BGA算法在备件库存控制策略优化中的应用研究

张守京; 秦小凡

doi:10.13433/j.cnki.1003-8728.20200088

BGA算法在备件库存控制策略优化中的应用研究

doi: 10.13433/j.cnki.1003-8728.20200088

西安工程大学机电工程学院西安　710048

基金项目: 西安市现代智能纺织装备重点实验室（2019220614SYS021CG043）、陕西省教育厅科研计划项目（No.17JK0321）及中国纺织联合会项目（No.2017100）

详细信息

作者简介:
张守京（1976−），副教授，硕士生导师，研究方向为智能制造技术及系统、智慧物流与柔性生产调度，zhangshoujing@xpu.edu.cn

中图分类号: TG156
计量
- 文章访问数: 161
- HTML全文浏览量: 68
- PDF下载量: 28
- 被引次数: 0
出版历程
- 收稿日期: 2020-01-14
- 网络出版日期: 2021-04-16
- 刊出日期: 2021-04-16

Application of BGA Algorithm in Optimization of Spare Parts Inventory Control Strategy

School of Mechanical and Electrical Engineering, Xi′an Polytechnic University, Xi′an 710048, China

摘要

摘要: 建立一个考虑备件重要度库存控制模型，并运用提出的BGA算法(BAS-Genetic algorithm)实现运算收敛快，获得总成本更优。首先以库存成本最小化为原则，建立库存模型的目标函数，其次建立了以基于重要度的服务水平为约束条件，构建库存控制模型。最后根据考虑维修备件重要度的库存控制策略模型。提出BGA（BAS-Genetic algorithm）算法用于最优库存控制策略解的查找。结果表明，BGA算法的收敛比GA（Genetic algorithm）收敛的速度快，且计算的目标函数成本值更小，不易陷入局部最优。
- 库存服务系统 /
- 服务水平 /
- BGA算法 /
- MATLAB /
- 库存控制策略
Abstract: This article establishes an inventory control model that considers the importance of spare parts, and uses the proposed BGA algorithm (BAS-Genetic algorithm) to achieve fast convergence and better total cost control. Firstly, based on the principle of minimizing inventory cost, the objective function of inventory model is established. Secondly, the inventory control model is constructed under the constraints of the service level based on importance. According to the inventory control strategy model considering the importance of maintenance spare parts, this paper proposes the BGA algorithm for the search of the optimal inventory control strategy solution. Through MATLAB simulation, comparing the BGA operation results with traditional genetic algorithms, we can find that the BGA algorithm converges faster than the GA (Genetic algorithm) convergence, and the calculated objective function cost is smaller, and it is not easy to fall into a local optimum.
- inventory service system /
- service level /
- BGA algorithm /
- MATLAB /
- inventory control strategy

HTML全文

图 1 库存水平状态转移图

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图 2 BGA算法流程图

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图 3 父代染色体

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图 4 染色体交叉

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图 5 染色体变异

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图 6 GA结果

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图 7 BGA结果

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表 1 不同μ、I_m参数控制下的敏感性分析

μ	T_C,（r, S）
μ	I_m = 0.1	I_m = 0.2	I_m = 0.3	I_m = 0.4
10	1029.074074,(17,30)	1137.289157,(43,167)	1175.83004,(46,172)	1596.392111,(47,262)
20	1019.336735,(11,23)	1038.928571,(13,25)	1077.675439,(28,42)	1133.932741,(41,169)
30	936.826484,(3,15)	1015.730594,(11,23)	1025.509804,(23,37)	1036.199621,(36,153）
40	924.8324742,(2,14)	974.3170103,(7,19)	1004.14823,(21,35)	1012.180659,(33,50)

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表 2 I_m=0.4，λ =5，μ参数控制下的成本敏感度分析

μ	R_e	S_h	I
10	0.023	1.64029×10⁴³	154.748
20	0.038	8.06219×10⁵⁵	105.373
30	0.042	3.00072×10⁵⁹	94.916
40	0.278	2.78834×10⁶²	41.933

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表 3 不同λ、I_m参数控制下的敏感性分析

λ	T_C,（ r, S ）
λ	I_m= 0.1	I_m= 0.2	I_m= 0.3	I_m= 0.4
10	3299.1667,(6,12)	3305.595238,(7,13)	3324.880952,(10,16)	3376.309524,(18,24)
20	3685.09803,(8,24)	4238.587571,(12,25)	4587.261905,(33,45)	4602.146893,(67,80)
30	5547.5,(9,27)	5894.074074,(33,50)	6029.642857,(84,102)	6151.785714,(103,121)
40	7234.53074,(12,33)	7486.611577,(63,75)	7694.3017,(136,149)	7733.38374,(140,153)

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表 4 I_m=0.4，μ=3，λ参数控制下的成本敏感度分析

λ	R_e	S_h	I
10	1.42857142857140	13549.51988	19.05952381
20	1.42857142857143	138296072.32923	69.36723164
30	1.57894736842105	360459408.71955	105.5357143
40	2.85714285714286	946071904.02461	136.7046414

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