SciELO - Scientific Electronic Library Online

 
vol.22 número1Note from the EditorConvergence of technologies índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Articulo

Indicadores

Links relacionados

  • En proceso de indezaciónCitado por Google
  • En proceso de indezaciónSimilares en Google

Compartir


South African Journal of Industrial Engineering

versión On-line ISSN 2224-7890
versión impresa ISSN 1012-277X

S. Afr. J. Ind. Eng. vol.22 no.1 Pretoria  2011

 

Modelling of uncertainty in minimising the cost of inventory for disaster relief

 

 

E. van WykI; W.L. BeanII; V.S.S. YadavalliIII

IDepartment of Industrial and Systems Engineering, University of Pretoria, South Africa. estelle.vanwyk@up.ac.za
IILogistics and Quantitative, Methods CSIR Built Environment, South Africa. wbean@csir.co.za
IIIDepartment of Industrial and Systems Engineering, University of Pretoria, South Africa. sarma.yadavalli@up.ac.za

 

 


ABSTRACT

Natural disasters - and even those caused by people - are largely unpredictable. So disasters need to be researched and their impact fully understood, so that the aid supplies required to ensure survival during and after disaster events will be available. The member states of the Southern African Development Community (SADC) are the countries of interest for this paper, as insufficient research has been conducted into inventory pre-positioning for disaster response in these countries. It is vital to anticipate the needs of disaster victims in potential disasters. These needs are evaluated according to the types and amounts of aid supplies required. This paper proposes a stochastic inventory model that can be applied in a generic way to any SADC country, providing a means to improve disaster preparedness through keeping aid supplies in pre-positioned facilities in the SADC region, at reasonable and affordable cost.


OPSOMMING

Natuurlike en mensgemaakte rampe is grootliks onvoorspelbaar. Gevolglik moet rampe nagevors en hul impak ten volle begryp word, sodat noodvoorrade wat benodig word vir oorlewing doeltreffend beplan kan word vir aanwending tydens en na rampgebeure. Die lede van die Suid-Afrikaanse Ontwikkelingsgemeenskap (SAOG) is die lande van belang vir hierdie artikel omrede navorsing oor voorraadhouding vir rampreaksie in hierdie betrokke lande tot nog toe onvoldoende was. Dit is noodsaaklik om doeltreffend in die behoeftes van rampslagoffers te voorsien. Hierdie behoeftes word beoordeel na aanleiding van die aard en hoeveelhede van noodvoorrade wat benodig mag word in ramptoestande. Hierdie artikel stel 'n stochastiese voorraadmodel voor vir toepassing op 'n generiese wyse in enige SAOG land, om sodoende 'n metode te verskaf om rampvoorbereiding te verbeter deur die opgaar van noodvoorrade in vooraf-geïdentifiseerde fasiliteite binne die SAOG, teen redelike en bekostigbare koste.


 

 

“Full text available only in PDF format”

 

 

REFERENCES

[1] Altay, N. & Green, W.G. 2006. OR/MS research in disaster operations management. European Journal of Operations Research, 175, pp 475-493.         [ Links ]

[2] Beamon, B.M. & Balcik, B. 2008. Performance measurement in humanitarian relief chains. International Journal of Public Sector Management, 21, pp 4-25.         [ Links ]

[3] Beamon, B.M. & Kotleba, S. 2006b. Inventory modelling for complex emergencies in humanitarian relief operations. International Journal of Logistics: Research and Applications, 9(1), pp 1-18.         [ Links ]

[4] Bryson, K-M.N., Millar, H., Joseph, A. & Mobolurin, A. 2002. Using formal MS/OR modelling to support disaster recovery planning. European Journal of Operations Research, 141, pp 679-688.         [ Links ]

[5] Card, W.I. & Mooney, G.H. 1977. What is the monetary value of a human life? British Medical Journal, 2, pp 1627-1629.         [ Links ]

[6] Coyle, J.J., Bardi, E.J., & Langley Jr., C.J.. 2003. The management of business logistics: A supply chain perspective. Thomson Learning.         [ Links ]

[7] CRED. 2009. Country profiles. Technical report, Centre for Research on the Epidemiology of Disasters. Available online from http://www.emdat.be/disaster-profiles/. Retrieved 12 March 2010.         [ Links ]

[8] De Beer, E.J.H. & Van Niekerk, E. 2004. The estimation of unit costs of road traffic accidents in South Africa. Technical report, National Department of Transport.         [ Links ]

[9] Duran, S., Gutierrez, M.A. & Keskinocak, P. 2009. Pre-positioning of emergency items worldwide for CARE International. INFORMS. Doi: 10.1287.         [ Links ]

[10] Hills, A. 1998. Seduced by recovery: The consequences of misunderstanding disaster. Journal of Contingencies and Crisis Management, 6(3), pp 162-170.         [ Links ]

[11] IFRC. 2009. Disaster reduction programme 2001-2008. International Federation of Red Cross and Red Crescent Societies. Available online from http://www.ifrc.org/Docs/pubs/disasters/resources/reducing-risks/dr-programme-en.pdf. Retrieved 12 March 2010.         [ Links ]

[12] Karlin, S. & Fabens, A. 1960. The (s,S) inventory model under Markovian demand process. Mathematical Methods in the Social Sciences (Chapter 8), pp 159-175.         [ Links ]

[13] Kovacs, G. & Spens, K.M. 2007. Humanitarian logistics in disaster relief operations. International Journal of Physical Distribution and Logistics Management, 37(2), pp 99-114.         [ Links ]

[14] Leichenko, R.M. & O'Brien, K.L. 2002. The dynamics of rural vulnerability to global change: The case of Southern Africa. Mitigation and Adaption Strategies for Global Change, 7, pp 1-18.         [ Links ]

[15] Mete, H.O. & Zabinsky, Z.B. 2009. Stochastic optimization of medical supply location and distribution in disaster management. International Journal of Production Economics. Doi: 10.1016/j.ijpe.2009.10.004.         [ Links ]

[16] Rawls, C.G. & Turnquist, M.A. 2009. Pre-positioning of emergency supplies for disaster response. Transportation Research, Part B. Doi: 10.1016/j.trb.2009.08.003.         [ Links ]

[17] Samii, R. & Wassenhove, L.V. 2002. IFRC Choreographer of Disaster Management Hurricane Mitch. Technical report, INSEAD Case Study No.06/ 2002-5039.         [ Links ]

[18] Taskin, S. & Lodree, E.J.J. 2009. Inventory decisions for emergency supplies based on hurricane count predictions. International Journal of Production Economics, Doi: 10.1016/jipe.2009.10.008, pp 1-10.         [ Links ]

[19] Tomasini, R. & Wassenhove, L.V. 2009. Humanitarian logistics. Palgrave Macmillan.         [ Links ]

[20] Turoff, M. 2002. Past and future emergency response information systems. Communications of the ACM, 45, 29-33.         [ Links ]

[21] Van Wyk, E., Yadavalli, V.S.S. & Bean, W. 2011. Strategic inventory management for disaster relief. To appear in Management Dynamics, 2011.         [ Links ]

[22] Whybark, D. 2007. Issues in managing disaster relief inventories. International Journal of Production Economics, 108, 228-235.         [ Links ]

[23] Winston, W.L. 2004. Introduction to probability models, Volume 2. Curt Hinrichs, 4th edition. 12        [ Links ]

 

 

* Corresponding author.
1 The author was enrolled for a B Eng (Industrial) degree in the Department of Industrial and Systems Engineering, University of Pretoria.

Creative Commons License Todo el contenido de esta revista, excepto dónde está identificado, está bajo una Licencia Creative Commons