SavedAdd your journal

Journals / An International Journal of Optimization and Control: Theories & Applications (IJOCTA) / 2017 / Cilt: 7 - Sayı: 1

A stochastic location and allocation model for critical items to response large-scale emergencies: A case of Turkey

Erkan CELİK
Nezir AYDIN
Alev Taskin GUMUS
Pages
1–15
Publication Date
2022-10-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results provide a framework for relief organizations to determine the location and number of DCs in different settings, by using the proposed model considering the main parameters, as; capacity of facilities, probability of being affected for each demand points, severity of events, maximum distance between a demand point and distribution center.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper aims to decide on the number of facilities and their locations, procurement for pre and post-disaster, and allocation to mitigate the effects of large-scale emergencies.
📊
Key Findings & Data:Also,theuseofthemodelisdemonstratedthroughacasestudyfor prepositioning of supplies in probable large-scale emergencies in the eastern and southeastern Anatolian sides of Turkey.
💡
Conclusion & Impact:The results provide a framework for relief organizations to determine the location and number of DCs in different settings, by using the proposed model considering the main parameters, as; capacity of facilities, probability of being affected for each demand points, severity of events, maximum distance between a demand point and distribution center.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

This paper aims to decide on the number of facilities and their locations, procurement for pre and post-disaster, and allocation to mitigate the effects of large-scale emergencies. A two-stage stochastic mixed integer programming model is proposed that combines facility location-prepositioning, decisions on pre-stocking levels for emergency supplies, and allocation of located distribution centers (DCs) to affected locations and distribution of those supplies to several demand locations after large-scale emergencies with uncertainty in demand. Also,theuseofthemodelisdemonstratedthroughacasestudyfor prepositioning of supplies in probable large-scale emergencies in the eastern and southeastern Anatolian sides of Turkey. The results provide a framework for relief organizations to determine the location and number of DCs in different settings, by using the proposed model considering the main parameters, as; capacity of facilities, probability of being affected for each demand points, severity of events, maximum distance between a demand point and distribution center.

🔖

Cite This Article

CELİK, E., AYDIN, N., GUMUS, A. T. (2022). A stochastic location and allocation model for critical items to response large-scale emergencies: A case of Turkey. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), Cilt: 7 - Sayı: 1, 1–15. https://ajindex.com/articles/440658

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (1)

  1. Murali, P. Ordóñez, F. & Dessouky, M.M., "Facility location under demand uncertainty: Response to a large-scale bio-terror attack",Socio-Economic Planning Sciences,vol. 46 No. 1, pp. 78-87(2012). [2]Sheu, J. B., "An emergency logistics distribution approach for quick response to urgent relief demand in disasters",Transportation Research Part E: Logistics and Transportation Review,Vol. 43 No. 6, pp. 687-709(2007). [3]Jia, H. Ordonez, F. & Dessouky, M., "A modeling framework for facility location of medical services for large-scale emergencies", IIE Transactions, Vol. 39 No. 1, pp. 41-55(2007a). [4]Jia, H. Ordonez, F. & Dessouky, M., "Solution approaches for facility location of medical supplies for large-scale emergencies", Computers & Industrial Engineering, Vol. 52, pp. 257-276(2007b). [5]Rawls, C.G. & Turnquist, M.A., "Pre-positioning ofemergency supplies for disaster response",Transportation Research Part B: Methodological,Vol. 44 No. 4, pp. 521-534(2010). [6]Megiddo N. & Supowit K.J., "On the complexity of some common geometric location problems", SIAM Journal on Computing, Vol. 13, pp. 182-196(1984). [7]Haugen, K.K. Løkketangen, A. & Woodruff, D.L., "Progressive hedging as a meta-heuristic applied to stochastic lot-sizing", European Journal of Operational Research, Vol. 132 No. 1, pp. 116-122(2001). [8]Birge, J.R. & Louveaux, F., "Introduction to stochastic programming", Springer-Verlag, New York(1997). [9]Aydin, N., and Murat, A. "A swarm intelligence based sample average approximation algorithm for the capacitated reliable facility location problem", International Journal of Production Economics, Vol 145 No. 1, pp. 173-183(2013). [10]Ayvaz, B., Bolat, B., & Aydin, N. "Stochastic reverse logistics network design for waste of electrical and electronic equipment". Resources, Conservation and Recycling, Vol 104, pp. 391-404(2015). [11]Balcik, B., & Beamon, B. M. "Facility location in humanitarian relief", International Journal of Logistics, Vol 11 No. 2, pp. 101-121(2008). [12]Altay, N. Green, W.G., "OR/MS research in disaster operations management",European Journal of Operational Research,Vol. 175 No.1, pp. 475-493(2006). [13]Galindo, G. & Batta, R., "Review of Recent Developments in OR/MS Research in Disaster Operations Management", European Journal of Operational Research, Vol. 230 No. 2, pp. 201-211(2013). [14]Caunhye, A.M. Nie, X. & Pokharel, S., "Optimization models in emergency logistics: A literature review", Socio-Economic Planning Scienc