电动车-无人机协同配送模式下带时间窗的车辆路径优化问题

doi:10.16381/j.cnki.issn1003-207x.2023.0447

Abstract

Abstract:

With national efforts to achieve carbon neutrality goals， electric vehicles have gradually become the preferred choice for logistics enterprises. However， electric vehicles require planning additional charging routes during delivery， which results in high logistics costs. To reduce the logistics cost of the electric vehicle delivery system， integration of drones is considered into the existing delivery system， and the vehicle routing problem with time windows is investigated under the collaborative delivery mode of electric vehicle-drone. The primary goal is to determine the optimal routes for both electric vehicles and drones to minimize total costs while satisfying the customer's time window requirements. During the delivery process in this mode， electric vehicles may require recharging at charging stations due to their limited battery capacity， when drones are loaded with batteries and goods， launched from， and recovered to the depot. To solve this problem， a mixed-integer programming-based mathematical optimization model is constructed. Then， an extended adaptive large neighborhood search algorithm （EALNS） is proposed， which integrates a construction heuristic algorithm to quickly obtain the initial feasible solution. In the algorithm， new insertion criterion and removal criterion of charging station are incorporated to satisfy the battery capacity constraint， and a shortest path removal operator is designed to accelerate the algorithmic convergence. Finally， simulation experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm. The experimental results show that：（1） Compared with the Gurobi solver and ALNS algorithm， the EALNS algorithm obtains a better solution with a shorter running time in solving the proposed model；（2） Compared to existing delivery systems， integrating drones into the delivery systems can yield cost savings ranging from 1.07% to 19.50%， with an average saving of 5.97%； and （3） The changes in model parameters affect the cost of the solution. Specifically， the costs of the solution decrease as the load capacity and flight duration of drones increase， or as the load capacity and battery capacity of electric vehicles increase. Furthermore， as the customer's time window constraints are tightened， the cost of the solution increases. In conclusion， a new variant of the electric vehicle routing problem with time windows is presented， verifying that integrating drones into electric vehicle delivery systems can significantly reduce logistics costs， and the proposed EALNS algorithm is effective in solving this problem.

Key words: time windows, electric vehicle-drone, collaborative delivery, routing problem, adaptive large neighborhood search algorithm

CLC Number:

U492.2

Shuai Zhang,Siliang Liu,Wenyu Zhang. Vehicle Routing Problems with Time Windows under the Collaborative Delivery Mode of Electric Vehicle-drone[J]. Chinese Journal of Management Science, 2025, 33(4): 131-141.

Figures/Tables 12

符号	定义
节点、集合
$V$	顾客节点集合
$0$	作为出发地的仓库节点
$N + 1$	作为返回地的仓库节点
$S$	充电站节点集合
$S'$	虚拟充电站节点集合
$S 0'$	虚拟充电站、出发地仓库节点集合， $S 0' = S' ⋃ {0}$
$V 0$	顾客、出发地仓库节点集合， $V 0 = V ⋃ {0}$
$V'$	顾客、虚拟充电站节点集合， $V' = V ⋃ S'$
$V 0'$	顾客、虚拟充电站、出发地仓库节点集合， $V 0' = V ⋃ S' ⋃ {0}$
$V N + 1'$	顾客、虚拟充电站、返回地仓库节点集合， $V N + 1' = V ⋃ S' ∪ {N + 1}$
$V 0, N + 1'$	顾客、虚拟充电站、出发地和返回地仓库节点集合， $V 0, N + 1' = V ⋃ S' ⋃ {0} ⋃ {N + 1}$
参数
$C 0$	每辆电动车的派遣成本
$C 1$	电动车每单位距离的行驶成本
$C 2$	无人机每单位距离的行驶成本
$Q 1$	电动车的载货容量
$Q 2$	无人机的载货容量
$B$	电动车的电池容量
$F$	无人机飞行续航时间
$q i$	节点i的商品需求量
$t u i$	电动车/无人机在节点i卸下商品的所需时间
$[s i, e i]$	节点i的时间窗，其中 $s i$ 为时间窗的开始时间， $e i$ 为时间窗的结束时间
$h g +$	电动车的电池充电系数
$h g -$	电动车的电池耗电系数
$t i j 1$	电动车从节点i到节点j的行驶时间
$t i j 2$	无人机从节点i到节点j的行驶时间
$d i j$	节点i到节点j的距离
$l i$	节点i的配送标记， $l i = 1$ 表示节点i可由电动车或无人机配送， $l i = 0$ 表示为节点i只能由电动车配送
决策变量
$x i j$	0-1变量，若电动车从节点i到节点j，则 $x i j = 1$
$y i$	0-1变量，若无人机从仓库到节点i后返回仓库，则 $y i = 1$
$r b i$	连续变量，电动车到达节点i时剩余电池容量
$r q i$	连续变量，电动车到达节点i时剩余载货容量
$t a i$	连续变量，电动车到达节点i的时间

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文献来源	问题特征				求解算法
文献来源	时间窗	协同	载机平台	参与配送	求解算法
Schneider等^[4]	√	×	×	电动车	改进型变邻域搜索混合算法
Murray和Chu^[9]	√	√	燃油车/仓库	燃油车&无人机	FSTSP/PDSTSP启发式算法
Zhang等^[12]	×	√	燃油车	燃油车&无人机	改进型二代非支配排序遗传算法
Nguyen等^[16]	√	√	仓库	燃油车&无人机	Ruin-Recreate算法
Kyriakakis等^[17]	×	√	电动车	无人机	蚁群-变邻域搜索混合算法
Mara等^[18]	×	√	电动车	电动车&无人机	改进型模因算法
本文	√	√	仓库	电动车&无人机	拓展型自适应大邻域搜索算法

小规模算例	Gurobi		构造启发式算法			拓展型ALNS算法
小规模算例	结果	求解时间(s)	平均结果	求解时间(s)	Gap₁（%）	平均结果	求解时间(s)	Gap₂（%）
c'101-10-5	2597.00	94.44	2621.60	0.04	0.94	2597.00	29.55	0.00
c'104-10-5	1821.79	1800.00	1902.80	0.01	4.26	1821.79	22.38	0.00
c'202-10-5	1391.60	1155.56	1813.77	0.01	23.28	1391.60	46.87	0.00
c'205-10-5	1203.87	1.94	1281.12	0.01	6.03	1203.87	21.67	0.00
r'102-10-5	2247.57	15.18	2808.22	0.01	19.96	2247.57	19.68	0.00
r'103-10-5	1588.29	1800.00	1588.29	0.01	0.00	1588.29	18.51	0.00
r'201-10-5	1223.90	76.28	1245.99	0.03	1.77	1223.90	33.53	0.00
r'203-10-5	1154.64	1800.00	1709.87	0.02	32.47	1154.64	57.59	0.00
rc'102-10-5	3236.68	9.45	3821.39	0.01	15.30	3236.68	19.48	0.00
rc'108-10-5	2537.78	1245.27	2847.85	0.02	10.89	2537.78	24.27	0.00
rc'201-10-5	1622.50	17.53	2198.19	0.02	26.19	1622.50	35.00	0.00
rc'205-10-5	1971.73	82.66	2401.61	0.02	17.90	1971.73	30.29	0.00
c'103-15-5	2653.28	1800.00	2640.57	0.05	-0.48	2623.14	88.37	-1.14
c'106-15-5	1819.53	16.84	2429.24	0.02	25.10	1819.53	56.63	0.00
c'202-15-5	2139.79	1800.00	2303.25	0.04	7.10	2139.79	97.38	0.00
c'208-15-5	1354.16	427.65	1472.82	0.04	8.06	1354.16	78.38	0.00
r'102-15-5	3217.10	1800.00	4440.68	0.04	27.55	3227.86	57.76	0.33
r'105-15-5	2489.84	1800.00	3100.90	0.03	19.71	2489.84	53.26	0.00
r'202-15-5	2074.01	1800.00	2557.28	0.10	18.90	2074.01	133.84	0.00
r'209-15-5	1439.71	1800.00	1972.48	0.13	27.01	1439.71	140.68	0.00
rc'103-15-5	3176.28	1800.00	3320.36	0.02	4.34	3176.28	46.57	0.00
rc'108-15-5	2635.07	1800.00	3543.97	0.05	25.65	2596.64	63.35	-1.46
rc'202-15-5	2183.16	1800.00	2614.13	0.09	16.49	2183.16	121.08	0.00
rc'204-15-5	1656.44	1800.00	1879.70	0.09	11.885	1624.06	258.38	-1.95
平均值	2059.82	1105.95	2438.17	0.04	15.52	2056.06	64.77	-0.18

大规模算例	拓展型ALNS算法				原始ALNS算法
大规模算例	最好结果	最差结果	平均结果	求解时间(s)	最好结果	最差结果	平均结果	求解时间(s)
c'101-50-21	4026.94	4175.90	4074.62	1733.34	4032.22	4633.50	4331.44	1810.12
c'104-50-21	3445.51	3968.39	3647.74	2337.22	3700.17	4163.13	3918.14	2212.08
c'202-50-21	2186.31	2332.52	2246.10	3471.77	2204.61	2774.92	2400.59	3510.34
c'205-50-21	2179.71	2312.07	2213.34	3373.58	2186.29	2481.25	2387.98	3632.94
r'102-50-21	7853.29	8775.69	8222.46	1437.06	7915.18	8568.89	8224.19	2008.88
r'103-50-21	6410.88	7151.83	6785.54	1643.21	6387.14	7664.78	6874.59	2130.63
r'201-50-21	3575.30	3695.09	3632.72	3268.21	3585.84	3878.04	3674.69	2512.29
r'203-50-21	2636.83	3023.25	2808.07	3784.78	2705.28	3165.94	2838.17	3530.82
rc'102-50-21	6822.06	8085.07	7608.92	1518.09	7343.42	8047.72	7691.07	1986.68
rc'108-50-21	4982.78	5876.91	5585.97	1819.73	4996.45	6548.56	5603.96	2288.80
rc'201-50-21	4151.51	4848.81	4422.21	3540.07	4232.24	4843.93	4493.76	2660.32
rc'205-50-21	3143.20	3727.59	3439.14	4056.24	3221.38	3921.27	3608.49	3676.23
c'103-75-21	5975.25	6595.09	6284.27	5638.79	6442.85	7264.70	6767.96	6659.92
c'208-75-21	2945.13	3561.68	3182.93	13658.89	3281.87	3985.13	3521.66	12331.50
r'102-75-21	11088.43	11801.42	11461.22	4163.39	10974.41	11731.95	11517.31	5831.83
r'209-75-21	3433.44	3721.12	3537.16	14956.28	3390.15	4062.27	3854.47	12420.52
rc'103-75-21	8382.04	9712.31	9070.71	4554.43	9045.72	10066.35	9444.52	6338.62
rc'204-75-21	3316.06	4159.33	3646.66	18842.94	3690.78	4147.93	3838.15	13677.38

算例	EVRPTW-D模型	基准模型	成本节省比（%）
c'101-50-21	4403.83	4074.62	7.48
c'104-50-21	4154.12	3647.74	12.19
c'202-50-21	2679.77	2246.10	16.18
c'205-50-21	2749.42	2213.34	19.50
r'102-50-21	8464.87	8222.46	2.86
r'103-50-21	7008.55	6785.54	3.18
r'201-50-21	4031.61	3632.72	9.89
r'203-50-21	2984.04	2808.07	5.90
rc'102-50-21	7821.50	7608.92	2.72
rc'108-50-21	5757.45	5585.97	2.98
rc'201-50-21	4470.19	4422.21	1.07
rc'205-50-21	3632.78	3439.14	5.33
c'103-75-21	6651.91	6284.27	5.53
c'208-75-21	3490.21	3182.93	8.80
r'102-75-21	11802.24	11461.22	2.89
r'209-75-21	4054.40	3537.16	12.76
rc'103-75-21	9572.20	9070.71	5.24
rc'204-75-21	3969.89	3646.66	8.14
平均值	5427.72	5103.88	5.97

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Vehicle Routing Problems with Time Windows under the Collaborative Delivery Mode of Electric Vehicle-drone

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