Multi-objective Optimization of Flexible Flow-Shop Intelligent Scheduling Based on a Hybrid Intelligent Algorithm

Abstract

With the complexity of the production process, the mass quantification of production jobs, and the diversification of production scenarios, research on scheduling problems are bound to develop in a direction closer to the actual production problems. Considering the combination of workshop scheduling problems and process planning problems, the study of such problems is of great significance for improving the production efficiency of enterprises. Therefore, this chapter studies the intelligent scheduling problem of a flexible flow-shop and establishes a two-stage flexible flow-shop scheduling model. On this basis, the fast non-dominated sorting genetic algorithm II (NSGA-II) and the variable neighborhood search algorithm (VNS) are combined to optimize the established two-stage intelligent scheduling model. Finally, a papermaking production process is taken as an example to comprehensively evaluate the performance of the model and the hybrid intelligent algorithm. The experimental results show that the model and algorithm can effectively solve the presented problem.