地铁网络火灾协同救援调度模型

doi:10.3976/j.issn.1002-4026.2025004

Abstract

Abstract:

The integrated dispatch of metro firefighting engines (MFEs) and trains during fire emergencies presents unique challenges due to confined tunnel spaces that restrict conventional fire truck access and the often uncertain fire location. This necessitates cross-line deployment of MFEs within the metro network. We decompose the problem into two subproblems: MFE rescue operations and evacuation of trapped trains. For each, a mixed-integer programming model is formulated. To enhance computational efficiency, we propose a line-based decomposition algorithm combined with a constraint filtering technique. The models are validated using the Suzhou metro network as a case study. Results demonstrate that the proposed approach efficiently generates solutions within practical timeframes, enabling effective cross-line MFE dispatch and rapid train evacuation. This research offers both theoretical insights and a practical decision-support tool for emergency management in metro fire scenarios, contributing significant value to metro operation safety and resilience.

Key words: metro firefighting engine, metro fire rescue, train operation adjustment, mean-integer programming, cross-line dispatch

CLC Number:

U291

JI Shengyi, ZHANG Yong. Collaborative rescue dispatch model for metro network fire emergencies[J].Shandong Science, 2026, 39(1): 64-76.

Figures/Tables 11

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Table 1

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决策变量	含义
${a}_{i,k,\eta }^{up/dn}$	线路η中上/下行列车i到达车站k的时刻
${d}_{i,k,\eta }^{up/dn}$	线路η中上/下行列车i离开车站k的时刻
a_F_,_k_,_η	线路η中地铁消防车到达车站k的时刻
d_F_,_k_,_η	线路η中地铁消防车离开车站k的时刻
${q}_{i,j,k}^{\eta }$	线路η中同向列车次序变量,若列车i先于j到达车站k,${q}_{i,j,k}^{\eta }$ =1
${p}_{i,j,k}^{\eta }$	线路η中对向列车次序变量,若列车i先于j到达车站k,${p}_{i,j,k}^{\eta }$ =1

场景	火场位置	火灾时间	求解算法	FLTime/s	CPU Time/s	LB/s	OB1/s	gap	OB2/s
场景1	[1-17, 1-18]	8:00	CPLEX	—	5 000	2 125	—	—	831
			De	232	638	2 125	2 239	5.0%	831
			De+Ft	80	305	2 125	2 239	5.0%	831
场景2	[1-7, 1-8]	18:00	CPLEX	—	3 000	1 435	—	—	842
			De	415	1 060	1 435	1 482	3.2%	842
			De+Ft	79	266	1 435	1 482	3.2%	842
场景3	[2-26, 2-27]	9:00	De	423	1 158	2 062	2 107	2.2%	875
场景3	[2-26, 2-27]	9:00	De+Ft	89	281	2 062	2 107	2.2%	875
场景4	[4-18, 4-19]	16:00	De	215	415	1 606	1 679	4.3%	1 369
场景4	[4-18, 4-19]	16:00	De+Ft	92	296	1 606	1 679	4.3%	1 369
场景5	[3-30, 3-31]	17:00	De	433	762	1 273	1 325	3.9%	862
场景5	[3-30, 3-31]	17:00	De+Ft	99	420	1 273	1 325	3.9%	862

Collaborative rescue dispatch model for metro network fire emergencies

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