobile robot controlled by a central controller, an automated guided vehicle has a strong ability for material transportation. This article studies a dynamic scheduling problem in a shop floor, where machines and automated guided vehicles run at a specified speed and specifies a mathematical model for the dynamic scheduling problem with the goal of makespan minimization. Meanwhile, inspired by the hormone secretion principle of the endocrine system, a bio-inspired scheduling optimization approach is developed to solve the dynamic scheduling problem in the flexible manufacturing system. To verify its practical application, the bio-inspired scheduling optimization approach and other scheduling approaches are tested, and the results illustrate that the bio-inspired scheduling optimization approach has better scheduling performance as well as optimizes the quality of integrated and real-time scheduling of machines and automated guided vehicles.