一种基于动态决策块的超启发式跨单元调度方法
对运输能力受限条件下的跨单元调度问题进行分析,提出一种基于动态决策块和蚁群优化（Ant colony optimization,ACO）的超启发式方法,同时解决跨单元生产调度和运输调度问题.在传统超启发式方法的基础上,采用动态决策块策略,通过蚁群算法合理划分决策块,并为决策块选择合适的规则.实验表明,采用动态决策块策略的超启发式方法比传统的超启发式方法具有更好的性能,本文所提的方法在最小化加权延迟总和目标方面有较好的优化能力并且具有较高的计算效率....
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| Published in | 自动化学报 Vol. 42; no. 4; pp. 524 - 534 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
北京理工大学计算机学院智能信息技术北京市重点实验室 北京100081
2016
延安大学数学与计算机科学学院 延安 716000%北京理工大学计算机学院智能信息技术北京市重点实验室 北京100081 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0254-4156 1874-1029 |
| DOI | 10.16383/j.aas.2016.c150402 |
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| Abstract | 对运输能力受限条件下的跨单元调度问题进行分析,提出一种基于动态决策块和蚁群优化（Ant colony optimization,ACO）的超启发式方法,同时解决跨单元生产调度和运输调度问题.在传统超启发式方法的基础上,采用动态决策块策略,通过蚁群算法合理划分决策块,并为决策块选择合适的规则.实验表明,采用动态决策块策略的超启发式方法比传统的超启发式方法具有更好的性能,本文所提的方法在最小化加权延迟总和目标方面有较好的优化能力并且具有较高的计算效率. |
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| AbstractList | 对运输能力受限条件下的跨单元调度问题进行分析,提出一种基于动态决策块和蚁群优化(Ant colony optimization, ACO)的超启发式方法,同时解决跨单元生产调度和运输调度问题。在传统超启发式方法的基础上,采用动态决策块策略,通过蚁群算法合理划分决策块,并为决策块选择合适的规则。实验表明,采用动态决策块策略的超启发式方法比传统的超启发式方法具有更好的性能,本文所提的方法在最小化加权延迟总和目标方面有较好的优化能力并且具有较高的计算效率。 对运输能力受限条件下的跨单元调度问题进行分析,提出一种基于动态决策块和蚁群优化（Ant colony optimization,ACO）的超启发式方法,同时解决跨单元生产调度和运输调度问题.在传统超启发式方法的基础上,采用动态决策块策略,通过蚁群算法合理划分决策块,并为决策块选择合适的规则.实验表明,采用动态决策块策略的超启发式方法比传统的超启发式方法具有更好的性能,本文所提的方法在最小化加权延迟总和目标方面有较好的优化能力并且具有较高的计算效率. |
| Abstract_FL | In this paper, the inter-cell scheduling problem with a transportation capacity constraint is analyzed. An ant colony optimization (ACO)-based hyper-heuristic with dynamic decision blocks is proposed, which selects appropriate heuristic rules for production and transportation simultaneously. On the basis of traditional hyper-heuristics, a dynamic decision block strategy is proposed, in which several entities are grouped into a decision block under the guidance of pheromones, and appropriate heuristic rules are selected for each decision block. Comparisons between the proposed method and other hyper-heuristics with different decision block strategies are conducted. Computational results show a satisfying performance of the proposed method in minimizing total weighted tardiness with less computational costs. |
| Author | 田云娜 李冬妮 刘兆赫 郑丹 |
| AuthorAffiliation | 北京理工大学计算机学院智能信息技术北京市重点实验室,北京100081 延安大学数学与计算机科学学院,延安716000 |
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| Author_FL | LIU Zhao-He ZHENG Dan TIAN Yun-Na LI Dong-Ni |
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| DocumentTitleAlternate | A Hyper-heuristic Approach with Dynamic Decision Blocks for Inter-cell Scheduling |
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| Keywords | 跨单元调度 超启发式 蚁群算法 动态决策块 inter-cell scheduling Dynamic decision block hyper-heuristic ant colony optimization (ACO) |
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| Notes | In this paper, the inter-cell scheduling problem with a transportation capacity constraint is analyzed. An ant colony optimization（ACO）-based hyper-heuristic with dynamic decision blocks is proposed, which selects appropriate heuristic rules for production and transportation simultaneously. On the basis of traditional hyper-heuristics, a dynamic decision block strategy is proposed, in which several entities are grouped into a decision block under the guidance of pheromones, and appropriate heuristic rules are selected for each decision block. Comparisons between the proposed method and other hyper-heuristics with different decision block strategies are conducted. Computational results show a satisfying performance of the proposed method in minimizing total weighted tardiness with less computational costs. TIAN Yun-Na,LI Dong-Ni,LIU Zhao-He,ZHENG Dan （ 1. Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing 100081 2. College of Mathem |
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| Publisher | 北京理工大学计算机学院智能信息技术北京市重点实验室 北京100081 延安大学数学与计算机科学学院 延安 716000%北京理工大学计算机学院智能信息技术北京市重点实验室 北京100081 |
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| SubjectTerms | 动态决策块 蚁群算法 超启发式 跨单元调度 |
| Title | 一种基于动态决策块的超启发式跨单元调度方法 |
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