A Real World Humanitarian Logistics Model

 

Floods in Colombia caused widespread damage
Floods in Colombia caused widespread damage

When a disaster such as a flood strikes, the response can be chaotic as various agencies and sources of supply converge on the stricken area. A humanitarian logistics model developed by the Center for Latin American Logistics Innovation (CLI) improves the efficiency of response efforts by taking a holistic view of these operations.

CLI researchers are now refining the model by allowing for social factors such as the influence of activist groups on relief operations. The new work is being carried out in collaboration with Antioquia University, Colombia, University of California, Davis, US, and an independent field expert in Colombian conflict zones.

The current model is driven by the need to minimize three critical features of relief operations: evacuation flow-time, distribution flow-time, and total cost.

“These elements are often managed separately rather than holistically, which gives sub-optimal results. We can use the model to take all relevant factors into account, and help relief agencies to respond more effectively to frequent disasters such as floods,” explains Christopher Mejia, Postdoctoral Associate at CLI. He worked with researchers from Tecnologico de Monterrey, Mexico, Malaga University, Spain, and Complutense University at Madrid, Spain, to develop the model.

Each essential component of a relief effort is built into the model. These activities include aid distribution, evacuation procedures, locations of emergency facilities such as distribution centers, meeting points and shelters, and pre-positioned stocks of supplies.

Another key component taken into account – and one that can be overlooked by aid agencies – is the vulnerability of specific locations. This is especially important in floods where low points in a city, for example, are more prone to flooding than locations on higher ground. Logistics networks can be severely disrupted if critical facilities such as hospitals, shelters, and infrastructure are located in these high-risk areas. The model incorporates a geographic information system that simulates outcomes like these.

A case study based on a catastrophic flood that struck the city of Villahermosa, Mexico in 2007 provides a real-world application of the model. The waters reached a height of some four meters in low-lying parts of the city, affected 160,000 people, and caused $700 million worth of damage.
A total of 129 disaster zones were evacuated during the crisis, and the operation utilized 244 shelters and one distribution center. There were 500 nodes in the logistics network.

Other characteristics of the operation that were used to test the model include the following.

  • The spatial distribution of facilities. This has a major impact on evacuation and supply operations.
  • The number of facilities and extent of available relief resources, both of which tend to be lacking in a disaster of this magnitude.
  • Costs in terms of what resources were required on the ground and the assigned budget for the response program.

The model significantly outperformed the government response program across all of the factors considered. There were also a number of important findings from the analysis. For example, as spatial distribution increases, shelter utilization rates and the number of active meeting points increases. Also, maximum evacuation and distribution flow times decrease as the number of facilities increases.

The next step is to incorporate social factors into the initial generic model to align it even more closely with real-world crisis situations.  Existing research on how social environments can shape logistics networks will be used, says Mejia.

For example, when a disaster alert is issued, people in some communities tend to ignore evacuation measures, preferring instead to stay and rely on their social networks to survive the crisis. This behavior pattern affects the extent to which shelters and other amenities are needed as well as evacuation flows. In order to counter these choices, responders need to build trust and perhaps educate local populations on the wider implications of their decisions.

The presence of political extremist or criminal groups is another social factor that can be taken into account in some countries. In Colombia, for example, FARC (Revolutionary Armed Forces of Colombia) is such a group. Extremists can impede the flow of relief supplies by stealing goods and stopping trucks. But their actions vary according to each group’s mission and strategy. In cases where they have strong local links they might actually assist in relief efforts, particularly if recognized organizations such as the United Nations and the Red Cross are involved.

Social influences like these affect important logistics decisions, including where to locate large distribution centers and how to deliver urgently needed supplies to end users. “And the flow of aid can change according to the nature of local conflicts and the way activist groups behave,” says Mejia.

The research team plans to complete the conceptual phase of the project in about six months, and if funding is in place, will begin collecting data in the field.

For more information on the project contact Christopher Meija at cmejia@logyca.org.

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