Skip to main content

Advertisement

SpringerLink
Log in

An integrated routing and scheduling model for evacuation and commodity distribution in large-scale disaster relief operations: a case study

  • S.I.: Applications of OR in Disaster Relief Operations
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Every year natural and man-made disasters cause considerable human and economic losses. It is essential to prepare for different relief operations to prevent and reduce these losses. In this paper, we propose an integrated evacuation and distribution logistic system to obtain simultaneous routing and scheduling of vehicles to evacuate people from affected areas to shelters and provide them with necessary relief commodities. We assume that shelters and vehicles have limited capacity and the demand of each affected area and distribution center could be fulfilled by more than one vehicle (split delivery). The proposed problem is formulated as a Mixed-Integer Linear Programming model with the objective of minimization of the sum of arrival times of the vehicles at affected areas, shelters, and distribution centers. We also propose a Memetic Algorithm (MA) to solve this integrated model on large-scale problems efficiently after tuning the MA parameters using the Taguchi method. The proposed model and algorithm are used to solve a case study in Tehran, the capital of Iran. The evaluation of the results shows the effectiveness of the proposed disaster relief logistic system in minimizing the total waiting time of evacuees and delivery time of supplies. The results also show that the number of relief vehicles and capacity of shelters can considerably affect the total relief time in disaster relief operations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. http://region4.tehran.ir/Default.aspx?tabid=514&CategoryID=31.

References

  • Abdelgawad, H., & Abdulhai, B. (2011). Large-scale evacuation using subway and bus transit: Approach and application in city of Toronto. Journal of Transportation Engineering,138, 1215–1232.

    Google Scholar 

  • Abdelgawad, H., Abdulhai, B., & Wahba, M. (2010). Multiobjective optimization for multimodal evacuation. Transportation Research Record: Journal of the Transportation Research Board,2196, 21–33.

    Google Scholar 

  • Ahmadi, M., Seifi, A., & Tootooni, B. (2015). A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district. Transportation Research Part E: Logistics and Transportation Review,75, 145–163.

    Google Scholar 

  • Alinaghian, M., & Naderipour, M. (2016). A novel comprehensive macroscopic model for time-dependent vehicle routing problem with multi-alternative graph to reduce fuel consumption: A case study. Computers & Industrial Engineering,99, 210–222.

    Google Scholar 

  • Altay, N., & Green, W. G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research,175, 475–493.

    Google Scholar 

  • Banomyong, R., Varadejsatitwong, P., & Oloruntoba, R. (2017). A systematic review of humanitarian operations, humanitarian logistics and humanitarian supply chain performance literature 2005 to 2016. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2549-5.

    Article  Google Scholar 

  • Bayram, V. (2016). Optimization models for large scale network evacuation planning and management: A literature review. Surveys in Operations Research and Management Science,21, 63–84.

    Google Scholar 

  • Berkoune, D., Renaud, J., Rekik, M., & Ruiz, A. (2012). Transportation in disaster response operations. Socio-Economic Planning Sciences,46, 23–32.

    Google Scholar 

  • Bish, D. R. (2011). Planning for a bus-based evacuation. OR Spectrum,33, 629–654.

    Google Scholar 

  • Bozorgi-Amiri, A., Jabalameli, M., & Al-E-Hashem, S. M. (2013). A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR Spectrum,35, 905–933.

    Google Scholar 

  • Bozorgi-Amiri, A., Jabalameli, M. S., Alinaghian, M., & Heydari, M. (2012). A modified particle swarm optimization for disaster relief logistics under uncertain environment. The International Journal of Advanced Manufacturing Technology,60, 357–371.

    Google Scholar 

  • Bretschneider, S. (2012). Mathematical models for evacuation planning in urban areas. Berlin: Springer.

    Google Scholar 

  • Burkart, C., Nolz, P. C., & Gutjahr, W. J. (2016). Modelling beneficiaries’ choice in disaster relief logistics. Annals of Operations Research,256(1), 41–61.

    Google Scholar 

  • Cattaruzza, D., Absi, N., Feillet, D., & Vidal, T. (2014). A memetic algorithm for the multi trip vehicle routing problem. European Journal of Operational Research,236, 833–848.

    Google Scholar 

  • Caunhye, A. M., Zhang, Y., Li, M., & Nie, X. (2016). A location-routing model for prepositioning and distributing emergency supplies. Transportation Research Part E: Logistics and Transportation Review,90, 161–176.

    Google Scholar 

  • Coutinho-Rodrigues, J., Tralhão, L., & Alçada-Almeida, L. (2012). Solving a location-routing problem with a multiobjective approach: The design of urban evacuation plans. Journal of Transport Geography,22, 206–218.

    Google Scholar 

  • Cross, R., & Crescent, R. (2011). The sphere handbook: Humanitarian charter and minimum standards in humanitarian response. The Sphere Project. http://www.sphereproject.org/handbook.

  • Dean, M. D., & Nair, S. K. (2014). Mass-casualty triage: Distribution of victims to multiple hospitals using the SAVE model. European Journal of Operational Research,238, 363–373.

    Google Scholar 

  • Dror, M., Laporte, G., & Trudeau, P. (1994). Vehicle routing with split deliveries. Discrete Applied Mathematics,50, 239–254.

    Google Scholar 

  • Dror, M., & Trudeau, P. (1989). Savings by split delivery routing. Transportation Science,23, 141–145.

    Google Scholar 

  • Dror, M., & Trudeau, P. (1990). Split delivery routing. Naval Research Logistics (NRL),37, 383–402.

    Google Scholar 

  • Gan, X., Wang, Y., Kuang, J., Yu, Y., & Niu, B. (2015). Emergency vehicle scheduling problem with time utility in disasters. Mathematical Problems in Engineering,2015, 1–7.

    Google Scholar 

  • Gan, X., Wang, Y., Yu, Y., & Niu, B. (2013). An emergency vehicle scheduling problem with time utility based on particle swarm optimization. In Intelligent computing theories and technology. Springer.

  • Georgiadou, P. S., Papazoglou, I. A., Kiranoudis, C. T., & Markatos, N. C. (2010). Multi-objective evolutionary emergency response optimization for major accidents. Journal of Hazardous Materials,178, 792–803.

    Google Scholar 

  • Goerigk, M., Deghdak, K., & Heßler, P. (2014). A comprehensive evacuation planning model and genetic solution algorithm. Transportation Research Part E: Logistics and Transportation Review,71, 82–97.

    Google Scholar 

  • Goerigk, M., & Grün, B. (2014). A robust bus evacuation model with delayed scenario information. OR Spectrum,36, 923–948.

    Google Scholar 

  • Hamedi, M., Haghani, A., & Yang, S. (2012). Reliable transportation of humanitarian supplies in disaster response: Model and heuristic. Procedia-Social and Behavioral Sciences,54, 1205–1219.

    Google Scholar 

  • Hu, Z.-H., Sheu, J.-B., Yin, Y.-Q., & Wei, C. (2017). Post-disaster relief operations considering psychological costs of waiting for evacuation and relief resources. Transportmetrica A: Transport Science,13, 108–138.

    Google Scholar 

  • Huang, X., & Song, L. (2016). An emergency logistics distribution routing model for unexpected events. Annals of Operations Research. https://doi.org/10.1007/s10479-016-2300-7.

    Article  Google Scholar 

  • Karaoglan, I., & Altiparmak, F. (2015). A memetic algorithm for the capacitated location-routing problem with mixed backhauls. Computers & Operations Research,55, 200–216.

    Google Scholar 

  • Kelle, P., Schneider, H., & Yi, H. (2014). Decision alternatives between expected cost minimization and worst case scenario in emergency supply—Second revision. International Journal of Production Economics,157, 250–260.

    Google Scholar 

  • Luis, E., Dolinskaya, I. S., & Smilowitz, K. R. (2012). Disaster relief routing: Integrating research and practice. Socio-economic Planning Sciences,46, 88–97.

    Google Scholar 

  • Manopiniwes, W., & Irohara, T. (2015). Integrated relief supply distribution and evacuation: A stochastic approach. In Toward sustainable operations of supply chain and logistics systems. Springer.

  • Mokhtarinejad, M., Ahmadi, A., Karimi, B., & Rahmati, S. H. A. (2015). A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment. Applied Soft Computing,34, 274–285.

    Google Scholar 

  • Moshref-Javadi, M., & Lee, S. (2016a). The customer-centric, multi-commodity vehicle routing problem with split delivery. Expert Systems with Applications,56, 335–348.

    Google Scholar 

  • Moshref-Javadi, M., & Lee, S. (2016b). The latency location-routing problem. European Journal of Operational Research,255, 604–619.

    Google Scholar 

  • Naderi, B., Zandieh, M., & Fatemi Ghomi, S. (2009). Scheduling job shop problems with sequence-dependent setup times. International Journal of Production Research,47, 5959–5976.

    Google Scholar 

  • Najafi, M., Eshghi, K., & De Leeuw, S. (2014). A dynamic dispatching and routing model to plan/re-plan logistics activities in response to an earthquake. OR Spectrum,36, 323–356.

    Google Scholar 

  • Najafi, M., Eshghi, K., & Dullaert, W. (2013). A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transportation Research Part E: Logistics and Transportation Review,49, 217–249.

    Google Scholar 

  • Nolz, P. C., Semet, F., & Doerner, K. F. (2011). Risk approaches for delivering disaster relief supplies. OR Spectrum,33, 543–569.

    Google Scholar 

  • Ozdamar, L. (2011). Planning helicopter logistics in disaster relief. OR Spectrum,33, 655–672.

    Google Scholar 

  • Özdamar, L., Ekinci, E., & Küçükyazici, B. (2004). Emergency logistics planning in natural disasters. Annals of Operations Research,129, 217–245.

    Google Scholar 

  • Pedraza-Martinez, A. J., & Van Wassenhove, L. N. (2012). Transportation and vehicle fleet management in humanitarian logistics: Challenges for future research. EURO Journal on Transportation and Logistics,1, 185–196.

    Google Scholar 

  • Pelling, M., Maskrey, A., Ruiz, P., Hall, P., Peduzzi, P., Dao, Q.-H., et al. (2004). Reducing disaster risk: A challenge for development. New York: United Nations Development Programme.

    Google Scholar 

  • Pourrahmani, E., Delavar, M. R., Pahlavani, P., & Mostafavi, M. A. (2015). Dynamic evacuation routing plan after an earthquake. Natural Hazards Review,16(4), 040150061-8.

    Google Scholar 

  • Rath, S., & Gutjahr, W. J. (2014). A math-heuristic for the warehouse location–routing problem in disaster relief. Computers & Operations Research,42, 25–39.

    Google Scholar 

  • Rennemo, S. J., Rø, K. F., Hvattum, L. M., & Tirado, G. (2014). A three-stage stochastic facility routing model for disaster response planning. Transportation Research Part E: Logistics and Transportation Review,62, 116–135.

    Google Scholar 

  • Ruiz, R., & Stützle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research,177, 2033–2049.

    Google Scholar 

  • Saadatseresht, M., Mansourian, A., & Taleai, M. (2009). Evacuation planning using multiobjective evolutionary optimization approach. European Journal of Operational Research,198, 305–314.

    Google Scholar 

  • Safaei, N., Saidi-Mehrabad, M., & Jabal-Ameli, M. (2008). A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system. European Journal of Operational Research,185, 563–592.

    Google Scholar 

  • Salehi, F., Mahootchi, M., & Husseini, S. M. M. (2017). Developing a robust stochastic model for designing a blood supply chain network in a crisis: A possible earthquake in Tehran. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2533-0.

    Article  Google Scholar 

  • Sheu, J.-B., & Pan, C. (2014). A method for designing centralized emergency supply network to respond to large-scale natural disasters. Transportation Research Part B: Methodological,67, 284–305.

    Google Scholar 

  • Taguchi, G. (1986). Introduction to quality engineering: Designing quality into products and processes. Tokyo: Asian Productivity Organization.

    Google Scholar 

  • Talarico, L., Meisel, F., & Sörensen, K. (2015). Ambulance routing for disaster response with patient groups. Computers & Operations Research,56, 120–133.

    Google Scholar 

  • Tzeng, G.-H., Cheng, H.-J., & Huang, T. D. (2007). Multi-objective optimal planning for designing relief delivery systems. Transportation Research Part E: Logistics and Transportation Review,43, 673–686.

    Google Scholar 

  • Vahdani, B., Tavakkoli-Moghaddam, R., Zandieh, M., & Razmi, J. (2012). Vehicle routing scheduling using an enhanced hybrid optimization approach. Journal of Intelligent Manufacturing,23, 759–774.

    Google Scholar 

  • Vásquez, Ó. C., Sepulveda, J. M., Alfaro, M. D., & Osorio-Valenzuela, L. (2013). Disaster response project scheduling problem: A resolution method based on a game-theoretical model. International Journal of Computers Communications & Control,8, 334–345.

    Google Scholar 

  • Wex, F., Schryen, G., Feuerriegel, S., & Neumann, D. (2014). Emergency response in natural disaster management: Allocation and scheduling of rescue units. European Journal of Operational Research,235, 697–708.

    Google Scholar 

  • Wohlgemuth, S., Oloruntoba, R., & Clausen, U. (2012). Dynamic vehicle routing with anticipation in disaster relief. Socio-economic Planning Sciences,46, 261–271.

    Google Scholar 

  • Yi, W., & Kumar, A. (2007). Ant colony optimization for disaster relief operations. Transportation Research Part E: Logistics and Transportation Review,43, 660–672.

    Google Scholar 

  • Yi, W., & Özdamar, L. (2007). A dynamic logistics coordination model for evacuation and support in disaster response activities. European Journal of Operational Research,179, 1177–1193.

    Google Scholar 

  • Yu, Y., Chu, C., Chen, H., & Chu, F. (2012). Large scale stochastic inventory routing problems with split delivery and service level constraints. Annals of Operations Research,197, 135–158.

    Google Scholar 

  • Yusoff, M., Ariffin, J., & Mohamed, A. (2008). Optimization approaches for macroscopic emergency evacuation planning: A survey. In International symposium on information technology, 2008. ITSim 2008 (pp. 1–7). IEEE.

Download references

Acknowledgements

The authors would like to thank the two anonymous referees for their constructive comments.

Author information

Authors and Affiliations

  1. School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

    Fatemeh Sabouhi & Mehdi Heydari

  2. School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Ali Bozorgi-Amiri

  3. School of Industrial Engineering, Purdue University, 315 N. Grant St., West Lafayette, IN, 47907, USA

    Mohammad Moshref-Javadi

Authors
  1. Fatemeh Sabouhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Bozorgi-Amiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Moshref-Javadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mehdi Heydari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Moshref-Javadi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sabouhi, F., Bozorgi-Amiri, A., Moshref-Javadi, M. et al. An integrated routing and scheduling model for evacuation and commodity distribution in large-scale disaster relief operations: a case study. Ann Oper Res 283, 643–677 (2019). https://doi.org/10.1007/s10479-018-2807-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-018-2807-1

Keywords

Search

Navigation