基于同步循环取货的多行程汽车零部件入厂物流优化研究

doi:10.16381/j.cnki.issn1003-207x.2023.1833

Abstract

Abstract:

Parts inbound logistics refers to the process of managing the transportation， storage， and handling of parts or components as they move from suppliers to a manufacturer. This is particularly critical in the new energy vehicle industry， where the variety of models and personalized components requires optimized logistics for incoming parts to enhance competitive advantage. Synchronous pickup is one of the important means of parts inbound logistics for just-in-time production. Personalized demand for high and large parts needs to be supplied to mixed-model assembly lines on time. In order to ensure the parts timely delivery， direct delivery mode is usually used to ensure synchronous pickup. Although it can ensure the timeliness of transportation， it suffers from low logistical efficiency and high empty return rates.In order to achieve better synchronous pickup， a new synchronous milk-run mode is proposed by combining milk-run with synchronous pickup. Its specific process is given as follows. First， automotive manufacturer splits parts demand of synchronous workstations in a mixed-model assembly line and sends the demand to suppliers in advance. Second， the third-party logistics company adopts the multi-trip synchronous milk-run mode to pick up parts and deliver to synchronous workstations directly within strict delivery deadline.A mixed-integer programming model based on multi-trip vehicle routing problem is built by taking into account the characteristics of demand splitting of parts and synchronous delivery of parts. For the proposed model， an improved adaptive large neighborhood search （IALNS） algorithm is designed. It adopts a pushforward insertion heuristic to generate the initial solution and designs several problem-related destroy and repair operators to generate high-quality offspring solutions. Additionally， a new perturbation-based post-local search is proposed to improve global and local search capabilities. Numerical experimental results demonstrate excellent performance and speed of the proposed IALNS compared to Gurobi， variable neighborhood search and genetic algorithms in different-scale instances. Sensitivity experiments reveal that as delivery lead times become more lenient， the average vehicle travel distance initially increases before stabilizing gradually， while transportation costs show a decreasing trend. The proposed approach can effectively improve vehicle utilization efficiency， reduce inbound logistics costs for parts， and provide theoretical support for inbound logistics in automotive manufacturers.

Key words: parts inbound logistics, synchronous milk-run pickup, adaptive large neighborhood search, multi-trip vehicle routing problem

CLC Number:

C931

Jian Chen,Qiang Xue,Hong Zhou, et al. Multi-trip Automotive Parts Inbound Logistics Optimization Based on Synchronous Milk-run Pickup[J]. Chinese Journal of Management Science, 2026, 34(11): 210-218.

Figures/Tables 14

算法	参数设置
IALNS	$g m a x = 200$ , $ε = 50$ , $ϕ = 50$ , $T m a x = 1000$ , $T m i n = 1$ , $ζ = 0.99$
VNS	$g m a x = 200$
GA	$g m a x = 200$ , $p o p s i z e = 100$ , $p c = 0.8$ , $p m = 0.2$

$ξ$	$η$	$p m a x = 50$		$p m a x = 100$		$p m a x = 200$		$p m a x = 300$
$ξ$	$η$	运输费用	行程数	运输费用	行程数	运输费用	行程数	运输费用	行程数
30	0.25	623.8	1.4	1318.2	2.4	2299.0	2.8	3528.6	3.2
30	0.5	622.1	1.8	1283.5	2.6	2269.9	3.2	3485.3	3.4
30	0.75	621.5	1.8	1267.5	3.0	2258.0	3.2	3468.4	3.8
80	1	618.7	5.5	1258.3	4.6	2255.4	6.8	3463.3	9.0
80	1.25	617.2	5.5	1248.2	4.6	2240.2	8.4	3455.2	9.0
80	1.5	614.4	5.5	1255.6	5.8	2237.6	6.8	3425.6	9.0
130	1.75	608.4	10.0	1236.3	7.3	2237.0	10.3	3413.8	12.4
130	2	606.3	10.0	1232.8	7.3	2233.4	11.5	3411.8	15.5
130	2.25	604.8	10.0	1231.3	11.0	2231.4	13.7	3397.9	15.5
180	2.5	603.0	10.0	1229.5	11.0	2231.1	13.7	3382.1	20.3
180	2.75	599.5	10.0	1227.7	11.0	2230.1	13.7	3378.9	20.7
180	3	596.4	10.0	1226.9	11.0	2229.1	13.7	3370.0	20.7

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数值算例	车辆数	零部件数量
小规模	1,2,3	5,10,15
中规模	5,6,7	50,75,100,125
大规模	10,12,15	150,200,250,300

算例	Gurobi		IALNS
算例	Cost	Time	Cost	Time	Gap
1-5	76.7	3.7	76.7	0.9	0.0
2-5	75.8	3.5	75.8	0.6	0.0
3-5	75.5	2.3	75.5	0.5	0.0
1-10	142.7	99.7	143.0	2.0	0.2
2-10	143.0	88.4	143.0	1.5	0.0
3-10	143.0	56.4	143.0	1.3	0.0
1-15	191.2	1563.5	193.2	3.4	1.0
2-15	186.7	1378.3	187.7	3.0	0.5
3-15	184.9	2041.1	185.9	2.9	0.5
平均	135.5	581.9	136.0	1.8	0.3

实例	IALNS			VNS		GA
实例	Cost	Time	Cost	Time	Gap	Cost	Time	Gap
5-50	620.0	12.5	658.0	25.2	6.1	712.7	43.1	15.0
5-75	938.1	31.1	967.7	69.7	3.2	1058.0	73.2	12.8
5-100	1229.6	75.7	1290.4	121.4	4.9	1335.9	122.8	8.6
5-125	1406.2	140.5	1465.4	190.3	4.2	1488.6	147.5	5.9
6-50	621.9	13.1	647.8	30.9	4.2	702.6	43.2	13.0
6-75	968.1	25.8	935.2	69.6	-3.4	1024.2	74.3	5.8
6-100	1262.1	58.1	1313.0	105.8	4.0	1311.6	117.4	3.9
6-125	1394.0	104.6	1458.3	153.2	4.6	1516.2	175.8	8.8
7-50	617.0	13.9	644.9	31.1	4.5	698.7	41.0	13.2
7-75	929.0	32.9	973.0	67.2	4.7	1014.4	80.4	9.2
7-100	1263.1	74.7	1322.8	111.6	4.7	1338.2	117.6	5.9
7-125	1414.4	199.3	1478.9	175.5	4.6	1522.2	174.9	7.6
平均	1055.3	65.2	1096.3	96.0	3.9	1143.6	100.9	9.1

实例	IALNS			VNS		GA
实例	Cost	Time	Cost	Time	Gap	Cost	Time	Gap
10-150	1670.3	115.7	1814.0	279.6	8.6	1954.0	320.0	17.0
10-200	2256.9	253.8	2349.9	419.4	4.1	2532.2	684.4	12.2
10-250	2961.5	339.9	2941.5	625.8	-0.7	3177.4	884.1	7.3
10-300	3392.8	747.4	3536.7	890.5	4.2	3641.4	1227.2	7.3
12-150	1680.5	106.3	1772.8	254.0	5.5	1833.9	447.8	9.1
12-200	2280.4	317.1	2396.4	500.3	5.1	2522.9	735.2	10.6
12-250	3029.0	389.5	3045.24	629.9	0.5	3217.7	991.9	6.2
12-300	3429.6	727.1	3544.3	970.3	3.3	3674.8	1308.8	7.1
15-150	1679.2	236.4	1766.5	298.2	5.2	1855.3	552.3	10.5
15-200	2240.3	313.6	2360.8	485.9	5.4	2438.8	933.1	8.9
15-250	2932.8	413.2	3105.5	652.0	5.9	3367.0	1247.7	14.8
15-300	3413.5	685.6	3572.5	1179.8	4.7	3622.0	1577.9	6.1
平均	2580.5	387.1	2683.9	598.8	4.3	2819.8	909.2	9.8

零部件数量	小零部件		中零部件		大零部件
零部件数量	运输费用	所需车辆数	运输费用	所需车辆数	运输费用	所需车辆数
10	143.0	1	190.6	1	251.1	1
20	210.7	1	339.7	2	494.6	2
30	305.1	2	472.1	2	650.8	3
40	397.9	2	536.7	2	1055.3	4
50	458.6	2	734.2	3	1228.0	4
75	703.4	3	927.1	4	1686.7	6
100	914.3	3	1495.1	6	2327.3	8
125	1061.6	4	1824.4	6	2750.7	9
150	1378.0	4	2164.6	7	3520.2	10
200	1698.3	6	2864.8	9	4433.1	13
250	2150.6	7	3622.6	11	5532.1	15
300	2582.0	8	4270.0	12	6456.1	17

Multi-trip Automotive Parts Inbound Logistics Optimization Based on Synchronous Milk-run Pickup

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