GENETIC ALGORITHM-BASED DESIGN AND SIMULATION OF MANUFACTURING FLOW SHOP SCHEDULING

This paper applies the non-dominated sorting genetic algorithm (NSGA) to the design of non-compact flow shop scheduling plan, and successfully solves the multi-objective optimization problem considering process connection. Specifically, an NSGA-based scheduling strategy was developed after analysing the features of the non-compact flow shop in manufacturing enterprises, and an improved algorithm was created for the multi-objective optimization of non-compact flow shop scheduling considering process connection. The research results show that: the improved NSGA is a desirable way to solve the multi-objective optimization of non-compact flow shop scheduling, as it ensures the population diversity and guarantees the evolution effect; this algorithm is more realistic than traditional algorithms, which overlooks the process connection; the case simulation and analysis reveal that the established multi-objective scheduling model for non-compact flow shop enjoys good adaptability. The research finding carries profound theoretical and practical significance for enterprises, e.g. improving the scheduling of non-compact flow shop, production efficiency and response to market situations.