Dynamically coordinated routing optimization for mountainous forest fire emergency supply delivery using multi-modal vehicles

doi:10.16265/j.cnki.issn1003-3033.2026.05.0356

Abstract

Abstract:

To address the challenges of multi-modal vehicle coordination and dynamic scheduling in emergency supply delivery for mountain forest fires, this study proposes an optimized route planning method for emergency supply distribution. Considering factors such as mountainous road network structures, demand urgency, motorcycle and drone participation, and air-ground collaborative delivery, a dynamic collaborative truck-drone-motorcycle route optimization model for mountain forest fire emergency supply delivery is constructed, aiming to minimize delivery time. An improved adaptive large neighborhood search (IALNS) algorithm with a repair operator is designed to solve the model, and it is compared with traditional ALNS and tabu search algorithms(TSA). Research findings indicate: The proposed model and algorithm are applicable to emergency supply delivery in mountainous forest fire scenarios. The IALNS outperforms both ALNS and TSA. Dynamic coordination strategies yield superior results compared to static coordination, reducing the total delivery time by 2.49% to 25.75%. Leveraging the advantages of motorcycles and drones is essential for establishing an optimized multi-vehicle collaborative delivery model.

Key words: forest fire, emergency deliveries, dynamically coordinated, vehicle routing problem(VRP), adaptive large neighborhood search(ALNS)

CLC Number:

X928.7火灾与爆炸事故

Zhou Yufeng, Cheng Jiahao, Pan Zimei, Liu Changshi. Dynamically coordinated routing optimization for mountainous forest fire emergency supply delivery using multi-modal vehicles[J]. China Safety Science Journal, 2026, 36(5): 270-278.

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算例	TT/min	TDT/min	DDT/min	MDT/min	TDCT/min	TDN	DDN	MDN	RT/s
C101	827.24	279.11	410.45	266.33	281.80	20	12	18	5.98
C201	1 034.76	383.94	505.79	285.21	365.61	25	11	14	5.56
R101	1 340.21	441.86	524.62	533.29	365.06	21	10	19	5.74
R201	1 379.44	698.29	484.40	392.68	288.47	26	9	15	5.50
RC101	2 019.04	470.34	263.04	1 312.15	236.55	18	6	26	5.45
RC201	2 053.39	486.05	865.77	752.06	815.27	19	14	17	5.19
平均值	1 442.35	459.93	509.01	590.29	392.13	21.50	10.33	18.17	5.57

算例	策略	TT/min	TDT/min	DDT/min	MDT/min	TDCT/min	TDN	DDN	MDN
C102	动态	872.61	285.14	286.83	416.14	171.32	20	10	20
C102	静态	894.91	331.40	329.34	234.17	329.34	22	12	16
R202	动态	1 264.06	579.34	436.49	437.88	246.84	24	9	17
R202	静态	1 320.56	590.03	369.88	360.64	369.88	25	10	15
RC102	动态	1 186.60	464.71	427.01	448.67	273.22	22	9	20
RC102	静态	1 598.05	394.33	566.56	637.17	566.56	19	9	22

算例	节点数量	算法	最优目标/min	最差目标/min	平均值/min	Gap/%	标准差	平均时间/s
C205	25	IALNS	485.75	598.18	543.30	—	34.95	4.81
		ALNS	514.21	619.64	558.62	2.82	34.12	4.79
		TSA	575.40	665.54	610.45	12.36	27.98	10.01
C206	50	IALNS	1 282.38	1 516.38	1 434.86	—	83.97	5.99
		ALNS	1 380.82	1 664.81	1 550.42	8.05	88.02	6.13
		TSA	1 631.08	2 192.95	1 875.30	30.70	219.42	12.32
RC206	100	IALNS	3 390.10	3 788.68	3 667.33	—	123.85	13.53
		ALNS	3 486.18	4 610.39	3 959.89	7.98	393.04	14.92
		TSA	3 717.92	4 704.93	4 272.89	16.51	357.77	28.72
G-C102	200	IALNS	5 088.95	6 362.82	5 703.07	—	381.23	19.81
		ALNS	5 722.08	6 786.82	6 217.66	9.02	386.73	13.77
		TSA	7 303.94	9 073.83	8 244.30	44.56	601.28	35.65

算例	w_f/ kg	TT/min	TDT/min	DDT/min	MDT/min	TDCT/min	TDN	DDN	MDN
RC202	4	1 990.90	539.30	221.77	1 266.00	185.60	20	6	24
	6	1 888.79	665.60	368.04	896.03	327.15	22	9	19
	8	1 835.75	676.30	371.68	838.56	320.89	22	10	18
	10	1 696.70	642.58	448.02	655.20	398.93	22	13	15
	12	1 680.03	758.03	408.67	580.46	341.54	24	13	13
	14	1 645.52	550.76	393.91	764.29	330.47	20	14	16

算例	摩托车数量	TT/min	TDT/min	DDT/min	MDT/min	TDCT/min	TDN	DDN	MDN
RC202	2	1 990.02	775.49	408.60	844.38	370.15	22	14	14
	3	1 729.92	715.48	315.31	758.05	256.39	22	13	15
	4	1 696.70	642.58	448.02	655.2	398.93	22	13	15
	5	1 593.93	546.11	459.16	655.82	392.00	20	13	17
	6	1 458.52	602.19	271.19	648.89	207.44	22	10	18