The Second Kind Of Multi-objective Assembly Line Balancing Problem Optimization Research

In the production and manufacturing of products,the efficiency of the assembly line is limited by the balance problem of the assembly line.Scientific and efficient optimization of the balance problem of the assembly line can greatly improve the assembly efficiency and reduce the operating cost of enterprises.The traditional assembly line balancing problem only to a single target optimization,however,with the enterprise of complicated assembly environment,often need to optimize the assembly line balancing problem of multiple targets at the same time,although there are many literature studies the multi-objective assembly line balancing problem,however,most of the focus is the first kind of multi-objective assembly line balancing problem,the study of the second category of multi-objective assembly line balancing problem is less,and there is no relevant literature on the workstation space area planning and to carried out on the assembly line sequence of learning cost,In the balance of the assembly line,the study of the space area planning of workstations can make the spatial layout of the assembly workshop more reasonable,and the study of the learning cost brought by the reordering of assembly line tasks can save the operating cost of enterprises.Therefore,the study of these two issues is of great significance.A mathematical model for the second type of multi-objective assembly line balancing problem is established,which takes production tempo,floor space equilibrium index and learning cost as the optimization objectives.After the model is established,a multi-objective Ant-lion algorithm is proposed based on the single-objective Ant-lion algorithm.Decoding and decoding assembly task method based on Newton dichotomy are introduced to ensure that all the individuals in the population meet the basic constraints of assembly line balance.Pareto rule is introduced to ensure that the obtained elite antlion colony is the optimal solution set.An elite reservation strategy based on non-dominated ranking and crowding degree is used to replace the antlion predation rule in the single-objective antlion algorithm,and a multi-angle synthesis solution with better multiple target values is obtained.The effectiveness and superiority of the proposed algorithm are verified by the comparison of the calculation examples in the relevant literature.In view of the multi-objective intelligence group algorithm in solving a single subgoals extremum solution on the problem of insufficient search ability,put forward a hybrid particle swarm optimization algorithm for single subgoals are applied to solve the extremum by dynamically changing inertia weight,setting speed penalty value of standard particle swarm optimization algorithm was improved,through the dynamic change of crossover and mutation probability to improve the crossover and mutation operators of genetic algorithm;Then the selection operation and the improved crossover and mutation operation are integrated into the improved PSO to form the hybrid PSO algorithm.Through solving the examples in relevant literature and comparing with the integrated assembly scheme solved by the multi-objective antlion algorithm,it is verified that the assembly scheme solved by the hybrid particle swarm optimization algorithm can be used as a supplement to the integrated assembly scheme.Finally,based on the effective algorithm,the second kind of multi-objective assembly line balance problem solving system is established,and the hybrid particle swarm optimization algorithm and multi-objective antlion algorithm are integrated into the system to realize the second kind of multi-objective assembly line balance problem solving,and the solution results are processed visually.The effectiveness and feasibility of the proposed algorithm and model are verified by solving the medium and large scale example problems,which provides a useful reference for the research of multi-objective assembly line balancing problem.