Emergency Management

Emergency Management

Location-Routing Problem in Humanitarian Relief Chain Considering the Reliability of Road Network

Document Type : Original Article

Authors
Associate Professor, School of Industrial Engineering, College of Engineering, University of Tehran
Abstract
Crisis is an inevitable fact of the human’s life. Fortunately, science and technology development has highly
contributed to the reduction of losses and casualties, but it has not reduced the happenings or damages to zero.
Crisis management is mentioned as one of the most important scientific-practical issues nowadays that every
country stray toward it. This paper targeted the response phase of crisis management that is considered as the most
important crisis management phase. The basic operations such as relief and rescue, evacuation of the injured and
victims, and relief commodities distribution are carried out in this phase. In this study, the locating of temporary
depots and routing of vehicles were taken into account by considering the reliability of the roads and allocating
the distribution centers. The model is multi-objective and aimed at achieving the following goals: 1) Minimizing
the maximum shortage of the disaster points. 2) Minimizing the maximum time of the vehicles by considering the
velocity and normal speed of vehicles. The proposed method augmented Epsilon Constraint generalized model
for Case study in Sichuan, China. The results showed the effectiveness and applicability of the proposed model
was reliable for product distribution centers and making decisions about allocation and assignment of temporary
accommodation centers in different parts of logistics network in conditions of crisis .
Keywords

1. Bakuli, D. L., & Smith, J. M. (1996). Resource allocation
in statedependent emergency evacuation networks.
European Journal of Operational Research, 89(3),
543-555.
2. Haghani, S. C. O. A. (1997). Testing and evaluation of
a multi commodity multi modal network flow model
for disaster relief management. Journal of Advanced
Transportation, 31(3), 249-282.
3. Barbarosoǧlu, G., & Arda, Y. (2004). A two-stage stochastic
programming framework for transportation
planning in disaster response. Journal of the operational
research society , 55(1), 43-53.
4. Akkihal, A. R. (2006). Inventory pre-positioning for
humanitarian operations (Doctoral dissertation,
Massachusetts Institute of Technology).
5. 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(6), 673-686.
6. Eshghi, K, & Najafi, M. (2013). A Logistics Planning
Model to Improve the Response Phase of Earthquake.
International Journal of Industrial Engineering &
Production Management , 23, 401-416.
7. Balcik, B., & Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics,
11(2), 101-121.
8. Salmeron J. and Apte A. (2009). Stochastic optimization
for natural disaster asset prepositioning. Production
and Operations Management Society , 19(5): 561–574
9. Bozorgi-Amiri, A., Jabalameli, M. S., & Al-e-Hashem,
S. M. (2013). A multi-objective robust stochastic
programming model for disaster relief logistics under
uncertainty. OR spectrum , 35(4), 905-933.
10. Canbolat, M. S., & Von Massow, M. (2011). Locating
emergency facilities with random demand for risk
minimization. Expert Systems with Applications , 38(8),
10099-10106.
11. 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.
12. Vahdani, B., Tavakkoli-Moghaddam, R., & Jolai, F.
(2013). Reliable design of a logistics network under
uncertainty: A fuzzy possibilistic-queuing model.
Applied Mathematical Modelling , 37(5), 3254-3268.
13. Arkat, J., Zamani, S., & Qods, P. (2014). Location-
Routing for emergenci facilities considering destruction
probobilities for communication paths in crises.
14. Wang, H., Du, L., & Ma, S. (2014). Multi-objective
open location-routing model with split delivery
for optimized relief distribution in post-earthquake.
Transportation Research Part E: Logistics and
Transportation Review , 69, 160-179.
15. Bozorgi-Amiri, A., & Khorsi, M. (2015). A dynamic
multi-objective location–routing model for relief logistic
planning under uncertainty on demand, travel
time, and cost parameters. The International Journal of
Advanced Manufacturing Technology , 1-16.
16. Ghezavati, V., Soltanzadeh, F., & Hafezalkotob, A.
(2015). Optimization of reliability for a hierarchical
facility location problem under disaster relief situations
by a chance-constrained programming and
robust optimization. Proceedings of the Institution of
Mechanical Engineers, Part O: Journal of Risk and Reliability,
229(6), 542-555.
17. Vahdani, B., Veysmoradi, D., Shekari, N., & Mousavi,
S. M. Multi-objective, multi-period location-routing
model to distribute relief after earthquake by considering
emergency roadway repair. Neural Computing
and Applications, 1-20.
18. Xu, J., Wang, Z., Zhang, M., & Tu, Y. (2016). A new
model for a 72-h post-earthquake emergency logistics
location-routing problem under a random fuzzy
environment. Transportation Letters, 1-16.
19. Tavakkoli-Moghaddam, R., Shishegar, S., Siadat, A.,
& Mohammadi, M. (2016). Design of a Reliable Biobjective
Relief Routing Network in the Earthquake
Response Phase. Procedia Computer Science, 102, 74-
81.
20. Glover, F., & Woolsey, E. (1974). Technical note—converting
the 0-1 polynomial programming problem to
a 0-1 linear program. Operations researc h, 22(1), 180-
182.

  • Receive Date 22 March 2017
  • Revise Date 03 September 2018
  • Accept Date 17 December 2019