Disaster Relief - It's About Time

I have been busy co-organizing the 2nd International Conference on Dynamics of Disasters with my great colleagues, Professor Panos M. Pardalos of the University of Florida and Professor Ilias Kotsireas of Wilfrid Laurier University.

I've also been hard at work finishing up our paper for this conference, which takes place in Kalamata, Greece, at the end of June, and  grading the fascinating project papers of the students in my Humanitarian Logistics and Healthcare class.

Nice when your research is synergistic with your teaching!

The conference website now contains the program (to-date)  with confirmed speakers from many countries.

I have been completely engrossed in writing this paper, given the events in Nepal, post the 7.8 magnitude earthquake that struck in April.   One wants to help in any way possible and we have given a financial donation and, as as academics, we can certainly help through our research.

The title of the paper that I will be presenting at the conference is: "A Mean-Variance Disaster Relief Supply Chain Network Model for Risk Reduction with Stochastic Link Costs, Time Targets, and Demand Uncertainty."

This paper builds upon our earlier work in supply chain risk reduction (but in a corporate setting): Risk Reduction and Cost Synergy in Mergers and Acquisitions via Supply Chain Network Integration, Zugang Liu and Anna Nagurney, Journal of Financial Decision Making 7(2): (2011) pp 1-18, and our paper,  An Integrated Disaster Relief Supply Chain Network Model with Time Targets and Demand Uncertainty, Anna Nagurney, Amir H. Masoumi, and Min Yu, in Regional Science Matters: Studies Dedicated to Walter Isard, P. Nijkamp, A. Rose, and K. Kourtit, Editors, Springer International Publishing Switzerland (2015), pp 287-318.

Disaster relief is truly about time and models must incorporate the critical time dimension as ours do. For example, the U.S. Federal Emergency Management Agency (FEMA) has identified key benchmarks to response and recovery, which emphasize time and  they are: to meet the survivors’ initial demands within 72 hours, to restore basic community functionality within 60 days, and to return to as normal of a situation within 5 years. Timely and efficient delivery of relief supplies to the affected population not only decreases the fatality rate but may also prevent chaos.


There can be delays on any of the links in a disaster relief supply chain network and The New York Times on Monday, in an article  by Gardiner Harris, "Nepal's Bureaucracy Blamed as Quake Relief Supplies Pile Up," notes that:  Relief supplies for earthquake victims have been piling up at the airport and in warehouses here because of bureaucratic interference by Nepalese authorities who insist that standard customs inspections and other procedures be followed, even in an emergency, officials with Western governments and aid organizations said on Sunday. 


The article  continues with: The bottleneck was the fact that the bureaucratic procedures were just so heavy,” Jamie McGoldrick, the United Nations resident coordinator, said in an interview. “So many layers of government and so many departments involved, so many different line ministries involved. We don’t need goods sitting in Kathmandu warehouses. We don’t need goods sitting at the airport. We need them up in the affected areas.

We do  the following in our paper:  We develop a mean-variance disaster relief supply chain network model with stochastic link costs and time targets for delivery of the relief supplies at the demand points, under demand uncertainty. The humanitarian organization seeks to minimize its expected total operational costs and the total risk in operations with an individual weight assigned to its valuation of the risk, as well as the minimization of expected costs of shortages and surpluses and tardiness penalties associated with the target time goals at the demand points.


The risk is captured through the variance of the total operational costs, which is relevant to the reporting of the proper use of funds to stakeholders, including donors. The time goal targets associated with the demand points enable prioritization as to the timely delivery of relief supplies. The framework handles both the pre-positioning of relief supplies, whether local or nonlocal, as well as the procurement (local or nonlocal), transport, and distribution of supplies post-disaster. The time element is captured through link time completion functions as the relief supplies progress along paths in the supply chain network. Each path consists of a series of directed links, from the origin node, which represents the humanitarian organization, to the destination nodes, which are the demand points for the relief supplies.

We propose an algorithm, which yields closed form expressions for the variables at each iteration, and demonstrate the efficacy of the framework through a series of illustrative numerical examples, in which trade-offs between local versus nonlocal procurement, post- and pre-disaster, are investigated. The numerical examples include a case study on hurricanes hitting Mexico.

UMass Amherst has a nice release on this conference  and the University of Florida had this writeup posted a while back. 

I look forward to presenting our paper at the International Conference on Dynamics of Disasters. The topic is certainly so relevant and even NSF just released a big press release on 6 projects that it has funded, jointly with Japan, on Big Data and disaster response.