运筹与管理 ›› 2019, Vol. 28 ›› Issue (4): 94-99.DOI: 10.12005/orms.2019.0084

• 理论分析与方法探讨 • 上一篇    下一篇

最小化总反馈长度的耦合活动排程研究

钱艳俊1,林军2   

  1. 1.西北工业大学 管理学院,陕西 西安 710072;
    2.西安交通大学 管理学院,陕西 西安 710049
  • 收稿日期:2017-01-07 出版日期:2019-04-25
  • 作者简介:钱艳俊(1979-),女,浙江金华人,副教授,博士研究生,研究方向:新产品开发管理;林军(1976-),男,上海人,教授,博士研究生,研究方向:新产品管理。
  • 基金资助:
    国家社科基金课题(16BGL017),国家自然科学基金(71371149,71672140),中央高校基本科研业务费专项资金资助(3102017jc19004)

A Study on Scheduling Coupled Activities with Minimum Total Feedback Lengths

QIAN Yan-jun1, LIN Jun2   

  1. 1.School of Management, Northwestern Polytechnical University, Xi’an 710072,China;
    2.School of Management, Xi’an Jiaotong University, Xi’an 710049, China
  • Received:2017-01-07 Online:2019-04-25

摘要: 耦合活动的排程直接影响新产品开发的周期和成本,因而受到了学者和研发管理人员的普遍关注。本文针对最小化总反馈长度这一耦合活动排程常用目标,将遗传算法与局部搜索算法相结合,提出了一种新的混合优化算法,并系统分析了参数对算法性能的影响。然后将算法应用到实际案例和大量随机算例中,实验结果表明混合优化算法较大幅度提高了现有局部搜索算法解的质量;同等情形下,混合优化算法所获得解比单纯运用遗传算法所获得解更好。

关键词: 设计结构矩阵, 新产品开发, 耦合活动排程, 遗传算法, 混合优化算法

Abstract: The scheduling of coupled activities directly affects the time and cost of new product development, and thus has become a focal point of both scholars and design managers. Researchers have shown that finding an activity sequence with minimum total feedback length can significantly reduce development time. However, the feedback length minimization problem is NP-hard and hard to solve. To solve large problem instances in reasonable time, this study combines the genetic algorithm with an existing greedy heuristic, and designs a new hybrid optimization algorithm. We also systematically analyze the effect of parameters, such as crossover rate and mutation rate, on algorithm’s performance, and suggest appropriate parameter values. Experimental results indicate that the hybrid optimization algorithm greatly improves the existing greedy heuristic. Moreover, with same settings, the hybrid optimization algorithm often yields better solutions than the genetic algorithm without the greedy heuristic.

Key words: design structure matrix, new product development, coupled activity scheduling, genetic algorithm, hybrid optimization algorithm

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