原材料价格波动下钢铁生产库存多期优化模型

计算机工程与应用 ›› 2014, Vol. 50 ›› Issue (2): 216-221.

原材料价格波动下钢铁生产库存多期优化模型

伍景琼1，2，蒲云2

1.昆明理工大学交通工程学院，昆明 650504
2.西南交通大学交通运输与物流学院，成都 610031

出版日期:2014-01-15 发布日期:2014-01-26

Production and inventory multi-period optimization of steel enterprise under fluctuating raw material prices

WU Jingqiong1，2, PU Yun2

1.Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
2.School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China

Online:2014-01-15 Published:2014-01-26

摘要/Abstract

摘要： 考虑钢铁企业钢材生产受钢材需求和生产变动成本波动的双重影响，在预测基础上，建立钢材生产库存多期动态优化模型。由于模型涉及多种钢材及多个时段，属于大规模问题，求解困难，且为了避免粒子群算法陷入局部最优，提出采用模拟退火与粒子群组合智能算法对模型进行求解。最后通过钢铁企业L的案例，结果表明算法具有较强的收敛性和适用性，模型可用于解决钢铁企业多期生产实际问题。

关键词: 钢铁企业, 生产库存优化, 原材料价格波动, 多期决策

Abstract: The fluctuations of steel demand and production variable cost influence the steel production quantity jointly. Based on the raw material price, steel demand and steel price forecasting, steel production and inventory dynamic optimization model is established. Because the model relates to a variety of steel products and periods, it belongs to large-scale problems and is hard to be solved. And in order to avoid particle swarm algorithm falls into local optimum, a portfolio algorithm combined simulated annealing and particle swarm algorithm is put forward to solve the model. Through the case of steel enterprise L, the results show that the portfolio algorithm has strong convergence and widely use and the model can be used to solve the steel enterprise’s practical production problems.

Key words: steel enterprise, production and inventory optimization, raw material price fluctuation, multi-period decision

伍景琼1，2，蒲云2. 原材料价格波动下钢铁生产库存多期优化模型[J]. 计算机工程与应用, 2014, 50(2): 216-221.

WU Jingqiong1，2, PU Yun2. Production and inventory multi-period optimization of steel enterprise under fluctuating raw material prices[J]. Computer Engineering and Applications, 2014, 50(2): 216-221.

[1]	赵斐1，韩旭2，乔非2，许维胜2，吴启迪1. 面向钢铁企业能源流程的逐步形式化建模方法[J]. 计算机工程与应用, 2011, 47(14): 242-245.
[2]	宋旭东^1,2,刘晓冰¹,程晓兰²,翟坤¹. 钢铁企业生产成本关键工序数据挖掘应用研究[J]. 计算机工程与应用, 2008, 44(28): 184-186.