基于模块化A*寻优算法的地铁货车快递联运系统

doi:10.13229/j.cnki.jdxbgxb.20241293

Abstract

Abstract:

In order to alleviate the pressure of urban transportation， in order to further improve the transportation efficiency. This paper designs evaluation indexes according to the theory of complex transportation network nodes， combines the multi-objective decision-making method of hierarchical analysis （AHP-RatioMOORA）， ranks the importance of subway stations and calculates the weights， and selects the location of subway logistics and distribution hubs according to the importance. In order to solve the traditional optimization algorithm in the complex constraints there are convergence difficulties， global search can be poor， this paper through the A* algorithm to improve the structure， the establishment of a modular optimization algorithm， the subway truck express intermodal task analysis modeling， combined with a variety of constraints of the improvement of the objective function of the task of the case study analysis. The results of the case study show that the method proposed in this paper has a significant improvement compared with other optimization algorithms in the subway truck express intermodal transport task， the shortest logistics transportation distance after optimization is reduced by 13.21% on average， the logistics order completion time is shortened by 11.73% on average， and the algorithm running time is shortened by 24.79% on average.

Key words: urban transportation, subway network, A^* algorithm, RadioMOORA model, genetic algorithm, multi-objective collaborative optimization

CLC Number:

TP301.6

Ming LIU,Si-wei CHEN,Jun-jie WANG,Yu-xuan LIANG,Xiao-dong YU. Subway truck express transport system based on improved modular A^* optimization algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2026, 56(7): 1937-1949.

Figures/Tables 15

Fig.1

Fig.2

Fig.3

Table 1

Parameter setting of metro freight car logistics system"

参数	含义
$A$	地铁站点， $i ∈ A$
$B$	配送点 $j ∈ B$
$p$	寄件人 $p$ 位置， $p ∈ I$
$q$	收件人 $q$ 位置， $q ∈ L$
$C 1$	货车单位距离行驶成本
$C 2$	地铁单位距离行驶成本
$S p q 1$	从寄件人 $p$ 位置取货送至收件人 $q$ 位置，货车所行驶的总距离
$S p q 2$	从寄件人 $p$ 位置取货送至收件人 $q$ 位置，地铁所行驶的总距离
$d p q$	寄件人 $p$ 位置到收件人 $q$ 位置的距离长度
$φ 1$	未按寄件人预订的时间区间取件，单位时间惩罚系数
$φ 2$	未按收件人预订的时间区间送达，单位时间惩罚系数
$T p$	实际到寄件人 $p$ 位置取到货物时间
$T q$	实际将货物到收件人 $q$ 位置取件时间
$E p i, E p j$	寄件人 $p$ 预订的上门取件时间，其中 $E p i$ 为最早取件时间， $E p j$ 为最迟取件时间
$E q i, E q j$	寄件人 $q$ 预订的上门取件时间，其中 $E q i$ 为最早取件时间， $E q j$ 为最迟取件时间
$U$	货车集合， $v ∈ U$
$G v$	货车 $v$ 的最大载货量

Table 1

Table 2

Solve the decision variables related to the express transportation problem of metro trucks"

决策变量	含义
$x v p j$	从寄件人 $p$ 位置配送订单货物到配送点 $j$ 位置，货车不为空服务时为1，否则为0
$x v j i$	从配送点 $j$ 配送订单货物到 $i$ 地铁站，货车不为空服务时为1，否则为0
$x v i j$	从 $i$ 地铁站将订单货物配送至 $j$ 配送点使用货车 $v$ 时不为空服务时为1，否则为0
$x v j q$	将订单货物从 $j$ 配送点配送至收件人 $q$ 位置，货车 $v$ 不为空服务时为1，否则为0。
$y i 1 i 2$	订单货物由地铁站 $i 1$ 转运至地铁站 $i 2$ ，该次列车不为空服务时为1，否则为0

Table 2

Table 3

Routing priority and connectivity of subway stations in Shanghai's metro network"

序号	地铁站名称	路由优先度	连通的地铁路线
1	曹杨路	0.346 1	3、4、11、14号线
2	金沙江路	0.345 4	3、4、13号线
3	静安寺	0.345 2	2、7、14号线
$?$	$?$	$?$	$?$
445	金海湖	0.332 0	5号线
446	东方绿舟	0.331 7	17号线
447	奉贤新城	0.331 5	5号线

Table 3

Fig.4

Fig.5

Table 4

Renovation of subway station site selection"

序号	改造中转站名称	连通的地铁路线	路由优先度
1	曹杨路	3、4、11、14号线	1
2	金沙江路	3、4、13号线	0.948 8
3	静安寺	2、7、14号线	0.935 1
$?$	$?$	$?$	$?$
44	南京东路	2、10号线	0.759 2

Table 4

Table 5

Logistics order fulfillment information"

序号	寄件人位置				重量/kg	收件人位置
序号	经度	纬度	上门取件时间区间		重量/kg	经度	纬度	货物送达时间区间
1	31.368 1	121.603 4	9：00	9：59	2.16	31.338 2	121.569 8	19：00	19：59
2	31.437 2	121.190 1	11：00	11：59	2.82	31.260 4	121.581 4	13：00	13：59
$?$	$?$	$?$	$?$	$?$	$?$	$?$	$?$	$?$	$?$
20	31.400 4	121.589 8	8：00	8：59	4.48	31.124 5	121.653 1	15：00	15：59

Table 5

Table 6

Fig.6

Table 7

Table 8

Table 9

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参数	数值
初始种群规模	200
最大迭代次数	2 000
交叉概率	0.6
变异概率	0.05
未按时间窗取送件惩罚成本	100
货车运输单位距离运输成本	0.3
地铁运输单位距离运输成本	0.5

算法	优化前距离/km	优化后距离/km	性能提升比率/%
EGA	1 662.94	1 527.46	8.14
GGAP-SAP	1 611.93	1 604.84	0.439
SEGA	1 888.89	1 544.69	18.22
steadyGA	1 690.49	1 563.94	7.49
studGA	1 618.56	1 544.02	4.61
Multi-SEGA	3 245.42	1 576.77	51.46
SGA	1 575.85	1 543.11	2.07

算法	优化前订单完成时间/min	优化后订单完成时间/min	性能提升比率/%
EGA	3 945.977	3 624.509	8.14
GGAP-SAP	3 824.941	3 808.127	0.439
SEGA	4 482.142	3 665.388	18.22
steadyGA	4 011.365	3 711.074	7.48
studGA	3 468.757	3 663.788	-5.62
Multi-SEGA	7 701.041	3 741.505	51.41
SGA	3 739.326	3 661.645	2.07

算法	优化前运行时间/s	优化后运行时间/s	性能提升比率/%
EGA	40.604	30.026	26.05
GGAP-SAP	38.461	40.195	-4.31
SEGA	46.428	39.806	14.26
steadyGA	27.708	3.716	86.58
studGA	40.519	40.446	0.18
Multi-SEGA	58.318	37.324	35.99
SGA	35.325	30.090	14.82

Subway truck express transport system based on improved modular A^* optimization algorithm

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Subway truck express transport system based on improved modular A* optimization algorithm

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Subway truck express transport system based on improved modular A^* optimization algorithm