Research On The Scheduling Problem Of Mixed-flow Assembly Workshop Based On Complete Kit Of Materials

With the increasing popularity of mass customization,most manufacturing companies begin to assemble products in a mixed-flow manufacturing model.In order to solve the problem of global complete kit extending start time of order,the complexity of complete kit of materials becoming higher and higher,scheduling of general assembly workshop combined with logistics distribution in a manufacturing company,this thesis carried out the research on the scheduling method of mixed assembly line based on the analysis of complete kit of materials,funded by the Major Science and Technology Project of Sichuan Province "Research and application demonstration of key technologies for networked collaborative manufacturing in the electronic and electrical industry"(Project No.2020ZDZX0013).First of all,this thesis proposed a classified complete kit method.On the basis of obtaining the missing materials in the order,the materials were classified according to the scope and the kitting quality,and the products were classified according to whether they can be produced or not.And the applications of the classified complete kit method and the global complete kit method to a mixed assembly line showed that the classified complete kit method was better in terms of the completing time under different logistics distribution time of materials.Secondly,based on the minimizing maximum completing time combined with the learning forgetting effect,balancing the load of each station,and minimizing the product switching times,the assembly line static scheduling mathematical model was established.Aiming at the problem that the current learning effect is not related to the forgetting effect,an improved learning forgetting effect model that learning factor is related to forgetting parameter was established.And in the start time of each station,this thesis quantitated the waiting time of each station due to the lack of materials.Aiming at the problem that the optimizating speed of the MOGA algorithm is too slow and it is difficult to jump out of the local optimum,a multi-product initial sorting rule was designed to construct the initial solution set.based on the product classified set,the cross and mutation operator were improved to optimize the evoluting process of the algorithm.The examples verified the feasibility of the improved learning forgetting effect mode and the effectiveness of the initial sorting rule,and illustrated the applicable scenarios of the classified complete kit method under the multi-objective function.Thirdly,in the rescheduling oriented to sudden disturbance,this thesis established the rescheduling model with the objective functions of minimizing the scheduling deviation with static scheduling and minimizing the completing time.Combining the analysis of classified complete kit method,this thesis optimized periodic rescheduling strategies and event-driven rescheduling strategies to reduce the impact of disturbances such as delaying arrival of materials and urgent order insertion in the manufacturing workshop on producing activities.Finally,this thesis took a refrigerator mixed-flow assembly line in a certain enterprise as an example,the analysis of classified complete kit method,the formulation of a multi-objective static scheduling plan and rescheduling plan were carried out.In the follow-up,based on the current research,this thesis gave directions worthy of further research and exploration on the mixed-flow assembly line.