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Fuzzy multi-objective stochastic programming model for disaster relief logistics considering telecommunication infrastructures: a case study

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Abstract

In humanitarian relief logistics, providing a safe place for evacuees, supplying relief commodities and designing a proper telecommunication infrastructure, for fast communications during disaster, are important issues. Therefore, in this paper, we develop a fuzzy scenario-based optimization model concerning location of shelters, relief distribution centers and telecommunication towers. Towards effective management and reliable servicing, telecommunication towers and shelters are considered to constitute integrated facilities (shelter-TTs). Moreover, to enhance efficiency of emergency services during disaster, backup relief distribution centers, and to approach the model to the real world, failure probabilities in the routes and the relief distribution centers are considered. The problem is formulated in a nonlinear and multi-objective model. Nonlinearity is treated by applying heuristic arguments in conjunction with Lp-metrics method. Finally, the developed model for the case study of flood disaster in an urban district in Iran is implemented. The results demonstrate that the proposed model can help make decisions on both the preparation and response phases in humanitarian relief logistics.

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References

  • Akgün İ, Gümüşbuğa F, Tansel B (2015) Risk based facility location by using fault tree analysis in disaster management. Omega 52:168–179

    Article  Google Scholar 

  • Altay N, Green WG (2006) OR/MS research in disaster operations management. Eur J Oper Res 175(1):475–493

    Article  Google Scholar 

  • Altinel IK, Aras N, Guney E, Ersoy C (2006) Effective coverage in sensor networks: binary integer programming formulations and heuristics. In: IEEE international conference on communications, vol 9. Istanbul, pp 4014–4019

  • Ardalan A, Naieni KH, Mahmoodi M, Zanganeh AM, Keshtkar AA, Honarvar MR, Kabir MJ (2009) Flash flood preparedness in Golestan province of Iran: a community intervention trial. Am J Disaster Med 5(4):197–214

    Article  Google Scholar 

  • Aydogan EK, Karaoglan I, Pardalos PM (2012) HGA: hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems. Appl Soft Comput 12(2):800–806

    Article  Google Scholar 

  • Balcik B, Beamon BM (2008) Facility location in humanitarian relief. Int J Logist 11(2):101–121

    Article  Google Scholar 

  • Barbarosoǧlu G, Arda Y (2004) A two-stage stochastic programming framework for transportation planning in disaster response. J Oper Res Soc 55(1):43–53

    Article  Google Scholar 

  • Barzinpour F, Esmaeili V (2014) A multi-objective relief chain location distribution model for urban disaster management. Int J Adv Manuf Technol 70(5–8):1291–1302

    Article  Google Scholar 

  • Birge JR, Louveaux F (2011) Introduction to stochastic programming. Springer, Berlin

    Book  Google Scholar 

  • Bozorgi-Amiri A, Jabalameli MS, Al-e-Hashem SM (2013) A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR Spectr 35(4):905–933

    Article  Google Scholar 

  • Brauers WKM (2008) Multi-objective decision making by reference point theory for a wellbeing economy. Oper Res Int J 8(1):89–104

    Article  Google Scholar 

  • Chang MS, Tseng YL, Chen JW (2007) A scenario planning approach for the flood emergency logistics preparation problem under uncertainty. Transp Res Part E Logist Transp Rev 43(6):737–754

    Article  Google Scholar 

  • Chanta S, Sangsawang O (2012) Shelter-site selection during flood disaster. In: Lecture notes in management science, vol 4, pp 282–288

  • Daskin MS, Dean LK (2005) Location of health care facilities. In: Operations research and health care. Springer, US, pp 43–76. doi:10.1007/1-4020-8066-2_3

  • Davis T (1993) Effective supply chain management. Sloan Manag Rev 34:35

    Google Scholar 

  • Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Hoboken

    Google Scholar 

  • Dhillon SS, Chakrabarty K (2003) Sensor placement for effective coverage and surveillance in distributed sensor networks, vol 3. IEEE, pp 1609–1614

  • Dhillon SS, Chakrabarty K, Iyengar SS (2002) Sensor placement for grid coverage under imprecise detections. In: Proceedings of the fifth international conference on information fusion, vol 2. IEEE, pp 1581–1587

  • Djalali A, Hosseinijenab V, Hasani A, Shirmardi K, Castrén M, Öhlén G, Panahi F (2009) A fundamental, national, medical disaster management plan: an education-based model. Prehosp Disaster Med 24(06):565–569

    Article  Google Scholar 

  • Döyen A, Aras N, Barbarosoğlu G (2012) A two-echelon stochastic facility location model for humanitarian relief logistics. Optim Lett 6(6):1123–1145

    Article  Google Scholar 

  • Fiedrich F, Gehbauer F, Rickers U (2000) Optimized resource allocation for emergency response after earthquake disasters. Saf Sci 35(1):41–57

    Article  Google Scholar 

  • Ghahroodi Tali M, Servati MR, Sarafi M, Musavipoor M, Derafshi KhB (2012) Vulnerability assessment due to the floods in Tehran. Relief Rescue Q 4(3):79–92 (in Persian)

    Google Scholar 

  • Guha-Sapir D, Hoyois P, Below R (2014) Annual disaster statistical review 2012: the numbers and trends. 2013. Centre for Research on the Epidemiology of Disasters (CRED), Institute of Health and Society (IRSS) and Universite catholoque de Louvain, Louvain-la-neuve, Belgium

  • Güney E, Altınel IK, Aras N, Ersoy C (2010) A tabu search heuristic for point coverage, sink location, and data routing in wireless sensor networks. In: Evolutionary computation in combinatorial optimization. Springer, Berlin, pp 83–94. doi:10.1007/978-3-642-12139-5_8

  • Hwang HS (2004) A stochastic set-covering location model for both ameliorating and deteriorating items. Comput Ind Eng 46(2):313–319

    Article  Google Scholar 

  • Ingram J (1987) Food and disaster relief issues of management policy. Disasters 12(1):12–18

    Article  Google Scholar 

  • Irohara T, Kuo YH, Leung JM (2013) From preparedness to recovery: a tri-level programming model for disaster relief planning. In: Computational logistics. Springer, Berlin, pp 213–228. doi:10.1007/978-3-642-41019-2_16

  • Jalali S (2014) Disconnection of communication networks with five-magnitude earthquake. World Econ Newsp 3226:7 (in Persian)

    Google Scholar 

  • Jiménez M, Arenas M, Bilbao A, Rodrı MV (2007) Linear programming with fuzzy parameters: an interactive method resolution. Eur J Oper Res 177(3):1599–1609

    Article  Google Scholar 

  • Keeney GB (2004) Disaster preparedness: what do we do now? J Midwifery Women’s Health 49(4):2–6

    Article  Google Scholar 

  • Knott R (1987) The logistics of bulk relief suppliers. Disaster 11:113–115

    Article  Google Scholar 

  • Kongsomsaksakul S, Yang C (2005) Shelter location-allocation model for flood evacuation planning. J East Asia Soc Transp Stud 6:4237–4252

    Google Scholar 

  • Kwasinski A (2009) Telecom power planning for natural disasters: technology implications and alternatives to US Federal Communications Commission’s “Katrina Order” in view of the effects of 2008 Atlantic Hurricane Season. In: 31st International telecommunications energy conference, 2009. INTELEC 2009. IEEE, pp 1–6

  • Kwasinski A (2011) Effects of notable natural disasters from 2005 to 2011 on telecommunications infrastructure: lessons from on-site damage assessments. In: 2011 IEEE 33rd international telecommunications energy conference (INTELEC). IEEE, pp 1–9

  • Kwasinski A, Weaver WW, Chapman PL, Krein PT (2006) Telecommunications power plant damage assessment caused by Hurricane Katrina-Site survey and follow-up results. In: 28th annual international telecommunications energy conference, 2006. INTELEC’06. IEEE, pp 1–8

  • Li Q, Zeng B, Savachkin A (2013) Reliable facility location design under disruptions. Comput Oper Res 40(4):901–909

    Article  Google Scholar 

  • Liberatore F, Pizarro C, de Blas CS, Ortuño MT, Vitoriano B (2013) Uncertainty in humanitarian logistics for disaster management. A review. In: Decision aid models for disaster management and emergencies. Atlantis Press, pp 45–74

  • Lim M, Daskin MS, Bassamboo A, Chopra S (2010) A facility reliability problem: formulation, properties, and algorithm. Naval Res Logist (NRL) 57(1):58–70

    Google Scholar 

  • Lin YH, Batta R, Rogerson PA, Blatt A, Flanigan M (2012) Location of temporary depots to facilitate relief operations after an earthquake. Socio-Econ Plan Sci 46(2):112–123

    Article  Google Scholar 

  • Liu Q, Ruan X, Shi P (2008) Selection of emergency shelter sites for seismic disasters in mountainous regions: lessons from the Wenchuan Ms 8.0 Earthquake, China. J Asian Earth Sci 40:926–934

    Article  Google Scholar 

  • Mete HO, Zabinsky ZB (2010) Stochastic optimization of medical supply location and distribution in disaster management. Int J Product Econ 126(1):76–84

    Article  Google Scholar 

  • Pardalos PM, Aydogan EK, Gurbuz F, Demirtas O, Bakirli BB (2013) Fuzzy combinatorial optimization problems. In: Handbook of combinatorial optimization. Springer, New York, pp 1357–1413. doi:10.1007/978-1-4419-7997-1_68

  • Pishvaee MS, Torabi SA (2010) A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets Syst 161(20):2668–2683

    Article  Google Scholar 

  • Pishvaee MS, Torabi SA, Razmi J (2012) Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty. Comput Ind Eng 62(2):624–632

    Article  Google Scholar 

  • Rath S, Gutjahr WJ (2011) A math-heuristic for the warehouse location–routing problem in disaster relief. Comput Oper Res 42(2):25–39

    Google Scholar 

  • Rawls CG, Turnquist MA (2012) Pre-positioning and dynamic delivery planning for short-term response following a natural disaster. Socio-Econ Plan Sci 46(1):46–54

    Article  Google Scholar 

  • Saadatseresht M, Mansourian A, Taleai M (2009) Evacuation planning using multi-objective evolutionary optimization approach. Eur J Oper Res 198:305–314

    Article  Google Scholar 

  • Saffari A, Sasanpoor F, Musavand J (2011) Vulnerability assessment of urban areas against flood risk GIS and fuzzy logic case study: Region 3 of Tehran. J Appl Res Geogr Sci 11(20):129–150 (in Persian)

    Google Scholar 

  • Salmerón J, Apte A (2010) Stochastic optimization for natural disaster asset prepositioning. Prod Oper Manag 19(5):561–574

    Article  Google Scholar 

  • Shen Z, Dessouky MM, Ordóñez F (2009) A two-stage vehicle routing model for large-scale bioterrorism emergencies. Networks 54(4):255–269

    Article  Google Scholar 

  • Sherali HD, Alameddine A (1992) A new reformulation-linearization technique for bilinear programming problems. J Global Optim 2(4):379–410

    Article  Google Scholar 

  • Sherali HD, Carter TB, Hobeika AG (1991) A location-allocation model and algorithm for evacuation planning under hurricane/flood conditions. Transp Res Part B 25(6):439–452

    Article  Google Scholar 

  • Shiraz RK, Tavana M, Fukuyama H, Di Caprio D (2015) Fuzzy chance-constrained geometric programming: the possibility, necessity and credibility approaches. Oper Res. doi:10.1007/s12351-015-0216-7

    Article  Google Scholar 

  • Tomasini RM, Van Wassenhove LN (2004) Pan-American health organization’s humanitarian supply management system: de-politicization of the humanitarian supply chain by creating accountability. J Public Procure 4:437–449

    Article  Google Scholar 

  • Toregas C, Swain R, ReVelle C, Bergman L (1971) The location of emergency service facilities. Oper Res 19(6):1363–1373

    Article  Google Scholar 

  • Townsend AM, Moss ML (2005) Telecommunications infrastructure in disasters: preparing cities for crisis communications. Tech. Rep. of Robert F. Wagner Graduate School of Public Service, New York University

  • Van Hentenryck P, Bent R, Coffrin C (2010) Strategic planning for disaster recovery with stochastic last mile distribution. In: Integration of AI and OR techniques in constraint programming for combinatorial optimization problems. Springer, Berlin, pp 318–333

  • Widener MJ, Horner MW (2011) A hierarchical approach to modeling hurricane disaster relief goods distribution. J Transp Geogr 19:821–828

    Article  Google Scholar 

  • Yi W, Kumar A (2007) Ant colony optimization for disaster relief operations. Transp Res 43:660–672

    Google Scholar 

Download references

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Authors and Affiliations

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

    Ahmad Mohamadi, Saeed Yaghoubi & Mir Saman Pishvaee

Authors
  1. Ahmad Mohamadi
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  2. Saeed Yaghoubi
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  3. Mir Saman Pishvaee
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Corresponding author

Correspondence to Ahmad Mohamadi.

Appendix

Appendix

There exist several methods in the literature to solve fuzzy problems (Pishvaee et al. 2012). One of the common methods is proposed by Jiménez et al. (2007). Generally, this method relies on strong mathematical concepts and is computationally efficient to solve fuzzy linear problems since it (1) can be applied to different membership functions; (2) preserves the linearity of the model; (3) does not increase the number of objective functions or inequality constraints; (Pishvaee and Torabi 2010; Shiraz et al. 2015).

Consider the following linear programming problem with fuzzy parameters:

$$Min\,\,\tilde{h}X$$
(72)
$$st.\,\,\,\tilde{a}_{i} X \ge \tilde{b}_{i} ,\quad \forall i = 1, \ldots ,l$$
(73)
$$\tilde{a}_{i} X = \tilde{b}_{i} ,\quad \forall i = l + 1, \ldots ,m$$
(74)
$$X \ge 0$$
(75)

Now, let \(\tilde{h} = (h^{p} ,h^{m} ,h^{o} )\) be a triangular fuzzy number. Two following equations are respectively the expected value and the expected interval of triangular fuzzy number \(\tilde{h}\).

$$EV(\tilde{h}) = \frac{{E_{1}^{h} + E_{2}^{h} }}{2} = \frac{{h^{p} + 2h^{m} + h^{o} }}{4}$$
(76)
$$EI(\tilde{h}) = \left[ {E_{1}^{h} ,E_{2}^{h} } \right] = \left[ {\frac{1}{2}\left( {h^{p} + h^{m} } \right),\frac{1}{2}\left( {h^{m} + h^{o} } \right)} \right]$$
(77)

According to Jiménez method, and the study of Pishvaee and Torabi (2010), the above model can be reformulated as follows:

$$Min\,EV(\tilde{h})X$$
(78)
$$st.\,\,\,\,\left[ {(1 - \alpha )E_{2}^{{a_{i} }} + \alpha E_{1}^{{a_{i} }} } \right]X \ge \alpha E_{2}^{{b_{i} }} + (1 - \alpha )E_{1}^{{b_{i} }}$$
(79)
$$\left[ {\left( {\frac{\alpha }{2}} \right)E_{2}^{{a_{i} }} + \left( {1 - \frac{\alpha }{2}} \right)E_{1}^{{a_{i} }} } \right]X \le \left( {1 - \frac{\alpha }{2}} \right)E_{2}^{{b_{i} }} + \left( {\frac{\alpha }{2}} \right)E_{1}^{{b_{i} }}$$
(80)
$$\left[ {\left( {1 - \frac{\alpha }{2}} \right)E_{2}^{{a_{i} }} + \frac{\alpha }{2}E_{1}^{{a_{i} }} } \right]X \ge \frac{\alpha }{2}E_{2}^{{b_{i} }} + \left( {1 - \frac{\alpha }{2}} \right)E_{1}^{{b_{i} }}$$
(81)

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Mohamadi, A., Yaghoubi, S. & Pishvaee, M.S. Fuzzy multi-objective stochastic programming model for disaster relief logistics considering telecommunication infrastructures: a case study. Oper Res Int J 19, 59–99 (2019). https://doi.org/10.1007/s12351-016-0285-2

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