Research On Multi-Objective Flexible Job Shop Scheduling Problem Considering Learning Effect

The global manufacturing environment has provided rich development opportunities for the manufacturing industry of all countries in the world,but it is also facing various challenges and difficulties.Workshop scheduling is a production management style that adapts to the development needs of enterprises.Under various constraints,all kinds of limited resources are fully allocated to achieve the purpose of improving production efficiency and reducing production costs through scientific and reasonable scheduling arrangement.Under the premise of fully investigating the current research status of flexible job shop scheduling at home and abroad and in view of some deficiencies in today's research,this thesis studies a multi-objective flexible job shop scheduling problem considering the worker's learning effect,energy consumption optimization,dynamic events and other factors.The specific research contents are as follows:(1)First of all,the characteristics of flexible job shop scheduling problem are analyzed,and the performance index system of this thesis is proposed;Based on the classification of workshop energy consumption factors,workshop energy consumption calculation models are established,and two energy consumption optimization methods are proposed;Considering the worker factor in workshop scheduling,the learning effect theory is introduced into flexible job shop scheduling problem.(2)Secondly,multi-objective flexible job shop scheduling problem modeling and solution are studied.The basic problem is described,and a multi-objective optimization model aimed at minimizing the maximum completion time,processing cost,and processing energy consumption is established.An improved NSGA-? algorithm is designed,using a double-layer coding method and introducing the insertion greedy decoding algorithm in the decoding process to obtain the active scheduling solution.In order to make the algorithm have better global and local search capability,a method of adaptive dynamic crossover and mutation probability is designed,and a fitness comparison strategy is chosen in the later stage of mutation to carry out individual species better optimizing to avoid falling into local solution.Common calculation examples are used to verify the effectiveness of the improved NSGA-? algorithm,and the application of model and algorithm is studied combining with production cases.Four scheduling schemes are constructed from different angles,and the energy-saving scheme is analyzed to achieve good energy consumption optimization.(3)Thirdly,modeling and solution of dual flexible job shop scheduling problem considering learning effect are studied.Considering the level of skill caused by the learning ability of workers in the workshop,a multi-objective dual flexible job shop scheduling optimization model considering the learning effect of workers is constructed;Due to the complexity of the problem,a hybrid NSGA-? algorithm is designed by using a three-layer chromosome coding method,on the basis of the improved NSGA-? algorithm introduced in Chapter 3;The neighborhood search operator is introduced into the algorithm to better optimize the population search,the application research is carried out in combination with specific examples,and the analysis shows that the scheduling scheme considering the flexibility of the workers is more balanced among production targets.(4)Finally,the dynamic scheduling problem of dual flexible job shop considering learning effect is studied.Aiming at the common dynamic events,a classification study is carried out,a research method based on a combination of event-driven and periodic hybrid rescheduling strategies and rolling window optimization technology is proposed to solve the problems of random dynamic events and periodic rescheduling,and the update of problem information in dynamic scheduling is analyzed;The dynamic event scheduling and periodic rescheduling considering machine failure,worker vacation,new workpiece arrival are studied with a specific example.The analysis results show that the dynamic scheduling scheme can better maintain the stability of the production system.