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中国管理科学

中国管理科学 ›› 2008, Vol. 20 ›› Issue (6): 33-40.

• 论文 • 上一篇    下一篇

能力与资源双重约束下的启发式组合生产计划研究

肖依永1, 常文兵1, 张人千2   

  1. 1. 北京航空航天大学工程系统工程系, 北京 100083;
    2. 北京航空航天大学经济管理学院, 北京 100083
  • 收稿日期:2007-12-29 修回日期:2008-10-12 出版日期:2008-12-31 发布日期:2008-08-20
  • 作者简介:肖依永(1973- ),男(汉族),四川人,北京航空航天大学工程系统工程系博士后,研究方向:生产管理、信息系统、数据挖掘.
  • 基金资助:

    国家自然科学基金资助项目(70501002);航空科学基金资助项目(2007ZG51075)

A Research on Heuristic Production Planning with Dual Constraints of Capabilities and Resources

XIAO Yi-yong1, CHANG Wen-bing1, ZHANG Ren-qian2   

  1. 1. Department of System and Engineering, Beihang University, Beijing 100083, China;
    2. School of Economics and Management, Beihang University, Beijing 100083, China
  • Received:2007-12-29 Revised:2008-10-12 Online:2008-12-31 Published:2008-08-20

PDF (PC)

1090

被引次数

10

摘要： 企业面向市场的产品交付能力,一般受约束于各制造环节的生产能力,而各制造环节的生产能力又受约束于其可得的制造资源。研究了在局部生产能力具有上限,且总体制造资源受约束的情况下,如何制定生产计划,以使总体利润最大的问题。建立了基于能力和资源双重约束下的生产计划组合优化模型,设计了一种"双线退火"的模拟退火算法,来启发式求解此类连续变量组合优化问题,并以大量算例计算验证了双线模拟退火算法的效率和正确性。最后结合某耐火材料企业进行了应用分析研究,展示了模型的应用原理和算法的求解效果。

关键词: 综合生产计划, 模拟退火算法, 启发式算法, 作业成本管理

Abstract: Generally, the product delivery capability of enterprise is restricted to the process capabilities of all nodes on the manufacturing line, while the process capabilities are constrained by the limited manufacturingresourcesl This paper focuses on how to make the aggregate production planning(APP) for an enterprise to get the maximum profit while the constraints of process capabilities and manufacturing resources are simultaneously effectingl A model of APP with constraints ofboth capabilities and resources is presented, and to solve such combinatorial optimization problem of continuous variables, the simulated ant nealing algorithm with the new feature of "dual annealing" is designed, which has been validated to be efficient and accurate by lots of computation experiments in this paperl Finally, an application research is cart ried out on a factory of fireproof material to illustrate the applicable way of the model, as well as the new algorithml.

Key words: aggregate production planning, simulated annealing algorithm, heuristic algorithm, activity-based management

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