Evidence-Based Optimization in Humanitarian Logistics

Humanitarian crises like the Syrian war, Ebola, the earthquake in Haiti, the Indian Ocean tsunami, and the ongoing HIV epidemic prompt substantial demands for humanitarian aid. Logistics plays a key role in aid delivery and represents a major cost category for humanitarian organizations. Cost-effectiveness of humanitarian aid is therefore strongly affected by the way logistics resources are utilized.

Optimizing logistics has long been at the core of operations research: the discipline that explores the use of advanced analytical methods to improve decision making. This thesis discusses how such methods can guide policy and decision making in the humanitarian sector. Next to a discussion on their potential role in supporting humanitarian logistics in general, the thesis presents advanced models and methods to analyze three specific questions: How should humanitarian organizations plan their field operations? How to design a network of clinics that provide healthcare services to African truck drivers? How to deploy mobile teams that screen for infectious disease outbreaks? We specifically explore how best available evidence can be used to link such decisions to impact on beneficiaries, i.e., how to enable evidence-based optimization in humanitarian logistics.