| 引用本文: | 孙亮,潘全科,邹温强,王亚敏.车辆路径问题的轻鲁棒优化模型与算法[J].控制理论与应用,2021,38(2):206~212.[点击复制] |
| SUN Liang,PAN Quan-ke,ZOU Wen-qiang,WANG Ya-min.Vehicle routing problem: light-robust-optimization model and algorithm[J].Control Theory and Technology,2021,38(2):206~212.[点击复制] |
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| 车辆路径问题的轻鲁棒优化模型与算法 |
| Vehicle routing problem: light-robust-optimization model and algorithm |
| 摘要点击 2387 全文点击 732 投稿时间：2019-11-20 修订日期：2020-08-19 |
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| DOI编号 10.7641/CTA.2020.90957 |
| 2021,38(2):206-212 |
| 中文关键词 鲁棒优化 超启发式算法 车辆路径问题 粒子群算法 |
| 英文关键词 robust optimization hyper-heuristics vehicle routing problem particle swarm optimization |
| 基金项目 国家自然科学基金项目(61973203, 51575212)资助. |
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| 中文摘要 |
| 针对不确定旅行时间下的车辆路径问题, 以总变动成本最小为优化目标, 建立了一种轻鲁棒优化模型, 提
出了一种针对问题特征的超启发式粒子群算法. 在算法中, 利用基于图论中深度优先搜索的初始化策略加快算法
的早期收敛速度, 引入基于均衡策略的启发式规则变换方式来提高算法的寻优能力, 重新设计的粒子更新公式确保
生成低层构造算法的有效性. 实验结果表明: 所提算法能有效地求解不确定旅行时间下的车辆路径问题. |
| 英文摘要 |
| A light-robust-optimization model is proposed for a vehicle routing problem with uncertain travel times
(VRP–UT) with the objective of minimizing total variable cost. A hyper particle swarm optimization (HPSO) including
problem-specific knowledge is proposed to solve the model. The proposed HPSO algorithm uses an initialized strategy
based on deep first search in graph theory to accelerate the convergent speed at the early stage, and adopts two novel
updating rules to generate efficient constructive algorithms, as well as employs some search algorithms based on balance
strategy to enhance the optimization capability. Experimental results show that HPSO is an efficient algorithm to solve
VRP–UT. |