多期决策下钢铁企业采购与生产库存优化研究
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摘要
2008年末全球经济危机及2009年欧债危机爆发,世界钢铁工业受到较大冲击,中国大中型钢铁企业普遍出现了利润下滑或亏损。国内钢铁企业历来“重生产轻流通”,然而冶炼技术改革投入巨大成本,带来的效益往往也要多年后才能得到体现,越来越多的钢铁企业管理者开始从物流领域要“利润”。钢铁企业采购和库存成本占总生产成本的比例达到60%以上,对其进行优化研究不仅能够丰富采购库存理论,而且具有降低钢铁企业物流成本、扩大利润空间的现实意义。论文主要内容如下：
钢铁企业原材料采购种类多、数量大,不同物资具有不同的采购环境,管理难度较大,需要对物资进行合理的分类。论文提出采用Kraljic模型从利润影响和供应风险两个属性角度对物资进行分类管理,建立了较为完整的属性指标体系,提出了一种基于DEA的线性规划模型进行综合指标值的确定,得出钢铁企业物资在Kraljic矩阵中的位置,将原材料物资分为战略物资、瓶颈物资、杠杆物资和一般物资,并为每类物资提出相应的采购策略。
在进行原材料采购优化之前,钢铁企业管理者需要对原材料未来价格走向进行把握。论文以采购环境最为复杂且采购成本最大的铁矿石为例进行了研究。分析了铁矿石价格形成机制及铁矿石价格的影响因素。对于影响因素中不能直接量化的因素采用结合专家系统和事件数据分析法的方法进行量化,并应用偏相关系数与序列灰色关联度进行影响强度分析,并为钢铁企业提出相应的对策和建议。为了克服一般回归模型拟合度差的缺点,提出采用非线性半参数模型进行铁矿石价格的预测。虽然拟合精度有了较大的提高,但非线性半参数模型预测结果不是很理想。故又从时间序列角度,把各因素的影响作为一个整体来进行研究,论文采用基于粒子群参数优化的支持向量机(PSO-SVM)方法进行预测,并与差分自回归移动平均模型和BP神经网络方法进行比较,PSO-SVM方法预测精度更高,且更符合实际情况。
面对波动的原材料价格、随机采购提前期,论文建立了适合小型钢铁企业的平稳需求下的采购库存优化模型；若需求也为随机,则建立了适合大中型企业的随机需求下的采购库存优化模型。这两个模型从多期联合优化的角度来考虑原材料价格波动的影响。模型中考虑了采购成本、运输成本和仓储成本三个方面,以采购决策期内多期决策下的单位原材料采购成本最低为优化目标。由于模型的非线性特征,论文提出在价格和钢材需求预测的基础上采用改进的粒子群算法进行求解。以钢铁企业L为案例得出：多期联合优化下的单位原材料采购成本比单期经济订购批量下及延续企业L以补充库存为主的采购策略下都要低,验证了模型及算法的有效性。按照多期联合优化下的采购决策表进行采购,能够为企业节省大量采购成本。
原材料价格波动对钢材生产的影响表现为单位变动成本的波动,论文建立了随机需求下的钢材动态生产优化模型,以钢材多期生产决策来应对单位变动成本的波动。由于模型变量较多,采用粒子群算法容易陷入局部最优,故提出采用模拟退火与粒子群相结合的算法进行求解。同样也以钢铁企业L为例,验证了模型的有效性和算法的收敛性。
考虑到原材料采购优化与钢材生产优化之间的不断相互影响关系,论文建立基于双层规划的原材料采购与钢材生产库存联合优化模型。通过钢铁企业L的案例,验证了集中决策下比分散决策下更具有效益。
Due to the global economic crisis in2008and the European debt crisis in2009,the world steel industry is influenced greatly and Chinese large and medium-sized steel enterprise commonly appear profits decline or losses. Domestic steel enterprises have been always "heavy production light circulation", however smelting technology reform often costs huge but obtains benefits after many years, so more and more steel enterprises search profits from the logistics field. Procurement and inventory cost cover more than60%of total production cost in steel enterprise logistics, so optimization research is not only important to enrich purchase inventory theory,but also has practical significance to lower steel enterprise logistics cost, expanding the profit space.The dissert's main content is as follows:
Raw materials of Steel enterprise have the character of multiple kinds and large quality, different material has different purchasing environment and management is very difficult, so reasonable classification of goods is needed. First, Kraljic model is adopted for materials classification and management from two aspects of profit impact and supply risk, and a complete attribute index system is established. Then a linear programming model based on DEA is put forward to determine total index value. Through the model, positions of steel enterprise materials in the Kraljic matrix are obtained, and raw materials are divided into four classes:strategic items, bottleneck items,leverage items and non-critical items, and the corresponding purchasing strategies for each type of item are put forward.
Before purchasing optimization of the raw materials procurement, steel enterprise managers need to analyze future prices of raw materials. Iron ore which has the most complex purchase environment and largest cost is taken for an example. Price forming mechanism and influential factors of iron ore are analyzed. When the influential factors can not be quantized directly, the expert system and event data analysis method are combined for quantization. Partial correlation coefficient and the sequence grey relational analysis are applied for influence strength analysis, then corresponding countermeasures and suggestions are put forward for steel enterprise.In order to overcome the poor fitting quality of general regression model, the paper proposes the nonlinear semi-parametric model to predict iron ore prices. Although the fitting precision has greatly increased, nonlinear semi-parametric model's prediction results are not very ideal. So from the time series angle, the influence of various factors is taken as a whole variable for research. Support vector machine method based on particle swarm optimization (PSO-SVM) is adopted to forecast iron ore price. Comparing among auto-regression intergrated moving average model, the BP neural network method and PSO-SVM method, the results shows that PSO-SVM method's prediction accuracy is higher, and more tally with the actual situation.
Facing the fluctuation of raw material prices and random purchasing lead time, the dissert has established a stable demand purchase and inventory optimization model suitable for small steel enterprises. If demand is also random, the dissert has also established a purchasing stock optimization model suitable for large and medium-sized enterprises. From the aspect of muti-periods combination optimization, the two models have considered the influence of raw material price's fluctuations. These two models aim to reduce the unite cost of purchasing, transportation and warehousing under muti-periods decision. Because of the model's nonlinear characteristic, this dissert has put forward the improved particle swarm algorithm based on the price and steel demand forecasting. Steel enterprises L is taken for an example, results showed that the unit cost of raw materials of multi-period combination optimization is lower than the single period economic order quantity and the purchasing strategy to replenish inventories. It has proved the validity of the model and the algorithm. According to the purchasing decision table, the enterprise can save a lot of purchasing cost.
The influence of raw material prices fluctuations on the steel production is the fluctuation of unit production variable cost. A dynamic optimization model for steel production is established under stochastic demand. The model considers multi-period production decision to deal with fluctuation of unit variable cost. Because of model's large variables, particle swarm optimization algorithm is easy to fall into the local optimum, therefore the combination method of the simulated annealing and the particle swarm algorithm is put forward for solution. Take steel enterprise L as an example, the dissert proves effectiveness of the model and the convergence of the algorithm.
Considering the interaction relationship between the purchase of raw material optimization and steel production optimization, this dissert establishes an integrated optimal model based on bi-level programming to describe the cyclic variation process.Through the case of steel enterprise L, concentrated decisions have more profits than scattered decisions.
钢铁企业原材料采购种类多、数量大,不同物资具有不同的采购环境,管理难度较大,需要对物资进行合理的分类。论文提出采用Kraljic模型从利润影响和供应风险两个属性角度对物资进行分类管理,建立了较为完整的属性指标体系,提出了一种基于DEA的线性规划模型进行综合指标值的确定,得出钢铁企业物资在Kraljic矩阵中的位置,将原材料物资分为战略物资、瓶颈物资、杠杆物资和一般物资,并为每类物资提出相应的采购策略。
在进行原材料采购优化之前,钢铁企业管理者需要对原材料未来价格走向进行把握。论文以采购环境最为复杂且采购成本最大的铁矿石为例进行了研究。分析了铁矿石价格形成机制及铁矿石价格的影响因素。对于影响因素中不能直接量化的因素采用结合专家系统和事件数据分析法的方法进行量化,并应用偏相关系数与序列灰色关联度进行影响强度分析,并为钢铁企业提出相应的对策和建议。为了克服一般回归模型拟合度差的缺点,提出采用非线性半参数模型进行铁矿石价格的预测。虽然拟合精度有了较大的提高,但非线性半参数模型预测结果不是很理想。故又从时间序列角度,把各因素的影响作为一个整体来进行研究,论文采用基于粒子群参数优化的支持向量机(PSO-SVM)方法进行预测,并与差分自回归移动平均模型和BP神经网络方法进行比较,PSO-SVM方法预测精度更高,且更符合实际情况。
面对波动的原材料价格、随机采购提前期,论文建立了适合小型钢铁企业的平稳需求下的采购库存优化模型；若需求也为随机,则建立了适合大中型企业的随机需求下的采购库存优化模型。这两个模型从多期联合优化的角度来考虑原材料价格波动的影响。模型中考虑了采购成本、运输成本和仓储成本三个方面,以采购决策期内多期决策下的单位原材料采购成本最低为优化目标。由于模型的非线性特征,论文提出在价格和钢材需求预测的基础上采用改进的粒子群算法进行求解。以钢铁企业L为案例得出：多期联合优化下的单位原材料采购成本比单期经济订购批量下及延续企业L以补充库存为主的采购策略下都要低,验证了模型及算法的有效性。按照多期联合优化下的采购决策表进行采购,能够为企业节省大量采购成本。
原材料价格波动对钢材生产的影响表现为单位变动成本的波动,论文建立了随机需求下的钢材动态生产优化模型,以钢材多期生产决策来应对单位变动成本的波动。由于模型变量较多,采用粒子群算法容易陷入局部最优,故提出采用模拟退火与粒子群相结合的算法进行求解。同样也以钢铁企业L为例,验证了模型的有效性和算法的收敛性。
考虑到原材料采购优化与钢材生产优化之间的不断相互影响关系,论文建立基于双层规划的原材料采购与钢材生产库存联合优化模型。通过钢铁企业L的案例,验证了集中决策下比分散决策下更具有效益。
Due to the global economic crisis in2008and the European debt crisis in2009,the world steel industry is influenced greatly and Chinese large and medium-sized steel enterprise commonly appear profits decline or losses. Domestic steel enterprises have been always "heavy production light circulation", however smelting technology reform often costs huge but obtains benefits after many years, so more and more steel enterprises search profits from the logistics field. Procurement and inventory cost cover more than60%of total production cost in steel enterprise logistics, so optimization research is not only important to enrich purchase inventory theory,but also has practical significance to lower steel enterprise logistics cost, expanding the profit space.The dissert's main content is as follows:
Raw materials of Steel enterprise have the character of multiple kinds and large quality, different material has different purchasing environment and management is very difficult, so reasonable classification of goods is needed. First, Kraljic model is adopted for materials classification and management from two aspects of profit impact and supply risk, and a complete attribute index system is established. Then a linear programming model based on DEA is put forward to determine total index value. Through the model, positions of steel enterprise materials in the Kraljic matrix are obtained, and raw materials are divided into four classes:strategic items, bottleneck items,leverage items and non-critical items, and the corresponding purchasing strategies for each type of item are put forward.
Before purchasing optimization of the raw materials procurement, steel enterprise managers need to analyze future prices of raw materials. Iron ore which has the most complex purchase environment and largest cost is taken for an example. Price forming mechanism and influential factors of iron ore are analyzed. When the influential factors can not be quantized directly, the expert system and event data analysis method are combined for quantization. Partial correlation coefficient and the sequence grey relational analysis are applied for influence strength analysis, then corresponding countermeasures and suggestions are put forward for steel enterprise.In order to overcome the poor fitting quality of general regression model, the paper proposes the nonlinear semi-parametric model to predict iron ore prices. Although the fitting precision has greatly increased, nonlinear semi-parametric model's prediction results are not very ideal. So from the time series angle, the influence of various factors is taken as a whole variable for research. Support vector machine method based on particle swarm optimization (PSO-SVM) is adopted to forecast iron ore price. Comparing among auto-regression intergrated moving average model, the BP neural network method and PSO-SVM method, the results shows that PSO-SVM method's prediction accuracy is higher, and more tally with the actual situation.
Facing the fluctuation of raw material prices and random purchasing lead time, the dissert has established a stable demand purchase and inventory optimization model suitable for small steel enterprises. If demand is also random, the dissert has also established a purchasing stock optimization model suitable for large and medium-sized enterprises. From the aspect of muti-periods combination optimization, the two models have considered the influence of raw material price's fluctuations. These two models aim to reduce the unite cost of purchasing, transportation and warehousing under muti-periods decision. Because of the model's nonlinear characteristic, this dissert has put forward the improved particle swarm algorithm based on the price and steel demand forecasting. Steel enterprises L is taken for an example, results showed that the unit cost of raw materials of multi-period combination optimization is lower than the single period economic order quantity and the purchasing strategy to replenish inventories. It has proved the validity of the model and the algorithm. According to the purchasing decision table, the enterprise can save a lot of purchasing cost.
The influence of raw material prices fluctuations on the steel production is the fluctuation of unit production variable cost. A dynamic optimization model for steel production is established under stochastic demand. The model considers multi-period production decision to deal with fluctuation of unit variable cost. Because of model's large variables, particle swarm optimization algorithm is easy to fall into the local optimum, therefore the combination method of the simulated annealing and the particle swarm algorithm is put forward for solution. Take steel enterprise L as an example, the dissert proves effectiveness of the model and the convergence of the algorithm.
Considering the interaction relationship between the purchase of raw material optimization and steel production optimization, this dissert establishes an integrated optimal model based on bi-level programming to describe the cyclic variation process.Through the case of steel enterprise L, concentrated decisions have more profits than scattered decisions.
引文
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