作业能力扩张模型与求解算法研究

摘要/Abstract

摘要： 为解决基于作业中心的生产作业系统能力决策问题，使用作业成本算法，结合产品组合、能力规划等因素，建立了一个作业能力扩张模型。提出了对能力调整变量使用遗传搜索、而对其他变量使用线性规划进行寻优的近似混合启发式算法，并就遗传操作中解的可行性问题研究了解决方案。最后通过数值试验，证明了该算法的有效性,并与一般的遗传算法进行了性能对比。

关键词: 作业成本法, 能力扩张, 遗传算法, 线性规划

Abstract: For the activity-based capacity decision of production system, a capacity expansion model based on the Activity-Based Costing is built. The model integrates the product mix, activity capacity expansion in production activity system. According to the characteristics of this model, a hybrid heuristic algorithm (GA_LP) is produced, which uses Genetic Algorithm (GA) to optimize the capacity variables and Linear Programming (LP) to deal with continuous variables. At last, the solution feasibility is studied, the comparison between GA and GA_LP is discussed, and the effectiveness of GA_LP is proved by numerical test.

Key words: activity-based costing, capacity expansion, genetic algorithm, linear programming

中图分类号:

F273

张人千，魏法杰. 作业能力扩张模型与求解算法研究[J]. .

ZHANG Ren-qian, WEI Fa-jie. Model and Algorithm of Activity-based Capacity Expansion[J]. .

参考文献

［1］CHAND S，MCCLURG T，WARD J. A model for paralell machine replacement with capacity expansion[J]. European Journal of Operational Research, 2000,121:519-531.

[2]OLHAGER J, RUDBERG M, WIKNER J. Long-term capacity management: link the perspective from manufacturing strategy and sales and operations planning[J]. International Journal of Production Economics, 2001,69:215-225.

[3]HUA Zhongsheng. Research on line capacity design for a kind of flexible manufacturing system[D]. Hefei:Department of Computer Science and Technology, University of Science and Technology of China, 2000(in Chinese).[华中生.一类柔性制造系统的能力规划问题的研究[D].合肥：中国科学技术大学计算机科学技术系,2000.]

[4]SHAPIRO J F. On the connections among activity-based costing, mathematical programming models for analyzing strategic decisions, and the resource-based view of the firm[J]. European Journal of Operational Research, 1999,118:295-314.

[5]ZHOU Ming, SUN Shudong. Genetic algorithms: theory and application[M]. Beijing:National Defence Industry Press of China, 1999 (in Chinese).[周明,孙树栋.遗传算法原理及应用[M].北京：国防工业出版社,1999.]

[6]LIU Yong, KANG Lishan, CHEN Yuping. Non numerical parallel algorithm—genetic algorithm[M]. Beijing:Science Press of China,1995(in Chinese).[刘勇,康立山,陈毓屏.非数值并行算法—遗传算法[M].北京：科学出版社,1995.]

[7]LACKSONEN T.Empirical comparison of search algorithms for discrete event simulation[J]. Computers & Industrial Engineering, 2001,40:133-148.

[8]ZHANG Renqian.Research of production and capacity decision based on activity-based costing[D].Beijing：School of Economics and Management of Beihang University, 2003(in Chinese).[张人千. 基于作业成本的生产与能力决策研究[D].北京：北京航空航天大学经济管理学院,2003.

[9]ZHANG Renqian, WEI Fajie, TAN Zhen. Model and case study of workshop activity optimization based on cost[J].Chinese Journal of Management Science,2002,10(5):74-77(in Chinese).[张人千,魏法杰,谭甄.基于成本的作业优化模型与实证研究[J].中国管理科学,2002,10(5):74-77.]

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