Thematic Spotlight: Operations Research in Disaster Management

Operations research in disaster management deals with methods and techniques in predicting and preventing disasters, such as hurricane, earthquake, floods, etc., and post-disaster management problems, such as relief measure, food supply, etc. The field has grown into a significant domain because of the direct impact that it has on people and their health. Disaster management is a sequence of operations that can be broadly classified based on the phases of a disaster, including mitigation, preparedness, response, and recovery. The Mitigation is the application of measures to prevent the onset or reduce the impact of the disaster. The Preparedness phase activities prepare the community to respond to disaster events.  The Response phase is the utilization of resources and guidelines to preserve life, property of the community. Finally, the Recovery phase includes both long term and short term actions taken after the disaster occurs to stabilize the situation and restore normalcy. OR tools aid these operations from the standpoint of optimizing them and making the best possible use of available information and decision making. Tsunami in 2004 highlighted the gap in the disaster relief research and models in contrast to the other fields as it caused challenges in major relief measures and led to the realization of significance of humanitarian logistics. The OR/MS community faced the challenge of modeling, as the characteristics of disasters make response complicated and the inherent stochastic nature of the problem makes it hard, yet an important problem of practical significance. The seeming randomness of the impacts and problems and uniqueness of the incidents require dynamic, real-time effective solution, thus making OR/MS tools suitable.

International Federation of Red Cross and Red Crescent Societies (IFRC) recorded 7184 disasters between 2000 and 2009. The world trade center attack (2011), Tsunami (2004) and hurricane Katrina (2005) and the Haiti earthquake (2010) put together have an estimated economic damage cost of 986,691 million dollars with 1,105,352 causalities. The daunting statistics show the need to develop strategies to reduce the impacts of disasters on humankind.

Disasters can be natural or man-made, but both of them have severe consequences which are not well understood beyond the immediate effects on the people. For example, a hurricane can cause an entire city or state come to a standstill and with the globalization of economy and open markets, the impact transcends into regions which are not affected directly by the hurricane. The indirect consequences can be financial and hindrance to economic growth. A major incident in the economic powerhouse of the world can bring down the entire worlds’ economy or can cause a shift in the economic growth pattern.  There is an increasing recognition of the need for study of OR/MS issues in disaster management. The area that lacks OR/MS research is recovery planning in addressing key questions such as, what are the operational problems of damage assessment and cleanup? what are the key characteristics of food collection, allocation, and distribution? The announcement of EURO Management Science Strategic Innovation Prize (MSSIP 2006) and INFORMS 2010 conference hosting 16 tracks accounting for a total of 61 presentations in relevance to disaster management are promising steps towards answering the questions.

Disaster management research can be traced back as early as World War II in the context of emergency responses and services. Although not until 1970, many seminal works on quick response to emergencies were cast as mathematical problems. The fire station location problem, unit commitment problem, and queuing and simulation models for ambulance services are some of the problems studied in the 1970s.  Mathematical programming was the most common OR approach to the difficulties of disaster management followed by probability and statistics and then by simulation. Mathematical models dealt with problems like facility location, relief scheduling, stochastic assignment problem, distribution and transportation of relief supplies, such as food, medicine, etc. Statistics and probability played an essential role in mitigation and preparedness phase with forecasts of disaster events from historical data and also in data cleaning for further use in mathematical models while simulation was used to validate and replicate the disaster events and study their dynamics in order to provide a better understanding of the problem.

While the emergency-related OR research date back to 1970, early 80’s research was focused more on urban policy and emergency planning, such as fire station location, emergency medical service location planning, etc. In the 1990s, disaster-related research took off. The Union Carbide tragedy in Bhopal, India or the 1986 Chernobyl nuclear accident were the early disasters studied in extensive details. Within disaster management, works on applications of OR tools, such as network analysis and simulation to the process evaluation were relatively rare until the 1980s.  From 1990 to early 2000,  crowd evacuation in large public gatherings, ship evacuation, and emergency flight evacuation modeling were studied as fluid flow models or based on simulation models. Post tsunami (2004) OR methods in disaster management increased with its application in safety, relief logistics, volunteer scheduling, food distribution, and indirect effects of disasters, such as hurricane disrupts power, causing shutdowns and outages, communication breakdowns, structural damage to building and road networks. The above problems directed the research on the reliability of the underlying systems, such as power and communication networks.

The social and political nature of disaster management make the field suitable for data envelopment analysis, fuzzy systems, and systems dynamics. The key question is how to determine the decision rules on what method to use to address a specific class of disaster problems? Are the urban emergency planning models applicable to disaster relief planning or not? Problems in disaster relief management have multiple contradicting objectives and the social impacts of the relief are difficult to model because it is not straightforward to associate a number with actions that have a direct impact on human life. The interdisciplinary nature of the problem requires coordination between multiple organizations which necessitates effective data acquisition and communication systems. The RODOS (http://www.rodos.fzk.de/) project in Europe is a good example of such efforts.

In summary, the role of OR/MS community has been instrumental in understanding the problem and nature of the disaster management. The nature of the problem needs attention from researchers in different fields, such as computers in OR, risk management, data analysis, and Machine learning for solving the posed challenges. Better management of disaster operations will improve readiness, increase responsiveness, and quick recovery.

References

[1] Nezih Altay, Walter G. Green, 2006 “OR/MS research in disaster operations management,” European Journal of Operational Research, 175(1), 475-493.

[2] Gina Galindo, Rajan Batta, 2013 “Review of recent developments in OR/MS research in disaster operations management,” European Journal of Operational Research, 230(2), 201-211.

Keywords: Disaster operations management; Operations research; Management science