Research On Multi-objective Scheduling Optimization Of Hybrid Flow Shop

Hybrid flow shop scheduling problem is a kind of production scheduling problem in the field of production. It is more complex to solve the Hybrid flow shop multi objective scheduling problems. We do not only consider how to shorten the production cycle, but also consider multiple objectives at the same time to optimize the overall production system.In this thesis, based on domestic and foreign research of shop scheduling and multi-objective optimization problem, the non-dominated sorting genetic algorithm(NSGA-II) is used to optimize the multi-objective scheduling of hybrid flow shop. The NSGA-II is applied to solve the actual Hybrid job shop scheduling problem, NSGA-II has advantages in multi-objective optimization. It can solve the multi-objective scheduling problem of hybrid flow shop effectively.Firstly, the research status of the shop scheduling and the significance of the research are introduced in this thesis. Secondly, the hybrid flow shop and multi-objective optimization theory is introduced in detail. Based on the production environment of Hybrid flow shop, a multi-objective scheduling model is established, which aims at the shortest production cycle, the minimum machining cost and the lowest bad quality attributes. The NSGA-II is applied to solve the multi-objective scheduling model of hybrid flow shop, of which the operation process is introduced in detail. A matrix type real number coding method based on the workpiece and working procedure is designed to solve the scheduling problem. Finally, combining with a hybrid production enterprises, MATLAB is used to program a multi-objective optimization of non-inferior sorting genetic algorithm program, and gives the program code. Through MATLAB simulation of production scheduling example, a group of Pareto solutions is obtained, and the data is then standardized. The weight coefficient of each target is determined by the analytic hierarchy process. A satisfactory scheduling scheme is selected from the Pareto solutions by weighted sum. The numerical values of the three indexes are better than those of the manual programming. The results show that the algorithm is effective and feasible in solving the hybrid flow shop scheduling problem and can provide reference for the enterprise of the production scheduling.