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Dynamic Resource Allocation Problem for Transportation Network Evacuation

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  • Xiaozheng He
  • Srinivas Peeta

Abstract

Allocating movable response resources dynamically enables evacuation management agencies to improve evacuation system performance in both the spatial and temporal dimensions. This study proposes a mixed integer linear program (MILP) model to address the dynamic resource allocation problem for transportation evacuation planning and operations. To enable realism in practice, the proposed model includes spatiotemporal constraints related to the time required to reallocate resources to another location, the minimum time allocated resources should be at a location, and the minimum time gap between successive allocations of resources to a location. The proposed model is transformed into a two-stage optimization program for which a greedy-type heuristic algorithm is developed to solve the MILP approximately but efficiently. Results from computational experiments demonstrate the effectiveness of the proposed model and the efficiency of the heuristic solution algorithm. Copyright Springer Science+Business Media New York 2014

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  • Xiaozheng He & Srinivas Peeta, 2014. "Dynamic Resource Allocation Problem for Transportation Network Evacuation," Networks and Spatial Economics, Springer, vol. 14(3), pages 505-530, December.
    • Handle: RePEc:kap:netspa:v:14:y:2014:i:3:p:505-530
    DOI: 10.1007/s11067-014-9247-5
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    Cited by:

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    2. Alizadeh, Morteza & Amiri-Aref, Mehdi & Mustafee, Navonil & Matilal, Sumohon, 2019. "A robust stochastic Casualty Collection Points location problem," European Journal of Operational Research, Elsevier, vol. 279(3), pages 965-983.
    3. David S. Dixon & Pallab Mozumder & William F. Vásquez & Hugh Gladwin, 2017. "Heterogeneity Within and Across Households in Hurricane Evacuation Response," Networks and Spatial Economics, Springer, vol. 17(2), pages 645-680, June.
    4. Yan, Yongze & Hong, Liu & He, Xiaozheng & Ouyang, Min & Peeta, Srinivas & Chen, Xueguang, 2017. "Pre-disaster investment decisions for strengthening the Chinese railway system under earthquakes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 39-59.
    5. Xiaozheng He & Hong Zheng & Srinivas Peeta & Yongfu Li, 2018. "Network Design Model to Integrate Shelter Assignment with Contraflow Operations in Emergency Evacuation Planning," Networks and Spatial Economics, Springer, vol. 18(4), pages 1027-1050, December.
    6. Jin, Jian Gang & Shen, Yifan & Hu, Hao & Fan, Yiqun & Yu, Mingjian, 2021. "Optimizing underground shelter location and mass pedestrian evacuation in urban community areas: A case study of Shanghai," Transportation Research Part A: Policy and Practice, Elsevier, vol. 149(C), pages 124-138.
    7. Satish V. Ukkusuri & Samiul Hasan & Binh Luong & Kien Doan & Xianyuan Zhan & Pamela Murray-Tuite & Weihao Yin, 2017. "A-RESCUE: An Agent based Regional Evacuation Simulator Coupled with User Enriched Behavior," Networks and Spatial Economics, Springer, vol. 17(1), pages 197-223, March.

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