OR Routing


Thursday 25.11. at 11:40-12:55
Chair: Maria Besiou, Kühne Logistics University, Germany


Preparedness in Humanitarian Supply Chains – Exploring the Benefits of Investments in Different Operational Settings

Jonas Stumpf, HELP Logistics, Switzerland
Maria Besiou, Kuehne Logistics University, Germany
Tina Wakolbinger, Vienna University of Economics and Business, Austria

Keywords:
Preparedness
Humanitarian Supply Chains
Operational Settings
System Dynamics
Return-on-Investment

Investing in supply chain preparedness is considered a powerful trigger to improve the operational performance of humanitarian
actors, enabling them to provide more assistance with fewer resources. However, little research exists on the actual impact of
preparedness investments in the humanitarian space. To build strong business cases, preparedness investment planning should
take into account the different operational realities of humanitarian actors. Using system dynamics methodology, we model the
humanitarian supply chain from the perspective of a centralized setting with strong capacities at global hubs, a decentralized
setting with strong presence in the countries, and a hybrid setting with response capacities at global as well as country level. Based on data collected through five case studies we find that the decentralized setting is less costly for low value items, generates more local social impact but has also longer lead times for medium to large scale disasters than the centralized and the hybrid setting. When analyzing the impact of preparedness investments with respect to cost savings, lead-time reductions and social impact on local population, we conclude that preparedness investments pay off for all settings in countries with high disaster risk profile. Our results show that decentralized settings have the largest potential across all performance metrics. However, the models also demonstrate that decentralized settings are most vulnerable to major shocks such as the COVID19 pandemic.


A multi-layer network approach to model the situation before and after the occurrence of humanitarian crisis

Aurelie Charles, Lyon 2 University – DISP, France
Chantal Cherifi, Lyon 2 University – DISP, France
Guillaume Bouleux, INSA Lyon, France

Keywords:
Complex networks
Multi-layer approach
Disaster response
Humanitarian logistics

The complexity of the situation during a humanitarian crisis makes its overall vision difficult. The response to crisis involves many
heterogeneous entities, including a high diversity of field actors (international organizations, local NGOs, army), infrastructures
(air, health, water, road, telecom), transportation means (vehicles), affected populations and available resources. Data available in real time is often incomplete and dynamic. For example, some roads or other logistical infrastructure may be destroyed by the
disaster and need to be fixed to facilitate humanitarian response operations. Collecting and updating data takes time and is
therefore not always considered as a priority, especially in this specific context where resources are scare and population needs
are critical. Despite their high impact, both in lives and in dollar, few paper actually studied humanitarian crisis with a complex
network approach. While all provide elements to better analyze the situation, they all focus on specific, limited scopes. The complexity and the overall understanding of the situation is therefore limited. This is the limitation we focus on: providing an exhaustive view of the situation, combining various elements. We propose to model the response to a humanitarian crisis with a
multi-layer network approach. The model can provide useful insights during operations, to better understand what is going on,
and simulate various response scenarios to choose the best way to utilize the resources available. It can also be used in the
preparedness phase, to propose ways to improve the resilience of the actual network.


Multi-Period Capacitated Mobile Facility Location Problem with Mobile Demand: Aiding Refugees on the Move

Amirreza Pashapou,r Koc University, Turkey
Dilek Günnec, Ozyegin University, Turkey
Sibel Salman, Koc University, Turkey
Eda Yücel, TOBB University of Economics and Technology, Turkey

Keywords:
Humanitarian logistics
Capacitated mobile facility location
En route refugees
Mobile demand
Benders decomposition
Matheuristic

In this paper, we focus on humanitarian organizations that provide aid to refugees, who are on their journey to cross borders, by
means of mobile facilities. We study the problem of optimizing the number and routes of the mobile facilities, in addition to the
location and timing of the aid provisions over a planning horizon. The problem is represented on a network such that, in each
period, multiple refugee groups proceed in their predetermined paths. For continuity of service, each refugee group should be
served at least once every fixed number of consecutive periods via the capacitated mobile facilities. We aim to minimize the total
cost that comprises fixed costs, service provision costs, and facility relocation costs, while ensuring the service continuity requirement. We formulate a mixed integer linear programming model for this multi-period capacitated mobile facility location problem with mobile demand. We develop a matheuristic and an accelerated Benders decomposition algorithm as an exact
solution method. The proposed model and solution methods are tested through instances we extracted from the 2020-2021 Honduras migration crisis.


Routing decisions and network recovery in humanitarian operations

Lorena S. Reyes Rubiano, Otto-Von-Guericke University Magdeburg and
Universidad de La Sabana, Germany
Elyn Solano Charris, Universidad de La Sabana, Colombia

Keywords:
Labeled network
Network accessibility
Network connectivity
Routing decisions
Recovery decisions
Victims accessibility

Disasters are unexpected events characterized by a negative impact on lives lost and high logistical costs during and after the
disaster. The impact of a disaster can be estimated by analyzing the affected population and the magnitude of the disaster. We are interested in determining strategies to deal with the impact of a disaster on accessibility to the affected people. Disasters generate collapsed bridges or blocked roads which hinder the relief of the affected population. We propose a labeled network to determine the relevance of each connection in terms of accessibility to the affected people. The deployment of humanitarian aid is mainly done through the ground, trying to reach the most vulnerable population. In general, routing decisions are based on finding the shortest route to reach the most vulnerable people. However, this criterion could lead the humanitarian aid to blind spots and visit areas that do not reach victims. We propose to analyze the affected area as a network that when a disaster occurs, some arcs are missed (blocked streets), and some nodes are partially or entirely isolated from the network. Our analysis is based on the calculation of a labeled network. Each arc has a label representing its importance in terms of accessibility to the network and the victims. The importance of each arc is a measure that involves attributes of the network structure (node degree, network connectivity, spanning tree, and cycles, among others) and the location of the victims. This is very useful in scenarios where there is no information on the affected area. As missing arcs are detected, the label of all arcs in the network should change, indicating that some arcs are no longer important while the importance of other arcs increases, as long as these arcs are leading to reach a victim. Using this labeled network of the area affected, we can make routing decisions as to which arcs should be visited first and which arcs should be recovered (reconstructed streets) to improve accessibility to the affected population. We work in the experimental phase with networks of different sizes and structures.