东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2010, Vol. 31 ›› Issue (1): 20-22+27.DOI: -

• 论著 • 上一篇    下一篇

无资源约束MLLS问题的三种求解算法效果比较

韩毅;唐加福;蔡建湖;周根贵;   

  1. 东北大学流程工业综合自动化教育部重点实验室;浙江工业大学经贸管理学院;
  • 收稿日期:2013-06-20 修回日期:2013-06-20 出版日期:2010-01-15 发布日期:2013-06-20
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家创新群体科学基金资助项目(70721001);;

Comparison among three algorithms to solve unconstrained MLLS problems

Han, Yi (1); Tang, Jia-Fu (1); Cai, Jian-Hu (2); Zhou, Gen-Gui (2)   

  1. (1) Key Laboratory of Integrated Automation of Process Industry, Northeastern University, Shenyang 110004, China; (2) College of Business Administration, Zhejiang University of Technology, Hangzhou 310023, China
  • Received:2013-06-20 Revised:2013-06-20 Online:2010-01-15 Published:2013-06-20
  • Contact: Zhou, G.-G.
  • About author:-
  • Supported by:
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摘要： 用于求解无资源约束多级生产批量计划(UMLLS)问题的算法包括混合粒子群(HPSO)算法、混合分散搜索算法(HSS)和带排斥算子的遗传算法(RGA).为了研究各算法对问题的适用性,对于上述三种算法的求解效果采用标准测试集进行了较全面的测试和比较,给出了针对不同规模无资源约束生产批量计划问题的算法选择方案.测试结果显示:对于小规模和中规模问题,HSS算法的效果更好;对于大规模问题,HPSO算法的性能更优越.

关键词: 生产批量计划问题, 粒子群算法, 分散搜索, 遗传算法, 排斥算子

Abstract: To solve the unconstrained multilevel lot-sizing (UMLLS) problems, the harmonious particle swarm optimization (HPSO) algorithm, hybrid scatter search(HSS) algorithm and genetic algorithm integrated with repulsion operator (RGA) have been proposed. To study their suitability, a performance comparison among them was carried out systematically via a comprehensive test introducing the standard testing data sets, then the alternative of those algorithms can be decided for UMLLS problems with different sizes. The test results showed that HSS is more powerful for small/medium-sized problems and HPSO is superior to the other two algorithms in large-sized problems.

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