| 进化式超启发算法求解多车型低碳选址-路径问题 |
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| 引用本文： | 赵燕伟,冷龙龙,王舜,张春苗.进化式超启发算法求解多车型低碳选址-路径问题[J].控制与决策,2020,35(2):257-271. |
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| 作者姓名： | 赵燕伟 冷龙龙 王舜 张春苗 |
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| 作者单位： | 浙江工业大学机械工程学院,杭州310014;浙江工业大学机械工程学院,杭州310014;嘉兴职业技术学院机电与汽车分院,浙江嘉兴314036 |
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| 基金项目： | 国家自然科学基金项目(61572438)；浙江省科技计划项目(2017C33224). |
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| 摘 要： | 为了降低物流配送成本和减少CO$_2$排放量,提出一种综合考虑多车型和同时取送货的低碳选址-路径问题,并构建三维指数混合整数规划模型.针对所提问题,设计一种进化式超启发式求解算法,即在超启发式算法框架下,采用进化式策略作为高层学习策略,以实时准确地监控底层算子的性能信息并选择合适的底层算子,包括量子选择、蚂蚁策略、蛙跳机制以及自然竞争等.同时,挖掘算子性能信息以构建自适应接收机制,引导全局搜索,加快算法收敛速度.通过对不同规模实例的仿真实验与对比分析,验证了4种进化式超启发式算法在求解物流配送多车型同时取送货低碳选址-路径问题模型上的有效性与鲁棒性.
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| 关 键 词： | 低碳选址-路径问题 同时取送货 多车型 进化式超启发式算法 自适应接收机制 |
Evolutionary hyper-heuristics for low-carbon location-routing problem with heterogeneous fleet |
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| ZHAO Yan-wei,LENG Long-long,WANG Shun and ZHANG Chun-miao.Evolutionary hyper-heuristics for low-carbon location-routing problem with heterogeneous fleet[J].Control and Decision,2020,35(2):257-271. |
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| Authors: | ZHAO Yan-wei LENG Long-long WANG Shun and ZHANG Chun-miao |
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| Affiliation: | College of Mechanical Engineering,Zhejiang University of Technology,Hangzhou 310014,China,College of Mechanical Engineering,Zhejiang University of Technology,Hangzhou 310014,China,College of Mechanical Engineering,Zhejiang University of Technology,Hangzhou 310014,China and College of Mechanical Engineering,Zhejiang University of Technology,Hangzhou 310014,China;Mechanical and Automotive Branches,Jiaxing Vocational and Technology College,Jiaxing 314036,China |
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| Abstract: | Aiming at reducing logistics cost and carbon emission, a low-carbon location-routing problem considering simultaneous pickup and delivery and heterogeneous fleet(LCLRPSPDHF) is proposed, and a three-index exponential-size MIP model is defined. Aiming at this project, an evolutionary-heuristic(HH) algorithm is developed by utilizing evolutionary mechanisms as high level learning strategies to improve the performance of hyper-heuristic framework to monitor the performance information of low-level heuristics(LLH) timely and rapidly, and judge for choosing the most appropriate heuristic rightly, including quantum-inspired selection(QS), ant-based selection(AS), shuffled frog selectionn(LS) and nature-competition selection(NCS). Meanwhile, two adaptive acceptance criteria are developed by mining information of LLHs for realizing global search and improving convergence. Simulation results and comparisons show that the proposed algorithms are effective and robust, providing high quality solution for different scales instances within reasonable computing time. |
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