基于混合人工鱼群算法的应急物流路径优化研究

doi:10.16381/j.cnki.issn1003-207x.2022.1672

摘要/Abstract

摘要：

突发事件的发生，会对国家发展和社会稳定造成一定程度的危害，需要及时做出应急响应。在突发事件发生初期，考虑受灾物资分配公平性的前提下建立应急物流路径优化的两阶段模型，并设计自适应混合人工鱼群算法进行求解，通过算例对模型及算法的可行性进行验证。实验结果表明，算法的迭代初期收敛速度较快，局部寻优能力较强，以及算法耗时得以改善，算法的有效性得以验证。

关键词: 应急物流, 车辆路径, 响应初期, 公平性, 人工鱼群算法

Abstract:

In recent years， emergencies have occurred frequently， causing a certain threat to the stable and prosperous development of the country and the happy and healthy life of the people. After the occurrence of emergencies， effective emergency material distribution should be taken in time to avoid more serious consequences. At the beginning of the corresponding stage， there is a shortage of supplies as well as road damage. When the supply of materials is insufficient， the dissatisfaction of the affected points is reduced as much as possible according to different needs， and the materials are distributed fairly. After the emergency， the road network will be damaged to different degrees， which in turn will affect the transportation time of supplies and aggravate the losses of the affected sites. The emergency logistics path optimization problem is addressed， taking into account the fair distribution of materials and road damage. A two-stage model of emergency logistics path optimization under the situation of road damage and lack of emergency supplies is established. And the adaptive hybrid artificial fish swarm algorithm is designed to solve the model， and the model and algorithm are validated by some data in Solomon's calculation case， revealing the influence of maximum vehicle load and driving speed. The results show that （1） compared with the artificial fish swarm algorithm， the AH-AFSA algorithm is able to obtain better results in solving the emergency logistics vehicle path optimization problem considering the fair distribution of materials and road damage， with a 12.9% improvement in total vehicle transportation time and a 29.0% improvement in algorithm running time. （2） Compared with AFSA， AH-AFSA algorithm can converge faster in the early stage of the algorithm and converge faster； it still has stronger local search capability in the late iteration of the algorithm and can find better results. （3） Finally， the changes of the maximum load and range of the vehicle affect the total vehicle transportation time. In general， the total vehicle transportation time decreases with the increase of the maximum load weight， and produces some volatility with the change of the vehicle range distance. These results provide good insights for the solution of the emergency vehicle path optimization problem under the situation of road damage and material shortage.

Key words: emergency logistics, vehicle path, early response, fairness, artificial fish swarm algorithm

中图分类号:

U492.22

刘艳秋, 胡绩辉. 基于混合人工鱼群算法的应急物流路径优化研究[J]. 中国管理科学, 2025, 33(8): 198-208.

Yanqiu Liu, Jihui Hu. Research on Emergency Logistics Path Optimization Based on Hybrid Artificial Fish Swarm Algorithm[J]. Chinese Journal of Management Science, 2025, 33(8): 198-208.

图/表 13

表1

参考文献 31

[1]	曾婷婷，宫阿都，陈艳玲，等. 基于历史相似案例空间推演的地震伤亡人口评估方法研究［J］. 地球信息科学学报， 2020， 22（11）： 2166-2176.
	Zeng T T， Gong A D， Chen Y L， et al. Study on assessment method of earthquake casualties based on spatial reasoning of similarly historical cases［J］. Journal of Geo-Information Science， 2020， 22（11）： 2166-2176.
[2]	高啸峰. 多配送中心应急物资配送车辆调度模型与算法研究［D］. 北京：首都师范大学硕士学位论文， 2011.
	Gao X F. Research on the dispatching model and algorithm of emergency material distribution vehicles in multi-distribution centers［D］. Beijing： Master Dissertation of Capital Normal University， 2011.
[3]	G B， Ramser J H. The truck dispatching problem［J］. Dantzig Management Science， 1959， 6（1）： 80-91.
[4]	Wei X， Chang X. The optimization design of emergency logistics distribution path based on ant colony algorithm［C］// Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation， Paris， May 16 ， Atlantis Press， 2016： 23-31.
[5]	Penna P H V， Santos A C， Prins C. Vehicle routing problems for last mile distribution after major disaster［J］. Journal of the Operational Research Society， 2018， 69（8）： 1254-1268.
[6]	Molina J， López-Sánchez A D， Hernández-Díaz A G， et al. A Multi-start Algorithm with Intelligent Neighborhood Selection for solving multi-objective humanitarian vehicle routing problems［J］. Journal of Heuristics， 2018， 24（2）： 111-133..
[7]	王娟，谭康业. 重大突发事件下应急物流车辆路径优化模型与算法［J］. 物流科技， 2021， 44（9）： 69-75.
	Wang J， Tan K Y. Emergency logistics vehicle routing problem optimization model and algorithm under major epidemic situation［J］. Logistics Sci-Tech， 2021， 44（9）： 69-75.
[8]	Yi J H， Wang J， Wang G G. Using monarch butterfly optimization to solve the emergency vehicle routing problem with relief materials in sudden disasters［J］. Open Geosciences， 2019， 11（1）： 391-413.
[9]	Jiang Y， Li L， Liu Z. A multi-objective robust optimization design for grid emergency goods distribution under mixed uncertainty［J］. IEEE Access， 2018（6）： 61117-61129.
[10]	张杏雯，倪静. 公平约束下的应急物资配送模型及算法［J］. 统计与决策， 2020， 36（7）： 179-182.
	Zhang X W， Ni J.A model and algorithm for distribution of emergency supplies under fairness constraints ［J］. Statistics & Decision， 2020， 36（7）： 179-182.
[11]	Khorsi M， Chaharsooghi S K， Bozorgi-Amiri A， et al. A multi-objective multi-period model for humanitarian relief logistics with split delivery and multiple uses of vehicles［J］. Journal of Systems Science and Systems Engineering， 2020， 29（3）： 360-378.
[12]	Huang M， Smilowitz K， Balcik B. Models for relief routing： Equity， efficiency and efficacy［J］. Transportation Research Part E： Logistics and Transportation Review， 2012， 48（1）： 2-18.
[13]	Balcik B， Beamon B M， Smilowitz K. Last mile distribution in humanitarian relief［J］. Journal of Intelligent Transportation Systems， 2008， 12（2）： 51-63.
[14]	Lien R W， Iravani S M R， Smilowitz K R. Sequential resource allocation for nonprofit operations［J］. Operations Research， 2014， 62（2）： 301-317.
[15]	Wex F， Schryen G， Feuerriegel S， et al. Emergency response in natural disaster management： Allocation and scheduling of rescue units［J］. European Journal of Operational Research， 2014， 235（3）： 697-708.
[16]	Liberatore F， Ortuño M T， Tirado G， et al. A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in Humanitarian Logistics［J］. Computers & Operations Research， 2014， 42： 3-13.
[17]	Ukkusuri V S， Yushimito F W，et al. Location routing approach for the humanitarian prepositioning problem［J］. Transportation Research Record： Journal of the Transportation Research Board， 2008， 2089： 18-25.
[18]	韩孟宜，丁俊武，陈梦覃，等. 基于混合遗传算法的应急物资配送路径优化［J］. 科学技术与工程， 2021， 21（22）： 9432-9439.
	Han M Y， Ding J W， Chen M Q， et al. Optimization of emergency material distribution path based on hybrid genetic algorithm［J］. Science Technology and Engineering， 2021， 21（22）： 9432-9439.
[19]	程碧荣，赵晓波，秦进. 考虑供应不足的应急物流车辆路径优化模型及算法［J］. 计算机应用研究， 2016， 33（6）： 1682-1685.
	Cheng B R， Zhao X B， Qin J. Optimization model and algorithm for emergency vehicle route with insufficiency supply［J］. Application Research of Computers， 2016， 33（6）： 1682-1685.
[20]	胡小宇，刘庆，贺文宁，等.基于粒子群算法的单仓储多车物流配送优化［J］.计算机应用，2018，38（S2）： 21-26.
	Hu X Y， Liu Q， He W N， et al. Optimizing multi-car logistics and distribution in single-storage center via particle swarm optimization［J］. Journal of Computer Applications， 2018， 38（S2）： 21-26.
[21]	丁乔，李旭，王建春. 结合DBSCAN聚类算法和粒子群算法的大规模路径优化方法研究［J］. 物流科技， 2020， 43（4）： 10-15.
	Ding Q， Li X， Wang J C. A large-scale path optimization method based on DBSCAN clustering algorithm and particle swarm optimization algorithm［J］. Logistics Sci-Tech， 2020， 43（4）： 10-15.
[22]	罗耀. 基于改进粒子群算法的车辆路径问题研究［J］. 交通科技与经济， 2016， 18（2）： 13-17.
	Luo Y. Research on vehicle routing problem based on improved particle swarm optimization algorithm［J］. Technology & Economy in Areas of Communications， 2016， 18（2）： 13-17.
[23]	Qian T， Gengjun G. Route optimization of emergency material distribution vehicle under uncertain demand［J］. Scientific Journal of Economics and Management Research， 2021， 3（3）：1-10.
[24]	赵志学，李夏苗. 时变交通下生鲜配送电动车辆路径优化方法［J］. 交通运输系统工程与信息， 2020， 20（5）： 218-225+239.
	Zhao Z X， Li X M. Electric vehicle route optimization for fresh logistics distribution based on time-varying traffic congestion［J］. Journal of Transportation Systems Engineering and Information Technology， 2020， 20（5）： 218-225+239.
[25]	周晓晔，马小云，崔瑶，等.单线超市车辆配置与路径问题优化研究［J］.工业工程，2020，23（6）：83-88+116.
	Zhou X Y， Ma X Y， Cui Y， et al. A research on optimization of vehicle allocation and routing for single-line supermarket［J］. Industrial Engineering Journal， 2020， 23（6）： 83-88+116.
[26]	任腾，陈玥，向迎春，等. 考虑客户满意度的低碳冷链车辆路径优化［J］. 计算机集成制造系统， 2020， 26（4）： 1108-1117.
	Ren T， Chen Y， Xiang Y C， et al. Optimization of low-carbon cold chain vehicle path considering customer satisfaction［J］. Computer Integrated Manufacturing Systems， 2020， 26（4）： 1108-1117.
[27]	Zhang L Y， Fei T， Sun Y S. The research about simulated annealing ant colony algorithm in emergency logistics path optimization［J］. Advanced Materials Research， 2012， 482-484： 2470-2474.
[28]	柳毅. 带回程取货车辆路径问题的人工鱼群算法研究［J］. 杭州电子科技大学学报， 2010， 30（3）： 75-77.
	Liu Y. Research vehicle routing problem with backhaul based on artificial fish swarm algorithm［J］. Journal of Hangzhou Dianzi University， 2010， 30（3）： 75-77.
[29]	Yuan M， Kan X， Chi C， et al. An adaptive simulated annealing and artificial fish swarm algorithm for the optimization of multi-depot express delivery vehicle routing［J］. Intelligent Data Analysis，2022，26（1）： 239-256.
[30]	Ouyang F. Research on port logistics distribution route planning based on artificial fish swarm algorithm［J］. Journal of Coastal Research， 2020， 115： 78-80.
[31]	李晓磊，邵之江，钱积新. 一种基于动物自治体的寻优模式：鱼群算法［J］. 系统工程理论与实践， 2002， 22（11）： 32-38.
	Li X L， Shao Z J， Qian J X. An optimizing method based on autonomous animats： Fish-swarm algorithm［J］. Systems Engineering-theory & Practice， 2002， 22（11）： 32-38.

符号	说明
$P$	配送中心的集合， $P = 0$
$Q$	受灾点的集合， $Q = 1,2, ⋯, n$
$N$	所有节点的集合，N $= P ⋃ Q$
$K$	运输车辆的集合K $= 1,2, ⋯, k$
$B$	所有受灾点的需求量之和
$d i j$	节点 $i$ 到节点 $j$ 的距离
$α i j$	节点 $i$ 到节点 $j$ 之间的道路受损系数
$b j$	受灾点 $j$ 的需求量
$c j$	受灾点 $j$ 的实际供应量
$w j$	受灾点 $j$ 对物资的依赖程度，数值越大，则依赖程度越大
$r$	受灾点的破坏程度
$n$	受灾点的数量
$β$	受灾点的基本需求满足率
$A$	配送中心的应急物资储备量
$m$	运输车辆的数量
$D$	运输车辆的最大装载量
$L$	运输车辆的最大行驶距离
$v$	运输车辆的正常行驶速度
$v i j'$	运输车辆在道路受损情况下的行驶速度
$t i j$	运输车辆从节点 $i$ 到节点 $j$ 的运输时间
$f j k$	0-1变量，受灾点 $j$ 由运输车辆 $k$ 服务时为1；否则为0
$g i j k$	0-1变量，运输车辆 $k$ 从节点 $i$ 行驶到节点 $j$ 为1；否则为0
$h j k$	0-1变量，运输车辆 $k$ 从配送中心行驶到节点 $j$ 为1；否则为0

编号	坐标	储备量/需求量	物资依赖程度	编号	坐标	储备量/需求量	物资依赖程度
0	(35,35)	360	0	16	(10,20)	19	1.4
1	(41,49)	10	1.1	17	(5,30)	2	1.3
2	(35,17)	7	1	18	(20,40)	12	1.3
3	(55,45)	13	1.1	19	(15,60)	17	1.5
4	(55,20)	19	1.5	20	(45,65)	9	1
5	(15,30)	26	1.1	21	(45,20)	11	1.4
6	(25,30)	3	1.2	22	(45,10)	18	1.3
7	(20,50)	5	1.7	23	(55,5)	29	1.4
8	(10,43)	9	1.3	24	(65,35)	3	1.2
9	(55,60)	16	1.1	25	(65,20)	6	1.1
10	(30,60)	16	1.1	26	(45,30)	17	1.4
11	(20,65)	12	1.2	27	(35,40)	16	1.4
12	(50,35)	19	1	28	(41,37)	16	1.6
13	(30,25)	23	1.2	29	(64,42)	9	1.7
14	(15,10)	20	1	30	(40,60)	21	1.2
15	(30,5)	8	1

参数名称	数值大小
人工鱼群数量	100
最大迭代次数	200
最大试探次数	500
初始视野	100
拥挤度因子	1

对比项目		AFSA	AH-AFSA
车辆运输路径	配送路线1	0-30-3-12-25-23-22-21-4-26-0	0-27-10-11-19-7-8-5-6-0
	配送路线2	0-28-1-27-0	0-1-30-20-9-12-21-15-2-0
	配送路线3	0-18-7-19-10-11-20-9-29-24-0	0-28-3-29-24-25-4-23-22-26-0
	配送路线4	0-5-17-8-16-14-15-2-0	0-18-17-16-14-13-0
	配送路线5	0-6-13-0	\
车辆运输总时间		114.5559	99.7106
运行时间		1829.662846s	1299.163484s

最大载重/千克	车辆数量	运输时间/小时	程序运行时间/秒
100	8	135.1765	2317.488006
150	5	115.9465	1100.933458
200	5	103.7484	1222.643139
250	4	97.5348	1191.910142
300	4	98.9846	1302.312438