Humanitarian Relief Logistic Networks with Consideration of Pre- and Post-disaster Management

Swiss partners

• HES-SO, HEG - Genève: Naoufel Cheikhrouhou (main applicant)

Partners in the MENA region

• Institut des hautes études commerciales (IHEC) de Carthage, Tunisie: Jouhaina Chaouachi Siala
• University of Tunis El Manar (UTM): Hejer Khlif Hachicha

Presentation of the project

Historically, disasters have been seen as exceptional events, but their frequency and severity have been on the rise due to the rapid growth of population, increased human concentration in hazardous areas and fast climate change. These factors impact today (and will continue) both Tunisia and Switzerland, which imply the needs for a good preparation for disaster management and relief operations. Failing to account for the increased uncertainties in such conditions can lead to significant delays in relief distribution. It has become imperative to enhance the quality of relief operations, and designing effective Disaster Logistic Networks (DLNs) is a crucial aspect.

This collaborative research project aims to bridge the gap between theoretical models and practical applications with a focus on developing optimization models for pre- and post-disaster DLNs. These models will incorporate prevailing risk factors to tackle the challenges faced in real-world scenarios. By incorporating risk-averse networks, strategic plans can be devised to optimize service levels and response times to affected areas and people while considering the growing pressure of cost efficiency for humanitarian organizations. The research will introduce a novel decomposition model to solve the optimization problems using realistic data models.

The literature review reveals that current mathematical models and solution approaches need improvements to generate more practical and realistic results. The innovation in this research lies in two key areas. Firstly, both pre- and post-disaster factors, including casualty transportation to healthcare facilities, will be considered while constructing the DLN models.
Secondly, risk measures for disasters impacting the DLN will be incorporated through various scenarios encompassing both pre- and post-disaster elements. This comprehensive approach will enable the identification of the most critical factors in the system and lead to optimal solutions concerning relief quality, response time, and cost efficiency.

The outcomes of this research will provide valuable insights for researchers in the area of Operations research and Logistics as well as the humanitarian practitioners involved in relief operations in Tunisia and Switzerland. Moreover, it will support decision-making in the design and management of pre- and post-disaster logistics networks, making them more effective and responsive in times of crisis.