引用本文:韩毅,蔡建湖,周根贵,李延来,唐加福.多级生产批量规划问题的柔性惯量反捕食粒子群算法[J].控制理论与应用,2010,27(10):1300~1306.[点击复制]
HAN Yi,CAI Jian-hu,ZHOU Gen-gui,LI Yan-lai,TANG Jia-fu.Anti-predatory particle-swarm optimization with flexible inertial weight for unconstrained multilevel lot-sizing problems[J].Control Theory and Technology,2010,27(10):1300~1306.[点击复制]
多级生产批量规划问题的柔性惯量反捕食粒子群算法
Anti-predatory particle-swarm optimization with flexible inertial weight for unconstrained multilevel lot-sizing problems
摘要点击 1990  全文点击 1347  投稿时间：2008-11-04  修订日期：2009-12-15
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DOI编号  10.7641/j.issn.1000-8152.2010.10.CCTA081214
  2010,27(10):1300-1306
中文关键词  多级生产批量规划  反捕食粒子群算法  亚启发式算法  惯性权重  装配结构
英文关键词  multilevel lot-sizing  anti-predatory particle swarm optimization  meta-heuristics  inertial weight  assembly structure
基金项目  国家自然科学基金资助项目(70625001, 70721001, 70671095, 70971017); 浙江省科技计划软科学研究资助项目(2009C35007); 浙江省自然科学基金资助项目(Y1100854); 浙江省社科规划课题资助项目(10CGGL21YBQ).
作者单位E-mail
韩毅* 浙江工业大学 经贸管理学院 arctic_wind@yahoo.cn 
蔡建湖 浙江工业大学 经贸管理学院  
周根贵 浙江工业大学 经贸管理学院  
李延来 东北大学 流程工业综合自动化教育部重点实验室  
唐加福 东北大学 流程工业综合自动化教育部重点实验室  
中文摘要
      多级生产批量规划(MLLS)是原料需求计划(MRP)中主生产计划(MPS)的关键决策问题, 具有广泛的工业应用; 已被证明是NP-hard类型的组合优化问题. 反捕食粒子群算法(APSO) 是最近提出的一种与粒子群算法(PSO)密切相关的亚启发式算法. 本文提出带柔性惯性权重的反捕食粒子群算法(WAPSO) 对具有指定装配结构而无约束的MLLS问题进行了求解. 本算法对12个小规模benchmark数据集和1个随机产生的较大规模数据进行了测试. 测试结果与遗传算法(GA)和Wagner-Whitin(WW)动态规划算法的结果进行了比较. 结果表明了WAPSO算法的有效性和适用性.
英文摘要
      Multilevel lot-sizing(MLLS) is a crucial problem in decision-making for the master production scheduling(MPP) of the material requirement plan(MRP), which is with broad industrial applications and has been considered the NP-hard combinatory optimization problem. Anti-predatory particle-swarm optimization(APSO), which is closely related to particle-swarm optimization(PSO), is a recently emerged meta-heuristics. An anti-predatory particle-swarm optimization with flexible inertial weight(WAPSO) is proposed to solve the unconstrained MLLS problem in a given assembly structure. A set of 12 small-sized benchmark data and a randomly generated medium size data are adopted to test the proposed algorithm. The experimental results are compared with those of genetic algorithm(GA) and Wagner-Whitin(WW) dynamic programming algorithm, the results show that WAPSO algorithm is an effective and suitable tool for solving the unconstrained MLLS problem in a given assembly structure.