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Uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation model for earthquake evacuation planning

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Listed:
  • Ghasemi, Peiman
  • Khalili-Damghani, Kaveh
  • Hafezalkotob, Ashkan
  • Raissi, Sadigh

Abstract

In this paper, an uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation mixed-integer mathematical programing model is proposed for the response phase of the earthquake. The proposed model includes five echelons as affected areas, distribution centers, hospitals, temporary accommodation centers and temporary care centers. Two objective functions as minimizing the total cost of the location-allocation of facilities and minimizing the amount of the shortage of relief supplies, are considered. The uncertainty is modeled using a scenario-based probability approach. The main decisions made by the proposed model are locating of the temporary care and accommodation centers, allocation of the affected areas to the located centers and hospitals, as well as the allocation of the distribution centers to temporary accommodation centers. Several decisions associated with flow of injured people and commodities between facilities, and decisions associated with number of vehicles between facilities and shortage and inventory level at centers are also made by the proposed model. Several sets of constraints including demand and flow of relief commodities, capacity of centers, transportation of injured people, capacity of transportation vehicles for commodities and injured people, and back up centers are considered in multiple periods of planning in the proposed model. The proposed model is applied in a real case study in Tehran. The model is solved using modified multiple-objective particle swarm optimization (MMOPSO), Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and epsilon constraint method. The performance of the solution procedures is analyzed using multi-objective performance evaluation metrics. The results reveal the superiority of the MMOPSO over the other solution approaches. A preferred solution, from the set of non-dominated solutions generated by MMOPSO, has been selected, analyzed and described. Sensitivity analysis on main parameters of proposed model and the probabilities of the earthquake and failures of the facilities has also been accomplished.

Suggested Citation

  • Ghasemi, Peiman & Khalili-Damghani, Kaveh & Hafezalkotob, Ashkan & Raissi, Sadigh, 2019. "Uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation model for earthquake evacuation planning," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 105-132.
    • Handle: RePEc:eee:apmaco:v:350:y:2019:i:c:p:105-132
    DOI: 10.1016/j.amc.2018.12.061
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    References listed on IDEAS

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    1. Zhou, Yawen & Liu, Jing & Zhang, Yutong & Gan, Xiaohui, 2017. "A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 99(C), pages 77-95.
    2. Behzad Zahiri & Mir Saman Pishvaee, 2017. "Blood supply chain network design considering blood group compatibility under uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 2013-2033, April.
    3. Xie, Siyang & Li, Xiaopeng & Ouyang, Yanfeng, 2015. "Decomposition of general facility disruption correlations via augmentation of virtual supporting stations," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 64-81.
    4. Pérez-Galarce, Francisco & Canales, Linda J. & Vergara, Claudio & Candia-Véjar, Alfredo, 2017. "An optimization model for the location of disaster refuges," Socio-Economic Planning Sciences, Elsevier, vol. 59(C), pages 56-66.
    5. Christian Burkart & Pamela C. Nolz & Walter J. Gutjahr, 2017. "Modelling beneficiaries’ choice in disaster relief logistics," Annals of Operations Research, Springer, vol. 256(1), pages 41-61, September.
    6. Kınay, Ömer Burak & Yetis Kara, Bahar & Saldanha-da-Gama, Francisco & Correia, Isabel, 2018. "Modeling the shelter site location problem using chance constraints: A case study for Istanbul," European Journal of Operational Research, Elsevier, vol. 270(1), pages 132-145.
    7. Christophe Duhamel & Andréa Cynthia Santos & Daniel Brasil & Eric Châtelet & Babiga Birregah, 2016. "Connecting a population dynamic model with a multi-period location-allocation problem for post-disaster relief operations," Annals of Operations Research, Springer, vol. 247(2), pages 693-713, December.
    8. Mahmoud Golabi & Seyed Mahdi Shavarani & Gokhan Izbirak, 2017. "An edge-based stochastic facility location problem in UAV-supported humanitarian relief logistics: a case study of Tehran earthquake," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(3), pages 1545-1565, July.
    9. Chen, Albert Y. & Yu, Ting-Yi, 2016. "Network based temporary facility location for the Emergency Medical Services considering the disaster induced demand and the transportation infrastructure in disaster response," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 408-423.
    10. Michael B. Teitz & Polly Bart, 1968. "Heuristic Methods for Estimating the Generalized Vertex Median of a Weighted Graph," Operations Research, INFORMS, vol. 16(5), pages 955-961, October.
    11. Paul, Jomon Aliyas & MacDonald, Leo, 2016. "Location and capacity allocations decisions to mitigate the impacts of unexpected disasters," European Journal of Operational Research, Elsevier, vol. 251(1), pages 252-263.
    12. An, Shi & Cui, Na & Bai, Yun & Xie, Weijun & Chen, Mingliu & Ouyang, Yanfeng, 2015. "Reliable emergency service facility location under facility disruption, en-route congestion and in-facility queuing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 82(C), pages 199-216.
    13. Jomon Paul & Govind Hariharan, 2012. "Location-allocation planning of stockpiles for effective disaster mitigation," Annals of Operations Research, Springer, vol. 196(1), pages 469-490, July.
    14. Xiang Chu & QiuYan Zhong, 2015. "Post-earthquake allocation approach of medical rescue teams," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(3), pages 1809-1824, December.
    15. Gutjahr, Walter J. & Dzubur, Nada, 2016. "Bi-objective bilevel optimization of distribution center locations considering user equilibria," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 85(C), pages 1-22.
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