A Post-disaster Assessment Routing Multi-objective Problem under Uncertain Parameters

Document Type : Original Article

Authors

1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Arts et Métiers Paris Tech, Paris, France

3 School of Industrial Engineering, College of Engineering, K. N. Toosi University of Technology (KNTU), Tehran, Iran

Abstract

Given that disasters are unavoidable, and many people are suffering from them each year, we should manage the emergencies and plan for them well to reduce mortality and financial losses. One of the measures that organizations must take after the disaster is the assessment of the conditions and needs of the people. We consider some characteristics for sites and roads and two teams for assessment as well as the uncertain assessment time to modeling. A multi-objective model is proposed in this study. The first objective function maximizes the gain from the assessment of areas and roads. The second and third objective functions maximize total coverage at damaged areas and roads. We use the LP-metric technique to solve small size problems in the GAMS software and the Grasshopper Optimization Algorithm (GOA) as a Meta-heuristic algorithm to solve a case study.  Numerical results are presented to prove the credibility and efficiency of our model.

Keywords

References

  1. Özdamar, L. and M.A. Ertem, Models, "solutions and enabling technologies in humanitarian logistics", European Journal of Operational Research, Vol. 244, No. 1, (2015), 55-65. DOI: 10.1016/j.ejor.2014.11.030.
  2. Altay, N. and W.G. Green III, "OR/MS research in disaster operations management", European Journal of Operational Research, Vol. 175, No. 1, (2006), 475-493. DOI: 10.1016/j.ejor.2005.05.016.
  3. Parmar, P., M. Arii, and S. Kayden, "Learning from Japan: Strengthening US emergency care and disaster response", Health Affairs, Vol. 32, No. 12, (2013), 2172-2178. DOI: 10.1377/hlthaff.2013.0704.
  4. Balcik, B., "Site selection and vehicle routing for post-disaster rapid needs assessment", Transportation Research Part E: Logistics and Transportation Review, Vol. 101, (2017), 30-58. DOI: 10.1016/j.tre.2017.01.002
  5. Çelik, M., "Network restoration and recovery in humanitarian operations: Framework, literature review, and research directions", Surveys in Operations Research and Management Science, Vol. 21, No. 2, (2016), 47-61. DOI: 10.1016/j.sorms.2016.12.001.
  6. Beiki, H., Seyedhosseini, S.M., Ghezavati, V.R. and Seyedaliakbar, S.M., “A Location-Routing Model for Assessment of the Injured People and Relief Distribution under Uncertainty”, International Journal of Engineering, Transactions A: Basics, Vol. 33, No. 7, (2020), 1274-1284. DOI: 10.5829/ije.2020.33.07a.14.
  7. Oruc, B.E. and B.Y. Kara, "Post-disaster assessment routing problem". Transportation Research Part B: Methodological, Vol. 116, (2018),76-102. DOI: 10.1016/j.trb.2018.08.002.
  8. Huang, M., K.R. Smilowitz, and B. Balcik, "A continuous approximation approach for assessment routing in disaster relief". Transportation Research Part B: Methodological, Vol. 50, (2013), 20-41. DOI: 10.1016/j.trb.2013.01.005.
  9. Kaviyani-Charati, M., F. Heidarzadeh Souraki, and M. Hajiaghaei-Keshteli, "A Robust Optimization Methodology for Multi-objective Location-transportation Problem in Disaster Response Phase under Uncertainty", International Journal of Engineering, Transactions B: Applications, Vol. 31, No. 11, (2018). 1953-1961. doi: 10.5829/ije.2018.31.11b.20
  10. Talarico, L., F. Meisel, and K. Sörensen, "Ambulance routing for disaster response with patient groups", Computers & Operations Research, Vol. 56, (2015), 120-133. DOI: 10.1016/j.cor.2014.11.006.
  11. Beiki, H., Seyedhosseini, S.M., Ghezavati, V.R. and Seyedaliakbar, S.M., “Multi-objective Optimization of Multi-vehicle Relief Logistics Considering Satisfaction Levels under Uncertainty”, International Journal of Engineering, Transactions B: Applications, Vol. 33, No. 5, (2020). 814-824. DOI: 10.5829/ije.2020.33.05b.13.
  12. Akbari, V. and F.S. Salman, “Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity”, European Journal of Operational Research, Vol. 257, No. 2, (2017), 625-640. DOI: 10.1016/j.ejor.2016.07.043.
  13. Nikoo, N., M. Babaei, and A.S. Mohaymany, “Emergency transportation network design problem: Identification and evaluation of disaster response routes”, International Journal of Disaster Risk Reduction, Vol. 27, (2018), 7-20. DOI: 10.1016/j.ijdrr.2017.07.003.
  14. Ostermeier, M. and A. Hübner, “Vehicle selection for a multi-compartment vehicle routing problem”. European Journal of Operational Research, Vol. 269, No. 2, (2018), 682-694.DOI: 10.1016/j.ejor.2018.01.059.
  15. Nair, D.J., Grzybowska, H., Fu, Y. and Dixit, V.V., "Scheduling and routing models for food rescue and delivery operations", Socio-Economic Planning Sciences, Vol. 63, (2018), 18-32. DOI: 10.1016/j.seps.2017.06.003.
  16. Alinaghian, M., M. Aghaie, and M.S. Sabbagh,” A mathematical model for location of temporary relief centers and dynamic routing of aerial rescue vehicles”, Computers & Industrial Engineering, Vol. 131, (2019), 227-241. DOI: 10.1016/j.cie.2019.03.002.
  17. Javadian, N., S. Modares, and A. Bozorgi-Amiri, “A bi-objective stochastic optimization model for humanitarian relief chain by using evolutionary algorithms”, International Journal of Engineering, Transactions A: Basics,  Vol. 30, No. 10, (2017). 1526-1537. doi: 10.5829/ije.2017.30.10a.14
  18. Abazari, S.R., A. Aghsami, and M. Rabbani, “Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters”, Socio-Economic Planning Sciences, (2020). DOI: 10.1016/j.seps.2020.100933. In Press.
  19. Nikkhoo, F. and A. Bozorgi-Amiri, "A Procurement-distribution Coordination Model in Humanitarian Supply Chain Using the Information-sharing Mechanism", International Journal of Engineering, Transactions A: Basics,, Vol. 31, No. 7, (2018), 1057-1065. doi: 10.5829/ije.2018.31.07a.08
  20. Isaloo, F. and M.M. Paydar,” Optimizing a robust bi-objective supply chain network considering environmental aspects: a case study in plastic injection industry”, International Journal of Management Science and Engineering Management, Vol. 15, No.1, (2020),26-38. DOI: 10.1080/17509653.2019.1592720.
  21. Saremi, S., S. Mirjalili, and A. Lewis, “Grasshopper optimisation algorithm: theory and application”, Advances in Engineering Software, Vol. 105, (2017), 30-47. DOI: 10.1016/j.advengsoft.2017.01.004.
  22. Perry, C. and I. Greig, “Estimating the mean and variance of subjective distributions in PERT and decision analysis”, Management Science, Vol. 21, No. 12, (1975), 1477-1480. DOI: 10.1287/mnsc.21.12.1477.
International Journal of Engineering
Volume 33, Issue 12
TRANSACTIONS C: Aspects
December 2020
Pages 2503-2508

Files

Share

How to cite

Statistics