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

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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