Evaluating relief center designs for disaster relief distribution

Purpose Relief item distribution to victims is a key activity during disaster response. Currently many humanitarian organizations follow simple guidelines based on experience to assess need and distribute relief supplies. However, the interviews with practitioners suggest a problem in efficiency in relief distribution efforts. The purpose of this paper is to develop a model and solution methodology that can estimate relief center (RC) performance, measured by waiting time for victims and throughput, for any RC design and analyze the impact of key design decisions on these performance measures. Design/methodology/approach Interviews with practitioners and current practice guidelines are used to understand relief distribution and a queuing network model is used to represent the relief distribution. Finally, the model is applied to data from the 2015 Nepal earthquake. Findings The findings identify that dissipating congestion created by crowds, varying item assignment decisions to points of distribution, limiting the physical RC capacity to control congestion and using triage queue to balance distribution times, are effective strategies that can improve RC performance. Research limitations/implications This research bases the RC designs on Federal Emergency Management Agency guidelines and assumes a certain area and volunteer availability. Originality/value This paper contributes to humanitarian logistics by discussing useful insights that can impact how relief agencies set up and operate RCs. It also contributes to the queuing literature by deriving analytic solutions for the steady state probabilities of finite capacity, state dependent queues with blocking.

[1]  Alexandre Brandwajn,et al.  An Approximation Method for Tandem Queues with Blocking , 1988, Oper. Res..

[2]  Kaan Ozbay,et al.  Stochastic Humanitarian Inventory Control Model for Disaster Planning , 2007 .

[3]  Jingshan Li,et al.  Performance analysis of production systems with rework loops , 2004 .

[4]  Tayfur M. Altiok,et al.  Approximate analysis of exponential tandem queues with blocking , 1982 .

[5]  Yihong Ru,et al.  A Study on Inventory Management Method in Emergency Logistics Based on Natural Disasters , 2010, 2010 International Conference on E-Product E-Service and E-Entertainment.

[7]  M. Lawley,et al.  Hospital stockpiling for disaster planning , 2011 .

[8]  Emmett J. Lodree,et al.  A Bayesian decision model with hurricane forecast updates for emergency supplies inventory management , 2011, J. Oper. Res. Soc..

[9]  Stanley B. Gershwin,et al.  A decomposition method for analyzing inhomogeneous assembly/disassembly systems , 2000, Ann. Oper. Res..

[10]  Christopher S. Tang,et al.  Buttressing Supply Chains against Floods in Asia for Humanitarian Relief and Economic Recovery , 2014 .

[11]  Serhan Duran,et al.  Pre-Positioning of Emergency Items Worldwide for CARE International , 2008 .

[12]  Taesik Lee,et al.  Simultaneous Location of Trauma Centers and Helicopters for Emergency Medical Service Planning , 2014, Oper. Res..

[13]  Richard C. Larson,et al.  Responding to Emergencies: Lessons Learned and the Need for Analysis , 2006, Interfaces.

[14]  Gülay Barbarosoglu,et al.  A two-stage stochastic programming framework for transportation planning in disaster response , 2004, J. Oper. Res. Soc..

[15]  Ronald P. Archer International Federation of the Red Cross and Red Crescent Societies : Library and Information Services Network (LISN); final assessment report (1995) , 1995 .

[16]  Kenneth V Iserson,et al.  Triage in medicine, part I: Concept, history, and types. , 2007, Annals of emergency medicine.

[17]  Liang Liang,et al.  Pre-purchasing with option contract and coordination in a relief supply chain , 2015 .

[18]  Zhongsheng Hua,et al.  Forecasting demand of commodities after natural disasters , 2010, Expert Syst. Appl..

[19]  Yves Dallery,et al.  A robust decomposition method for the analysis of production lines with unreliable machines and finite buffers , 1995, Ann. Oper. Res..

[20]  Rajan Batta,et al.  Covering-Location Models for Emergency Situations That Require Multiple Response Units , 1990 .

[21]  L. V. Wassenhove,et al.  On the appropriate objective function for post‐disaster humanitarian logistics models , 2013 .

[22]  Rajan Batta,et al.  Review of recent developments in OR/MS research in disaster operations management , 2013, Eur. J. Oper. Res..

[23]  Benita M. Beamon,et al.  Facility location in humanitarian relief , 2008 .

[24]  B. Balcik,et al.  Supplier Selection for Framework Agreements in Humanitarian Relief , 2014 .

[25]  Jiuh-Biing Sheu,et al.  Dynamic Relief-Demand Management for Emergency Logistics Operations Under Large-Scale Disasters , 2010 .

[26]  Christos Douligeris,et al.  Optimal location and capacity of emergency cleanup equipment for oil spill response , 1997 .

[27]  Nezih Altay,et al.  OR/MS research in disaster operations management , 2006, Eur. J. Oper. Res..

[28]  Maged Dessouky,et al.  A modeling framework for facility location of medical services for large-scale emergencies , 2007 .

[29]  W. Whitt,et al.  Performance of the Queueing Network Analyzer , 1983, The Bell System Technical Journal.

[30]  Anup Roop Akkihal,et al.  Inventory pre-positioning for humanitarian operations , 2006 .

[31]  Samuel D. Conte,et al.  Elementary Numerical Analysis , 1980 .

[32]  Pinar Keskinocak,et al.  Pre-Positioning of Emergency Items for CARE International , 2011, Interfaces.

[33]  W. Whitt,et al.  The Queueing Network Analyzer , 1983, The Bell System Technical Journal.

[34]  Xing Hong,et al.  Stochastic network design for disaster preparedness , 2015 .

[35]  Stanley B. Gershwin,et al.  An Efficient Decomposition Method for the Approximate Evaluation of Tandem Queues with Finite Storage Space and Blocking , 1987, Oper. Res..

[36]  Shinya Hanaoka,et al.  Production , Manufacturing and Logistics Relief inventory modelling with stochastic lead-time and demand , 2014 .

[37]  Leyuan Shi,et al.  Optimization Based Method for Supply Location Selection and Routing in Large-Scale Emergency Material Delivery , 2011, IEEE Transactions on Automation Science and Engineering.

[38]  Yung-Li Lily Jow,et al.  An approximation method for tandem queues with blocking , 1988 .

[39]  Jayashankar M. Swaminathan,et al.  Inventory Management in Humanitarian Operations: Impact of Amount, Schedule, and Uncertainty in Funding , 2014, Manuf. Serv. Oper. Manag..

[40]  Subodha Kumar,et al.  An Integrated Logistic Model for Predictable Disasters , 2016 .

[41]  Ann Melissa Campbell,et al.  Routing for Relief Efforts , 2008, Transp. Sci..

[42]  Li Rui,et al.  Research on emergency supplies demand forecasting model , 2010, 2010 The 2nd International Conference on Industrial Mechatronics and Automation.

[43]  Yves Dallery,et al.  On Decomposition Methods for Tandem Queueing Networks with Blocking , 1993, Oper. Res..

[44]  J. Goentzel,et al.  Models and metrics to assess humanitarian response capacity , 2016 .

[45]  Jessica L. Heier Stamm,et al.  Improving Humanitarian Operations through Technology‐Enabled Collaboration , 2014 .

[46]  Andrea Matta,et al.  A decomposition approximation for three-machine closed-loop production systems with unreliable machines, finite buffers and a fixed population , 2009 .

[47]  Cejun Cao,et al.  Review of Relief Demand Forecasting Problem in Emergency Logistic System , 2015 .

[48]  D. C. Whybark,et al.  Issues in managing disaster relief inventories , 2007 .

[49]  Peter Tregenza The design of interior circulation: People and buildings , 1976 .

[50]  N. C. Simpson,et al.  Fifty years of operational research and emergency response , 2009, J. Oper. Res. Soc..