The Automotive Mixed Model Assembly Line Sequencing Research Based On Intelligent Optimization Algorithms

With the diversification of auto market and the increase in the demand ofpersonalized products,automobile manufacturers have been gradually shift productionmode,mass production paradigm opened by the Ford shift for many varieties of smallbatch production mode.Mixed model assembly line which can product varietiesgradually replaced the traditional single species assembly line,and an effective way formany varieties of small batch production mode.To organized the production ofmultispecies production,the key is the stabilization and heijunka assembly line is the coreof products that optimize the production sequence.Optimize the production sequence toensure a balanced production,shorten the delivery time,reduce inventory and improve thecompetitiveness of enterprises,to better adapt to current market demand.Therefore,mixed-model through the production line to sort the problem,could be better way to play amixed-flow assembly advantages.In this paper,based on the objectives of production load equalization,minimizing totalsetup cost and total setup time,a multi-objective optimization function on mixed modelassembly line is proposed.This function is minimizing the total overload and idle timewith the setup time in workstation and minimize the total setup cost.And establish thecorresponding mathematical model.Mixed-model production line scheduling problem is the issue of combinatorialoptimization problems in NP hard.In this paper,to solve this problem,an improved hybridgenetic ant colony algorithm is proposed.The algorithm uses genetic algorithm as the mainprocess.It takes full advantage of the randomness,the nature of global convergence withthe genetic algorithm.On the one hand the the pheromone of colony algorithm generatesthe better individual and adds them to the genetic populations.Other hand geneticiterations will further update the pheromone,which in turn used to guide the ant colony togenerate individual, to take full advantage of the parallelism of the ant colony, the positivefeedback mechanism as well as solving the high efficiency characteristics.Finally,through an example,assembly sequencing problem is solved by the improved hybrid genetic antcolony algorithm.Comparing the solving results of improved hybrid genetic ant colonyalgorithm,genetic algorithm and ant colony algorithm,It is verified that improved hybridgenetic ant colony algorithm is feasible and effective,and faster than the other algorithmsat searching for the optimal solution to the problem or sub-optimal solution.