Responsibility Area Redivision and Path Optimization for Emergency Management in Dynamic Disaster Environments

Abstract

Emergency management requires efficient evacuation planning and the delivery of rescue supplies within dynamic road networks disrupted by ongoing disasters. Two critical challenges arise: (1) determining appropriate origin-destination (OD) assignments; and (2) identifying optimal paths among multiple OD pairs in real time. However, traditional static path optimization (SPO) and dynamic path optimization (DPO) often fall short in adapting to rapidly evolving conditions, risking failure in emergency response. To address these limitations, we proposed a novel method by modifying the co-evolutionary path optimization (CEPO) based on the ripple spreading algorithm (RSA), which can simultaneously determine optimal OD pairs and corresponding paths in a single run, even under dynamic disaster environment. The effectiveness and advantages of the method are verified by comprehensive experiments.

Keywords

Co-evolutionary path optimization (CEPO) / Dynamic disaster environment / Emergency management / Ripple spreading algorithm (RSA)

References

[1] Bauer R, Wagner DVahrenhold J. Batch dynamic single-source shortest-path algorithms: An experimental study. SEA ‘09: Proceedings of the 8th International Symposium on Experimental Algorithms, 2009, Berlin. Springer51-62

[2] Bullock J, Haddow G, Coppola D. Introduction to emergency management, 2017, Oxford. Butterworth-Heinemann

[3] Chang MS, Tseng YL, Chen JW. A scenario planning approach for the flood emergency logistics preparation problem under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 2007, 43(6): 737-754.

[4] Chapman AC, Micillo RA, Kota R, Jennings NR. Decentralized dynamic task allocation using overlapping potential games. The Computer Journal, 2010, 53(9): 1462-1477.

[5] Crişan GC, Pintea CM, Palade V. Emergency management using geographic information systems: Application to the first Romanian traveling salesman problem instance. Knowledge and Information Systems, 2017, 50: 265-285.

[6] Feng JR, Gai WM, Li JY. Multi-objective optimization of rescue station selection for emergency logistics management. Safety Science, 2019, 120: 276-282.

[7] Hodges, J.L., B.Y. Lattimer, and K.D. Luxbacher. 2019. Compartment fire predictions using transpose convolutional neural networks. Fire Safety Journal 108: Article 102854.

[8] Hu XB, Wang M, Leeson MS, Di Paolo EA, Liu H. Deterministic agent-based path optimization by mimicking the spreading of ripples. Evolutionary Computation, 2016, 24(2): 319-346.

[9] Hu XB, Zhang MK, Zhang Q, Liao JQ. Co-evolutionary path optimization by ripple-spreading algorithm. Transportation Research Part B: Methodological, 2017, 106: 411-432.

[10] Huang K, Jiang Y, Yuan Y, Zhao L. Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transportation Research Part E: Logistics and Transportation Review, 2015, 75: 1-17.

[11] Jin, J., Y. Zhang, Z. Zhou, M. Jin, X. Yang, and F. Hu. 2023. Conflict-based search with D* lite algorithm for robot path planning in unknown dynamic environments. Computers and Electrical Engineering 105: Article 108473.

[12] Kemball-Cook D, Stephenson R. Lessons in logistics from Somalia. Disasters, 1984, 8(1): 57-66.

[13] Kim GY, Lee S. Prediction of extreme wind by stochastic typhoon model considering climate change. Journal of Wind Engineering and Industrial Aerodynamics, 2019, 192: 17-30.

[14] Lagos T, Auad R, Lagos F. The online shortest path problem: Learning travel times using a multiarmed bandit framework. Transportation Science, 2025, 59(1): 28-59.

[15] Li SH, Hong HP. Typhoon wind hazard estimation for China using an empirical track model. Natural Hazards, 2016, 82: 1009-1029.

[16] Li X, Xiao Y, Li J, Wang H, Chuo E, Bai H. Special emergency resources preallocation concerning demand time for tunnel collapse. International Journal of Disaster Risk Science, 2023, 14(1): 113-126.

[17] Lin L, Chen K, Xia D, Wang H, Hu H, He F. Analysis on the wind characteristics under typhoon climate at the southeast coast of China. Journal of Wind Engineering and Industrial Aerodynamics, 2018, 182: 37-48.

[18] Musliner, D.J., E.H. Durfee, J. Wu, D.A. Dolgov, R.P. Goldman, and M.S. Boddy. 2006. Coordinated plan management using multiagent MDPs. In Proceedings of AAAI 2006 spring symposium: Distributed plan and schedule management, 27–29 March 2006, Stanford, California, USA, 73–80.

[19] Nikoo N, Babaei M, Mohaymany AS. Emergency transportation network design problem: Identification and evaluation of disaster response routes. International Journal of Disaster Risk Reduction, 2018, 27: 7-20.

[20] Ning Y, Wang X, Yu Q, Liang D, Zhai J. Rapid damage prediction and risk assessment for tropical cyclones at a fine grid in Guangdong Province, South China. International Journal of Disaster Risk Science, 2023, 14(2): 237-252.

[21] Özdamar L, Ekinci E, Küçükyazici B. Emergency logistics planning in natural disasters. Annals of Operations Research, 2004, 129: 217-245.

[22] Smith, S.F., A. Gallagher, and T. Zimmerman. 2007. Distributed management of flexible times schedules. In Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, 14–18 May 2007, New York, 1–8.

[23] Tang G, Tang C, Claramunt C, Hu X, Zhou P. Geometric A-star algorithm: An improved A-star algorithm for AGV path planning in a port environment. IEEE Access, 2021, 9: 59196-59210.

[24] Tang, L., and Y. Li. 2022. An improved PSO algorithm for optimized material scheduling in emergency relief. Mathematical Problems in Engineering 2022(1): Article 5343521.

[25] Wang Y. Multiperiod optimal allocation of emergency resources in support of cross-regional disaster sustainable rescue. International Journal of Disaster Risk Science, 2021, 12(3): 394-409.

[26] Wang Z, Fan XJan SU. A systematic review and analysis of A-star pathfinding algorithms and its variations. Proceedings of the Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024), 2024, Bellingham. SPIE157-182

[27] Wang Z, Jia G. A novel agent-based model for tsunami evacuation simulation and risk assessment. Natural Hazards, 2021, 105(2): 2045-2071.

[28] Wang Z, Zlatanova S. Multi-agent based path planning for first responders among moving obstacles. Computers, Environment and Urban System, 2016, 56: 48-58.

[29] Wang S, Zhen L, Zhuge D. Dynamic programming algorithms for selection of waste disposal ports in cruise shipping. Transportation Research Part B: Methodological, 2018, 108: 235-248.

[30] Wang S, Zhang W, Bie Y, Wang K, Diabat A. Mixed-integer second-order cone programming model for bus route clustering problem. Transportation Research Part C: Emerging Technologies, 2019, 102: 351-369.

[31] Wang, X., J. Liu, C. Nugent, I. Cleland, and Y. Xu. 2023. Mobile agent path planning under uncertain environment using reinforcement learning and probabilistic model checking. Knowledge-Based Systems 264: Article 110355.

[32] Xiao YF, Duan ZD, Xiao YQ, Ou JP, Chang L, Li QS. Typhoon wind hazard analysis for southeast China coastal regions. Structural Safety, 2011, 33(4–5): 286-295.

[33] Xu K, Han Z, Xu H, Bin L. Rapid prediction model for urban floods based on a light gradient boosting machine approach and hydrological-hydraulic model. International Journal of Disaster Risk Science, 2023, 14(1): 79-97

[34] Yazdani, M., and M. Haghani. 2023. Hospital evacuation in large-scale disasters using limited aerial transport resources. Safety Science 164: Article 106171.

[35] Yazdani, M., K. Kabirifar, and M. Haghani. 2024. Optimising post-disaster waste collection by a deep learning-enhanced differential evolution approach. Engineering Applications of Artificial Intelligence 132: Article 107932.

[36] Yin J, Yin Z, Xu S. Composite risk assessment of typhoon-induced disaster for China’s coastal area. Natural Hazards, 2013, 69: 1423-1434.

[37] Zhang, M.K., X.B. Hu, and J.A. Wang. 2019. Research on evacuation route from high-rise building under real effect of fire spread. China Safety Science Journal 29(3): Article 32 (in Chinese).

[38] Zhou Y, Liu J, Zhang Y, Gan X. A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems. Transportation Research Part E: Logistics and Transportation Review, 2017, 99: 77-95.

[39] Zhou Y, Zhang Y, Zhang Y. HMLPA*: A hierarchical multi-target LPA* pathfinding algorithm designed for dynamic indoor path network. International Journal of Geographical Information Science, 2025, 39(7): 1484-1517.

RIGHTS & PERMISSIONS

The Author(s)