Research on Vehicle Path Optimization Algorithms for Urban Logistics and Distribution

doi:10.16182/j.issn1004731x.joss.24-0639

Abstract

Abstract:

Existing optimization algorithms for solving the vehicle routing problem with time windows (VRPTW) are prone to fall into local optimal solutions and have slow convergence speed. To address this issue, a K-means clustering algorithm and improved large neighborhood search algorithm (K-means-ILNSA) was proposed. A strategy of clustering before optimization was adopted, and the K-means algorithm was adopted to group the customers to be delivered, so as to improve the optimization efficiency. The genetic algorithm was adopted to optimize each group of customers generated by clustering separately to initially plan the distribution routes. The large neighborhood search (LNS) algorithm was introduced to further optimize the delivery routes, effectively avoiding the algorithm getting trapped in local optimal solutions. Experimental results show that the proposed algorithm can effectively solve the vehicle path problem with time windows, and the generated total distance of vehicle is short. The solving efficiency after optimization is high.

Key words: vehicle routing problem with time window(VRPTW), genetic algorithm, K-means clustering, large neighborhood search

CLC Number:

TP391.9

Ma Zhenpeng, Jiao Hanyang, Zhang Zhe, Liu Cheng, Jiang Bo, Wang Lin. Research on Vehicle Path Optimization Algorithms for Urban Logistics and Distribution[J]. Journal of System Simulation, 2025, 37(11): 2768-2777.

Figures/Tables 9

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算例	顾客规模	K-means-ILNSA		WPWOA			DBSCAN/SAVN
算例	顾客规模	车辆数	路程	车辆数	路程	路程偏差/%	车辆数	路程	路程偏差/%
C101	25	3	191.83	3	191.83	0	3	191.83	0
C101	50	5	363.24	5	426.40	‒14.81	5	363.24	0
C101	100	10	828.94	10	923.38	‒10.23	10	828.94	0
C103	25	3	190.83	3	190.83	0	3	190.83	0
C103	50	5	362.17	5	523.49	‒30.82	5	363.97	‒0.49
C103	100	10	828.94	10	1 052.10	‒21.21	10	830.06	‒0.13
C104	25	3	190.83	3	191.07	‒0.13	3	190.83	0
C104	50	5	362.1	5	527.15	‒31.30	5	363.46	‒0.35
C104	100	10	828.94	10	1 130.90	‒26.70	10	828.94	0
C201	25	2	215.57	3	228.58	‒5.69	2	215.57	0
C201	50	3	361.82	3	398.55	‒9.22	3	365.58	‒1.03
C201	100	3	626.80	3	707.92	‒11.46	3	650.24	‒3.60

算例	顾客规模	WPWOA	DBSCAN/ SAVN	K-means-ILNSA
C101	25	48.479	15.637	8.313
C101	50	150.585	30.988	10.408
C101	100	341.757	50.989	29.851
C103	25	48.007	15.723	8.676
C103	50	135.757	31.810	14.297
C103	100	386.347	55.602	37.758
C104	25	56.299	22.761	8.826
C104	50	145.851	29.277	13.225
C104	100	358.357	53.352	36.848
C201	25	85.584	53.586	25.982
C201	50	268.569	98.598	50.587
C201	100	359.854	216.497	112.347

算例	车辆数	顾客规模	K-means-ILNSA	WPWOA		DBSCAN/SAVN		DPC/TS
算例	车辆数	顾客规模	路程	路程	路程偏差/%	路程	路程偏差/%	路程	路程偏差
C101	10	100	828.94	953.38	‒13.05	828.94	0.00	828.94	0
C102	10	100	828.94	923.53	‒10.24	828.94	0.00	828.94	0
C103	10	100	828.94	1 052.10	‒21.21	830.06	‒0.13	828.94	0
C104	10	100	828.94	1 130.90	‒26.70	832.94	‒0.48	828.94	0
C105	10	100	828.94	934.42	‒11.29	828.94	0.00	828.94	0
C106	10	100	828.94	981.55	‒15.55	828.94	0.00	828.94	0
C107	10	100	828.94	951.23	‒12.86	828.94	0.00	828.94	0
C108	10	100	828.94	931.55	‒11.01	828.94	0.00	828.94	0
C109	10	100	828.94	984.57	‒15.81	828.94	0.00	828.94	0

算例	顾客规模	WPWOA	DBSCAN/SAVN	DPC/TS	K-means-ILNSA
C101	100	341.757	50.989	36.462	29.851
C102	100	385.758	45.952	35.864	30.451
C103	100	386.347	55.602	64.473	37.758
C104	100	358.357	53.352	47.753	36.848
C105	100	375.697	49.312	38.245	29.286
C106	100	356.788	48.313	35.866	28.909
C107	100	352.347	47.377	42.545	29.616
C108	100	378.587	49.698	45.563	30.406
C109	100	386.857	50.654	47.684	29.111

算例	车辆数	顾客规模	K-means-ILNSA	WPWOA		DBSCAN/SAVN		DPC/TS
算例	车辆数	顾客规模	路程	路程	路程偏差/%	路程	路程偏差/%	路程	路程偏差/%
C201	3	100	626.81	707.92	‒11.46	650.24	‒3.60	645.26	‒2.86
C202	3	100	678.53	739.37	‒8.23	690.94	‒1.80	684.26	‒0.84
C203	3	100	705.29	800.91	‒11.94	741.83	‒4.93	733.28	‒3.82
C204	3	100	650.57	831.24	‒21.73	770.62	‒15.58	697.43	‒6.72
C205	3	100	629.94	895.25	‒29.64	795.58	‒20.82	645.67	‒2.44
C206	3	100	620.27	751.98	‒17.52	644.35	‒3.74	647.84	‒4.26
C207	3	100	647.19	861.28	‒24.86	713.08	‒9.24	697.56	‒7.22
C208	3	100	620.27	798.58	‒22.33	782.69	‒20.75	665.46	‒6.79