Planning for Medical Emergency Transportation Vehicles during Natural Disasters

Subject Areas : Architecture

Hesam  Adrang ¹ (Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Ali  Bozorgi-Amiri ² (School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran)
Kaveh  Khalili-Damghani ³ (Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Reza  Tavakkoli-Moghaddam ⁴ (School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran)

Received: 2017-01-20 Accepted : 2020-04-19 Published : 2020-12-01

Keywords: disaster, Medical emergency services, Location-routing, Mixed-Integer Linear Programming, ε-constraint,

Abstract :

One of the main critical steps that should be taken during natural disasters is the assignment and distribution of resources among affected people. In such situations, this can save many lives. Determining the demands for critical items (i.e., the number of injured people) is very important. Accordingly, a number of casualties and injured people have to be known during a disaster. Obtaining an acceptable estimation of the number of casualties adds to the complexity of the problem. In this paper, a location-routing problem is discussed for urgent therapeutic services during disasters. The problem is formulated as a bi-objective Mixed-Integer Linear Programming (MILP) model. The objectives are to concurrently minimize the time of offering relief items to the affected people and minimize the total costs. The costs include those related to locations and transportation means (e.g., ambulances and helicopters) that are used to carry medical personnel and patients. To address the bi-objectiveness and verify the efficiency and applicability of the proposed model, the ε-constraint method is employed to solve several randomly-generated problems with CLEPX solver in GAMS. The obtained results include the objective functions, the number of the required facility, and the trade-offs between objectives. Then, the parameter of demands (i.e., number of casualties), which has the most important role, is examined using a sensitivity analysis and the managerial insights are discussed.

References:

Abounacer R., Rekik M., Renaud J., (2014), An exact solution approach for multi-objective location–transportation problem for disaster response. Computers & Operations Research, 41, 83-93.

Balcik B., Beamon B.M., (2008), Facility location in humanitarian relief. International Journal of Logistics, 11, 101-121.

Balcik B., Beamon B.M., Krejci C.C., Muramatsu K. M., Ramirez M., (2010), Coordination in humanitarian relief chains: Practices, challenges and opportunities. International Journal of Production Economics, 126, 22-34.

Barbarosoğlu G., Özdamar L., and Cevik A., (2002), An interactive approach for hierarchical analysis of helicopter logistics in disaster relief operations. European Journal of Operational Research, 140, 118-133.

Barbarosoğlu G., Özdamar L., and Cevik A., (2002), An interactive approach for hierarchical analysis of helicopter logistics in disaster relief operations. European Journal of Operational Research, 140, 118-133.

Bozorgi-Amiri, A., & Jabalameli, M. S. Mirzapour Al-e Hashem SMJ (2013), A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR Spectrum, 35(4), 905-933.

Chen, A. Y., & Yu, T. Y., (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, 91, 408-423.‏

Doerner K., Focke A., Gutjahr W.J., (2007), Multicriteria tour planning for mobile healthcare facilities in a developing country. European Journal of Operational Research, 179, 1078-1096.‏

Douglas L., (1997), Logistics for disaster relief, IIE soultions, 26-29.

Duan, X., Niu, T., and Huang, Q. (2018). An Improved Shuffled Frog Leaping Algorithm and Its Application in Dynamic Emergency Vehicle Dispatching. Mathematical Problems in Engineering, 2018.

Goli, A., Tirkolaee, E. B., Malmir, B., Bian, G. B., & Sangaiah, A. K. (2019). A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand. Computing, 101(6), 499-529.

Golpîra, H., & Tirkolaee, E. B. (2019). Stable maintenance tasks scheduling: A bi-objective robust optimization model. Computers & Industrial Engineering, 137, 106007.

Gupte, A., Ahmed, S., Cheon, M. S., & Dey S., (2013), Solving mixed integer bilinear problems using MILP formulations. SIAM Journal on Optimization, 23(2), 721-744.

Gutjahr, W. J., and Nolz, P. C. (2016). Multicriteria optimization in humanitarian aid. European Journal of Operational Research, 252(2), 351-366.

Huang M., Smilowitz K., Balcik B., (2012), Models for relief routing: Equity, efficiency and efficacy. Transportation Research Part E: Logistics and Transportation Review, 48, 2-18.

Halskau, Ø., (2014), Offshore Helicopter Routing in a Hub and Spoke Fashion: Minimizing Expected Number of Fatalities. Procedia Computer Science, 31, 1124-1132.‏

Jahre, M., Persson, G., Kovács, G., & Spens, K. M. (2007). Humanitarian logistics in disaster relief operations. International Journal of Physical Distribution & Logistics Management. 37(2), 99-114.

Knyazkov K., Derevitsky I., Mednikov L., & Yakovlev A., (2015), Evaluation of dynamic ambulance routing for the transportation of patients with acute coronary syndrome in Saint-Petersburg. Procedia Computer Science, 66, 419-428.‏

Knott R., (1987), The logistics of bulk relief supplies. Disasters, 11, 113-115.

Liu, Y., Lei, H., Zhang, D., and Wu, Z. (2018). Robust optimization for relief logistics planning under uncertainties in demand and transportation time. Applied Mathematical Modeling, 55, 262-280.

Maghfiroh, M.F., and Hanaoka, S. (2018). Dynamic truck and trailer routing problem for last mile distribution in disaster response. Journal of Humanitarian Logistics and Supply Chain Management, 8(2), 252-278.

Mavrotas, G., (2009), Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied Mathematics and Computation, 213(2), 455-465.

Mostafaeipour, A., Qolipour, M., Rezaei, M., & Babaee-Tirkolaee, E. (2019). Investigation of off-grid photovoltaic systems for a reverse osmosis desalination system: A case study. Desalination, 454, 91-103.

Naji-Azimi Z., Renaud J., Ruiz A., Salari M., (2012), A covering tour approach to the location of satellite distribution centers to supply humanitarian aid. European Journal of Operational Research, 222, 596-605.

Nolz P.C., Doerner K.F., Gutjahr W.J., Hartl R.F., (2010), A bi-objective metaheuristic for disaster relief operation planning. Advances in Multi-Objective Nature Inspired Computing, 272, 167-187.

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

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

Sangaiah, A. K., Tirkolaee, E. B., Goli, A., & Dehnavi-Arani, S. (2019). Robust optimization and mixed-integer linear programming model for LNG supply chain planning problem. Soft Computing, 1-21.

Talarico Luca, Frank Meisel, Kenneth Sörensen., (2015), Ambulance routing for disaster response with patient groups. Computers & Operations Research, 56, 120-133.

Tavana, M., Abtahi, A. R., Di Caprio, D., Hashemi, R., and Yousefi-Zenouz, R. (2018). An integrated location-inventory-routing humanitarian supply chain network with pre-and post-disaster management considerations. Socio-Economic Planning Sciences, 64, 21-37.

Tirkolaee, E. B., Mahdavi, I., & Esfahani, M. M. S. (2018). A robust periodic capacitated arc routing problem for urban waste collection considering drivers and crew’s working time. Waste Management, 76, 138-146.

Tirkolaee, E. B., Goli, A., Hematian, M., Sangaiah, A. K., & Han, T. (2019). Multi-objective multi-mode resource constrained project scheduling problem using Pareto-based algorithms. Computing, 101(6), 547-570.

Tirkolaee, E. B., Mahdavi, I., Esfahani, M. M. S., & Weber, G. W. (2020). A robust green location-allocation-inventory problem to design an urban waste management system under uncertainty. Waste Management, 102, 340-350.

Tofighi S., Torabi S.A., Mansouri S.A., (2015), Humanitarian logistics network design under mixed uncertainty. European Journal of Operational Research, 250.1: 239-250.

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

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

Zhang, B., Li, H., Li, S., and Peng, J. (2018). Sustainable multi-depot emergency facilities location-routing problem with uncertain information. Applied Mathematics and Computation, 333, 506-520.