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国内传统中小生产企业面临的市场需求复杂多变,其生存和发展受到极大挑战.在人工作业系统中,生产线的产能主要取决于工人的数量、掌握技能数及技能水平.在备货型生产模式下,对于只生产单产品的生产线,当计划期内产品需求随机波动性特征明显时,将导致产能与需求的不匹配.考虑工人的薪酬、培训成本,需求工时小于基本产能的库存成本,需求工时处于基本产能与最大产能之间的加班成本,需求工时超过最大产能的加班及外包成本,以员工多技能及各工序生产速率平衡为基础,对计划期初生产线所需的工人数量进行决策,构建总生产成本优化模型,采用极值法对最优解进行求解及分析.通过算例验证了模型的有效性,对员工薪酬、库存成本、加班成本、外包成本系数以及产品单件工时、需求均值及标准差等进行了敏感性分析,得出的结论是:产品单件总工时、需求均值对最优工人数量及总生产成本影响很大,其他各类成本系数对其也有一定影响.尽可能降低单件总工时、提高需求预测准确性等有利于企业控制生产成本.
In the manual operation system, the production capacity of the production line mainly depends on the number of workers, the number of skills and skills to master. In the production of stock-type production Model, the product line with only single product production will lead to the mismatch between production capacity and demand when the stochastic volatility of product demand is obvious during the planning period.Considering the compensation of workers, the training cost, the inventory cost of less than the basic capacity, The demand for working hours is between overtime costs of basic capacity and maximum capacity, overtime and outsourcing costs of working hours exceeding the maximum capacity, the number of workers required to plan the beginning of the production line based on the multi-skills of employees and the production rate of each process, The optimization model of total production cost is constructed, and the optimal solution is solved and analyzed by the extremum method.The validity of the model is verified by a numerical example, which shows the effectiveness of the model in terms of salary, inventory cost, overtime cost, outsourcing cost coefficient, Mean and standard deviation of the sensitivity analysis carried out, concluded that: the total number of hours a single product, The average value of demand has a great influence on the optimal number of workers and the total cost of production, while other types of cost factors also have some impact on it, minimizing the total working hours per unit and increasing the accuracy of demand forecasting are conducive to controlling production costs.