Standard cost setting and application of improved ant colony optimization algorithm

MENG Qiunan, LOU Jian, ZHU Junli, BAI Xue

Systems Engineering - Theory & Practice ›› 2016, Vol. 36 ›› Issue (7) : 1719-1731.

PDF(855 KB)
中国科学院数学与系统科学研究院期刊网
PDF(855 KB)
Systems Engineering - Theory & Practice ›› 2016, Vol. 36 ›› Issue (7) : 1719-1731. DOI: 10.12011/1000-6788(2016)07-1719-13

Standard cost setting and application of improved ant colony optimization algorithm

  • MENG Qiunan, LOU Jian, ZHU Junli, BAI Xue
Author information +
History +

Abstract

Setting standard cost, while ignoring the requirements of product quality and efficiency in production processes, will weaken cost control ability of standard cost. To solve the problem, the optimization method of standard cost setting based on the relations among production time, product quality and production cost was studied. Then a mathematical model of standard cost setting was formulated with objectives to minimize product standard cost and differentials between actual standard production time and ideal production time simultaneously under the condition of product quality requirements. Space-partition ant colony optimization algorithm was designed, and an ant searching strategy was established to overcome the precocious phenomenon. Compared with ant colony optimization algorithm with variable weight, the improved algorithm outperforms the latter. Finally, using the actual cost data of some enterprise, the proposed method of standard cost setting was compared with the present method in the enterprise. The results of simulation experiment testify that the optimization method of standard cost setting has a better effect in enhancing product quality and production efficiency, decreasing the standard cost, and reflecting resource consumption. So it can help to control cost and support for cost lean management in production processes.

Key words

standard cost setting / production time / requirements of quality control / improved ant colony optimization algorithm

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
MENG Qiunan , LOU Jian , ZHU Junli , BAI Xue. Standard cost setting and application of improved ant colony optimization algorithm. Systems Engineering - Theory & Practice, 2016, 36(7): 1719-1731 https://doi.org/10.12011/1000-6788(2016)07-1719-13

References

[1] Chenavaz R. Dynamic pricing, product and process innovation[J]. European Journal of Operational Research, 2012, 222(3):553-557.
[2] Cai X Q, Lai M H, Li X, et al. Optimal acquisition and production policy in a hybrid manufacturing/remanufactu-ring system with core acquisition at different quality levels[J]. European Journal of Operational Research, 2014, 233(2):374-382.
[3] 郑筠,文扬. 基于作业成本法的标准成本体系研究[J]. 北京航天航空大学学报(社会科学版), 2004, 17(3):62-65.Zheng Y, Wen Y. Standard costing system research based on activity-based costing approach[J]. Journal of Beijing University of Aeronautics and Astronautics (Social Sciences Edition), 2004, 17(3):62-65.
[4] De Zoysa A, Herath S K. Standard costing in Japanese firms-reexamination of its significance in the new manufacturing environment[J]. Industrial Management & Data Systems, 2007, 107(2):271-283.
[5] Edwards J R, Boyns T, Matthews M. Standard costing and budgetary control in the British iron and steel industry:A study of accounting change[J]. Accounting, Auditing & Accountability Journal, 2002, 15(1):12-45.
[6] Hsiao T Y. Establish standards of standard costing with the application of convergent gray zone test[J]. European Journal of Operational Research, 2006, 168(2):593-611.
[7] Deng S, Yeh T H. Using least squares support vector machines for the airframe structures manufacturing cost estimation[J]. International Journal of Production Economics, 2011, 131(2):701-708.
[8] Tang S Z, Wang D L, Ding F Y. A new process-based cost estimation and pricing model considering the influences of indirect consumption relationships and quality factors[J]. Computers & Industrial Engineering, 2012, 63(4):985-993.
[9] 田志波, 唐立新, 任一鸣, 等. 基于合成邻域的蚁群算法求解无委托板坯匹配问题[J]. 自动化学报, 2009, 35(2):186-192.Tian Z B, Tang L X, Ren Y M, et al. Solving open-order slab matching problem by ACO with compound neighborhood[J]. Acta Automatica Sinica, 2009, 35(2):186-192.
[10] 武照云, 刘晓霞, 李丽, 等. 产品开发任务分配问题的多目标优化求解[J]. 控制与决策, 2012, 27(4):598-602.Wu Z Y, Liu X X, Li L, et al. Multi-objective optimization for task assignment problem of product development[J]. Control and Decision, 2012, 27(4):598-602.
[11] 施进发, 焦合军, 陈涛. 交货期惩罚下柔性车间调度多目标Pareto优化研究[J]. 机械工程学报, 2012, 48(12):184-192.Shi J F, Jiao H J, Chen T. Multi-objective Pareto optimization on flexible Job-shop scheduling problem about due punishment[J]. Journal of Mechanical Engineering, 2012, 48(12):184-192.
[12] 宗欣露, 熊盛武, 方志祥. 基于蚁群算法的人车混合疏散优化及混合比例分析[J]. 系统工程理论与实践, 2012, 32(7):1610-1617.Zong X L, Xiong S W, Fang Z X. Optimization and proportion analysis of pedestrian-vehicle mixed evacuation based on ant colony algorithm[J]. Systems Engineering——Theory & Practice, 2012, 32(7):1610-1617.
[13] 丁力平, 谭建荣, 冯毅熊, 等. 基于Pareto蚁群算法的拆卸线平衡多目标优化[J]. 计算机集成制造系统, 2009, 15(7):1406-1413.Ding L P, Tan J R, Feng Y X, et al. Multiobjective optimization for disassembly line balancing based on Pareto ant colony algorithm[J]. Computer Integrated Manufacturing Systems, 2009, 15(7):1406-1413.
[14] 王守觉, 王柏南. 人工神经网络的多维空间几何分析及其理论[J]. 电子学报, 2002, 30(1):1-4.Wang S J, Wang B N. Analysis and theory of high-dimension space geometry for artificial neural networks[J]. Acta Electronica Sinica, 2002, 30(1):1-4.
[15] Tan K C, Goh C K, Yang Y J, et al. Evolving better population distribution and exploration in evolutionary multi-objective optimization[J]. European Journal of Operational Research, 2006, 171(2):463-495.
[16] Czyzżak P, Jaszkiewicz A. Pareto simulated annealing——A metaheuristic technique for multiple-objective combinatorial optimization[J]. Journal of Multi-criteria Decision Analysis, 1998, 7(7):34-47.
[17] 王凌, 刘波. 微粒群优化与调度算法[M]. 北京:清华大学出版社, 2008.Wang L, Liu B. Particle swarm optimization and scheduling algorithms[M]. Beijing:Tsinghua University Press, 2008.
[18] Backhaus K, Erichson B, Plinke W. 多元统计分析方法[M]. 上海:上海人民出版社, 2008.Backhaus K, Erichson B, Plinke W. Multivariate statistical analysis[M]. Shanghai:Shanghai People's Publishing House, 2008.

Funding

National Natural Science Foundation of China (71172137, 61034003);National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2015BAF08B02)
PDF(855 KB)

331

Accesses

0

Citation

Detail
Sections
Recommended

/

〈 〉