Research On Some Key Technologies Of Lean Production Operation Of Multi-variety Small Batch Forklifts

With the increasing demand for forklifts,the traditional seller's market turns into a buyer's market,and the customer's demand is personalized and diversified.In this context,forklift truck manufacturing enterprises,in order to improve their market competitiveness,began to shift to the production mode of multi-variety and small-batch production,and introduced the mixed flow assembly line for multi-variety and small-batch production.However,due to the lack of the guidance of lean thinking,the mixed flow assembly line could not give full play to its advantages of efficient production.Therefore,how to make use of the existing production resources of the enterprise and give play to the advantages of high-efficiency production of mixed flow assembly line has important guiding significance for forklift manufacturing enterprises.Aiming at the problems of inaccurate sales forecasting results in unscheduled procurement of materials,leading to increased inventory costs and delayed product delivery,this paper comprehensively considers the influencing factors in forklift sales forecasting,and proposes a prediction model that improves the support vector regression In order to reduce the impact of redundant data on the prediction model,the principal component analysis method was used to reduce the input data to improve the model's computational efficiency;in order to improve the prediction accuracy of the prediction model,an improved particle swarm algorithm was used to support the parameters of the support vector machine(Insensitive loss coefficient,penalty coefficient,and kernel function parameters)for optimization.Then,the proposed prediction model is verified by the sales data of internal combustion forklift of a forklift manufacturer.Considering the problem of imbalanced daily production load on the assembly line caused by the imbalanced mixed-flow assembly plan,a daily production load equalization and variety equalization are targeted.A mixed-flow assembly planning model based on balanced production is proposed,and a heuristic solution algorithm is proposed.Solving the model improves the production stability of the mixed-flow assembly workshop and the upstream parts production workshop.Aiming at the unreasonable sequencing of mixed-flow assembly lines,resulting in waste of production resources and instability of the material distribution system,a multi-objective commissioning optimization model was put forward with the goal of leveling material consumption and the shortest production cycle,and a bee evolutionary model The genetic algorithm solves the production and scheduling model.Finally,a forklift assembly workshop was used to verify the mixed-flow assembly planning model and the production sequencing model.Aiming at the chaotic and untimely material distribution of the mixed-flow assembly line,the characteristics of the material requirements of the mixed-flow assembly line of the forklift were analyzed.The material distribution methods were divided into the order of key large objects,SPS distribution of special parts,and kanban pull distribution of general parts Delivery Method.Then,in order to solve the problem of route optimization in the process of general parts distribution,according to the material distribution characteristics in the Kanban pull mode,and taking material flow load balancing as the optimization goal,a material distribution path optimization model based on the Kanban pull mode was established,considering teaching and learning.With the advantages of low algorithm complexity and high optimization precision,an improved mixed teaching and learning algorithm is proposed to determine the material distribution path.