考虑时变交通拥堵的纯电动物流车路径规划模型

doi:10.13229/j.cnki.jdxbgxb20211071

Abstract

Abstract:

In order to plan a reasonable scheme for the fleet travel and minimize the energy consumption of electric vehicles during traffic congestion periods， while relieving the mileage anxiety of delivery personnel， The pure electric delivery vehicle routing problem considering time-varying traffic congestion was studied. Firstly， a novel mixed integer programming model（TD-EVRP） is established to step for its extended model of EVRP， which especially characterizes the time-varying traffic congestion state and measures the energy consumption of electric delivery vehicles. This model also refines its constraints including customer time window， vehicle load， and vehicle charging requirements. Secondly， a traffic congestion avoidance strategy is introduced into the model， which allows vehicles to park and charge at customer points during peak traffic periods in order to actively avoid unnecessary energy consumption caused by traffic congestion. Finally， the improved ant colony algorithm is designed to acquire the optimal solution. The comparison analysis shows that the TD-EVRP model with avoidance strategy constructed than without scenario can significantly reduce the energy consumption by 14.91% in small-scale case.

Key words: transportation planning, time-varying traffic congestion, vehicle routing problem, electric delivery vehicles, energy consumption, traffic avoidance strategy

CLC Number:

N945.15

Bao-feng SUN,Tian-zi YAO,Yu-qi CHEN. Electric delivery vehicle routing problem optimization model with time⁃varying traffic congestion[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 468-479.

Figures/Tables 8

Fig.1

Fig.2

Table 1

Electric logistics vehicle related parameters"

参数	含义	赋值	参数	含义	赋值
$m$	汽车整备质量	2800 kg	$B m a x$	车辆电池最大电量	80 kW·h
$Q m a x$	车辆最大载重量	1550 kg	$B m i n$	车辆最低安全电量	20 kW·h
$g$	重力加速度	9.81 m/s²	$r$	充电速率	65 kW
$f$	滚动阻力系数	0.015	$η$	综合能量转换效率	0.9
$ρ a$	空气密度	1.20 kg/m³	$C d$	空气阻力系数	0.7
$A$	车辆迎风面积	4.912 m²	$P a u x$	辅助设备消耗功率	1.2 kW

Table 1

Table 2

Table 3

Minimization of total energy consumption for delivery scheduling under traffic congestion avoidance"

车辆编号	行驶路径顺序 $r o u t e$ （n*为安全充电点）	拥堵规避等待时间 $t w i$ /min	总能源消耗 $B$ /（kW·h）	总配送时间 $T$ /min
合计	—	202	197.811	1463.982
1	0—2—3*—4—5—7— —11—6—10—9—0	73 （8：07~9：20）	65.343	491.996
2	0—8—1—15*—20— —22—13—12—18—0	54 （8：36~9：30）	66.866	475.561
3	0—17—16*—14—19— —21—23—24—25—0	75 （8：25~9：40）	65.602	496.425

Table 3

Table 4

Minimization of total energy consumption for delivery scheduling without traffic congestion avoidance"

车辆编号	行驶路径顺序 $r o u t e$ （n* 为安全充电点）	安全电量充电时间 $t c i$ /min	总能源消耗 $B$ /（kW·h）	总配送时间 $T$ /min
合计	—	146	232.477	1469.924
1	0—2—3—4—5—1— —8—9*—11—7—0	52 （11：45~12：37）	83.706	518.218
2	0—17—6—10—12— —18—21—19—20*—0	50 （11：38~12：28）	64.068	426.896
3	0—13—22—23—24- —15—14*—16—25—0	44 （11：49~12：33）	84.703	524.810

Table 4

Table 5

Comparison of the results of delivery schemes under different strategies"

运行次数	交通拥堵规避策略		无-交通拥堵规避策略
运行次数	总能源消耗 $B$ /（kW·h）	总配送时间 $T$ /min	总能源消耗 $B$ /（kW·h）	总配送时间 $T$ /min
平均值	202.028	1512.778	237.439	1485.029
1	201.948	1483.599	232.739	1453.933
2	205.305	1531.255	240.053	1511.916
3	204.957	1556.283	239.822	1495.983
4	197.811	1463.982	239.130	1486.531
5	198.001	1488.151	238.936	1473.681
6	201.331	1503.640	237.478	1494.426
7	209.878	1578.205	239.105	1509.826
8	198.389	1496.955	236.231	1470.201
9	203.801	1527.617	232.477	1469.924
10	198.859	1498.096	238.421	1483.871

Table 5

Table 6

Comparison of the results of delivery schemes under different optimization objectives"

运行次数	总能源消耗最小化		路径最短
运行次数	*总能源消耗 $B$ /（kW·h）	总行驶距离 $D$ /km	总能源消耗 $B$ /（kW·h）	*总行驶距离 $D$ /km
平均值	202.028	552.335	220.665	525.349
1	201.948	533.560	217.631	523.270
2	205.305	550.470	219.334	528.860
3	204.957	555.280	219.627	526.670
4	197.811	543.590	222.478	530.740
5	198.001	543.780	217.455	519.390
6	201.331	531.390	218.581	522.760
7	209.878	583.360	220.281	524.140
8	198.389	559.150	225.395	530.120
9	203.801	563.140	216.811	520.980
10	198.859	559.630	229.055	526.560

Table 6

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客户点	X坐标	Y坐标	需求量/kg	时间窗	卸货时间/min
0	116.32943	39.814713	-	6：00-15：00	-
1	116.354901	39.896337	159	6：00-14：00	10
2	116.40233	39.964433	140	6：00-14：00	10
3	116.313522	39.883873	142	6：00-14：00	10
4	116.298209	39.842642	146	6：00-14：00	10
5	116.364964	39.845856	142	6：00-14：00	10
6	116.407664	39.871374	153	6：00-14：00	10
7	116.484582	39.821648	151	6：00-14：00	10
8	116.438223	39.860328	166	6：00-14：00	10
9	116.379135	39.939551	147	6：00-14：00	10
10	116.416312	39.919143	140	6：00-14：00	10
11	116.451524	39.804498	158	6：00-14：00	10
12	116.472741	39.910138	151	6：00-14：00	10
13	116.275678	39.869714	166	6：00-14：00	10
14	116.217519	39.899905	170	6：00-14：00	10
15	116.496211	39.953888	161	6：00-14：00	10
16	116.284865	40.007883	153	6：00-14：00	10
17	116.471175	39.870765	172	6：00-14：00	10
18	116.423122	39.890287	178	6：00-14：00	10
19	116.269478	39.893595	148	6：00-14：00	10
20	116.316691	39.921397	163	6：00-14：00	10
21	116.30787	39.965447	170	6：00-14：00	10
22	116.294622	39.916683	161	6：00-14：00	10
23	116.234761	39.951258	151	6：00-14：00	10
24	116.277199	39.932008	140	6：00-14：00	10
25	116.335601	39.967881	143	6：00-14：00	10

Electric delivery vehicle routing problem optimization model with time⁃varying traffic congestion

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