摘要
应急物流系统是解决突发事件的有效框架体系,如何选择合适的配送路径以确保受灾群众及时获取物资,对解决应急救援问题有重要意义。通过建立应急物流路径优化模型,考虑到使用人工萤火虫算法会产生寻优精度低以及陷入局部最优等问题,为了提高系统优化性能,参照蜂群和粒子群的群体移动规律,改进萤火虫算法的位置更新策略,引入两种群智能混合算法进行比较实验。设置距离参数和平均交通复杂度,计算物流运输路径系统消耗时间,并采取表格形式显示。实验结果表明群智能混合算法能大幅度优化应急物流路径规划模型,提高配送效率。
Emergency logistics system is an effective framework to solve emergencies. How to choose the appropriate dis- tribution path to ensure the timely access of goods to the affected people is of great significance for solving the problem of emergency rescue. Through the establishment of emergency logistics path optimization model, taking into account that the use of artificial firefly algorithm will produce low precision and fall into local optimization problems, and in order to improve the performance of the system, the locating strategy of the firefly algorithm is improved by referring to the swarmg movement rule of bee colony and particle swarm. And two swarm intelligence hybrid algorithms are introduced to compare experiments. The distance parameters and average traffic complexity are set, the lo of a table is used to display the emergency logistics path The experimental results show that gls th tics transport path system time is calculated, and the form e swarm intelligence hybrid algorithm can greatly optimize planning model and improve the delivery efficiency
作者
吴新胜
姜婷
赵梦超
张萍
孔令成
WU Xinsheng;JIANG Ting;ZHAO Mengchao;ZHANG Ping;KONG Lingcheng(Department of Information Engineering,Anhui Economic Management Cadre College,Hefei 230051,China;School of Information Science and Engineering,Changzhou University,Changzhou 213164,China;Institute of Advanced Manufacturing Technology-,Hefei Institute of Material Science,Chinese Academy of Sciences, Hefei 230051,China)
出处
《四川理工学院学报（自然科学版）》
CAS
2018年第4期68-73,共6页
Journal of Sichuan University of Science & Engineering(Natural Science Edition)
基金
安徽省高校自然科学研究重点项目(KJ2018A0683
KJ2018A0684)
合肥市哲学社会科学规划项目(HFSKYY201835)
安徽省高等教育振兴计划项目((2014zdjy193)
关键词
应急物流
萤火虫算法
人工蜂群算法
粒子群算法
模型
emergency logistics
firefly algorithm
artificial bee colony algorithm
particle swarm optimization
model
