March 14, 2012 at 7:45AM - March 14, 2012 at 9:00AM
MIT Room E62-550
A Case Study on Post-disaster Debris Operations and Solving Multi-period Network Capacity Expansion Problems
Özlem Ergun
Georgia Institute of Technology
Abstract
Debris is the waste generated by hazardous events such as a natural disaster or a terrorist attack. Post-disaster debris collection operations are in general not planned in advance and are done in an ad-hoc way after an event. Issues in tactical and operational planning include clearing quickly, widely, and in ways that is good for the environment and health. Debris impacts the logistics of humanitarian relief and debris generated in some large-scale disasters can be equivalent in volume to years of normal solid waste production in the affected areas. In the context of the Haiti earthquake, six months after the disaster, only 5% of the debris was collected. Hurricane Ike (2008) generated debris enough to fill a football field stacked 2 miles high. The 30-mile debris along the Texas coast that was standing months after Ike's landfall is a testament to the challenges and the inefficiencies that exist in debris clearance operations today.
In this research, we consider the decision problems related to the three phases of debris management operations: clearance, collection, and disposal. We develop mathematical models that capture the important characteristics of the debris related operations in each phase along with methodologies for solving these models efficiently. These models include considerations on fairness (given the public impact nature of the application) and robustness (given various types of uncertainty in these settings). We demonstrate the results of some of these models and algorithms on a case study based on the Haitian Earthquake.
One model that arises from this setting is a multi period network expansion model where flow capacities are big-M constrained and nodes have supply and demand of commodities. The objective is to minimize unsatisfied demand over all of the periods. We discuss this model in detail and show computational results behind restricting the M value to infeasible values as a means of speeding up computation and achieving better solutions. We discuss theoretical reasonings behind our computational findings.
Speaker Bio
Dr. Özlem Ergun is an associate professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology. She is also a co-founder and co-director of the Health and Humanitarian Logistics Research Center at the Supply Chain and Logistics Institute. She received a B.S. in Operations Research and Industrial Engineering from Cornell University in 1996 and a Ph.D. in Operations Research from the Massachusetts Institute of Technology in 2001.
Professor Ergun's research focuses on the design and management of large-scale networks. Specifically, she studies logistics and communications networks that are dynamic and partially decentralized. She has recently focused on understanding how collaboration among different entities can help the entities to be more efficient as well as create value for the overall system. She has applied her work on network design, management and collaboration to problems arising in the airline, ocean cargo and trucking industries and disaster response operations.
Recently, Dr. Ergun has taken a leadership role in promoting the use of systems thinking and mathematical modeling in applications with societal impact within the INFORMS (Institute for Operations Research and Management Science) community. She was a co-chair of the committee that established the INFORMS “Doing Good with Good OR Student Paper Competition”. She was an Editor for INTERFACES special issue on Humanitarian Applications. Finally, as a founding co-director of Center for Health and Humanitarian Logistics at Georgia Tech, she has worked with organizations that respond to humanitarian crisis around the world, including: UN World Food Programme, CARE USA, FEMA, USACE, CDC, AFCEMA, andMedShare International.
Professor Ergun teaches undergraduate and graduate courses in optimization and logistics. She was awarded the NSF Career Award in 2003. She won the EURO/INFORMS 2007 Management Science Strategic Innovation Prize given on the subject of Logistics in 2007.