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Despite a growing body of research on the problem of increasing disaster preparedness by pre-positioning relief supplies at strategic locations, there is a lack of a benchmark set of problem instances that hinders thorough hypotheses testing, sensitivity analysis, model validation or solution procedure evaluation. The purpose of this paper is to address this issue by constructing a public library of diverse pre-positioning problem instances.

By carefully manipulating some of the instance parameters, the authors generated 30 case studies that were inspired by four instances collected from the literature that focus on disasters of different type and scale that occurred in different parts of the world. In addition, the authors developed a tool to algorithmically generate arbitrarily many diverse random instances of any size.

For many purposes, the problem library can eliminate or reduce the time-consuming process of data collection, conversion, digitization, calibration and validation, while simultaneously increasing the statistical significance of research results and allowing comparison with different works in the literature.

The case studies are inspired by only four disasters, and some of the instance parameters are defined in a reasonable, albeit arbitrary way. The instances are also limited by the underlying problem assumptions.

The instances provide a more comprehensive and balanced experimental setting (compared to a single case study) that can be used to study the pre-positioning and related problems, or derive managerial implications that can directly benefit the practitioners.

The instances can be used to derive practical guidelines that humanitarian workers can use on the ground to better plan their pre-positioning strategies and therefore minimize human suffering.

The case studies and the random instance generator are made publicly available to foster further research on the problem of pre-positioning relief supplies and humanitarian logistics in general.

As the intensity of natural disasters increases, there is a need to develop policies and procedures to assist various agencies with moving aid to affected areas. One of the biggest limitations to this process is damage to transportation networks, in particular waterways. To keep waterways safe, aids to navigation (ATONs) are placed in various areas to guide mariners and ships to their final destination. If the ATONs are damaged, then the waterways are left unsafe, making it difficult to move supplies and recover from a disaster. The aim of this paper is to explore the effectiveness of pre‐positioning strategies for port recovery in response to a natural disaster.

A stochastic facility location model is presented to determine where teams and commodities should be pre‐positioned in order to maximize the number of ATONs repaired, given a constraint on response time. The first stage decisions focus on determining the location of resources. The second stage decisions consist of the distribution of supplies and teams to affected areas.

Results show the benefit of pre‐positioning and the value of coordination toward the responsiveness of restoring waterways. Furthermore, the relationship between resources, repair time, and response is characterized.

There has been extensive work addressing pre‐positioning as it relates to responding to the needs of populations affected by disasters. However, little has been done to explore pre‐positioning in the context of business recovery from severe weather events.

The increasing number of natural disasters has increased the attention on emergency plans aimed at providing fast support to affected communities. In this context, inventory pre-positioning management, which involves positioning the materials required to meet the affected community's needs early, has been increasingly acknowledged, but many challenges persist. The purpose of the paper is to provide a decision support system for the optimal quantification and location of humanitarian aid, trying to enhance and extend the existing literature on this topic.

The paper develops a numerical model for inventory pre-positioning of humanitarian aid to reduce both emergency response times and costs connected to goods procurement for seismic events. By examining the characteristics of the territory and the affected population, the model defines the optimal stock levels for four basic need items (hygienic sanitary kits, beds, blankets and camp tents) to be pre-allocated in the territory.

The model was validated using data obtained from the two severe earthquakes that occurred in Italy. The case study showed how the simulated outputs differ from the real case data and the economic benefits of adopting inventory pre-positioning considering the cost reductions (purchase, storage, transport and fulfilment of requirements).

The proposed decision support system allows the pre-positioning of emergency supplies in local areas in order to reduce response times and increase the speed of intervention in the event of seismic events, exploiting the advantages of a simulation model. Numerical and graphical results can easily support improvements in humanitarian logistics, providing those affected with rapid, cost-effective and better-adapted responses.

The purpose of this paper is to investigate the use of freight containers to store relief items instead of operating a permanent warehouse building.

A mathematical model is developed to determine the location and quantity of containers as well as the type and amount of relief supplies to store in order to investigate the practicality of using freight containers for storage. The model is tested using earthquake risk data, estimates of population under risk, and the distances between cities. An experimental study is performed using Turkish Prime Ministry Disaster and Emergency Management Presidency (abbreviated as AFAD in Turkish) data for total number of relief supplies.

Considering the earthquake risk of possible locations, the results of the study indicate the target locations for containers. The idea of using containers as storage facilities helped beneficiaries to be reached within a short distance and in an efficient way.

The presented model is not implemented in real life disaster relief operations even if it is tested with real earthquake risk, demand and distance data.

To apply this model in practice, the container locations within cities should be determined and managerial operations such as maintenance, environmental, and security planning have to be considered.

This study presents the first analysis of three sub-topics’ intersection: warehousing, pre-positioning in disaster relief, and containerization. To the best of authors’ knowledge, containers have not been considered for storage of relief items in humanitarian logistics before.

Humanitarian supply chains (HSCs) by their very nature require urgent reaction to unforeseeable needs, making it difficult to properly plan for the support of actual demands. As such, integrating sustainability into traditional HSC practices continues to present a challenge to governments, nongovernmental organizations (NGOs) and other humanitarian-related agencies. This study focuses on identifying and categorizing the leading enablers to green humanitarian supply chains (GHSCs) and proposes a model for improving the responsiveness based upon a fuzzy total interpretive structural modelling approach.

Total interpretive structural modelling (TISM) uses group decision-making to identify contextual relationships among each pair of enablers and elucidates the nature of each underlying relationship. The fuzzy TISM shows the level of strength (very high influence, high influence, low influence and very low influence) of each enabler in relation to other enablers, which can help to inform management decision-making.

GHSC management requires strategic planning of inventory and logistics management. The importance of collaborative relationship building with HSC partners for developing capability and the effective use of available resources are keys to success. These improved relationships also help to promote postponement and similar speculation-based logistics strategies, as well as advanced purchasing and pre-positioning strategies. Finally, the speed and quality of response is found to be the top enabler in GHSC management.

One noted shortcoming of the chosen research method is its reliance on subjective expert judgement. However, collecting judgements is at the basis of many research methods, and the research team took utmost care throughout the research process to allay biases. Future empirical research can further examine the relationships suggested herein. Managers can use the model developed in this research to consider impactful ways to design and execute sustainable HSCs.

To the best of the authors' knowledge, this is a novel attempt to identify enablers to GHSC management. Secondly, the research team has used an advanced methodology (fuzzy TISM) to develop the contextual inter-relationships among the enablers which has not been used earlier in this direction before and thus advances the GHSC literature.

This study proposed a mathematical model for decision-making in the pre- and post-disaster phases. This research aims to develop a mathematical model for three important fields in the context of humanitarian logistics; stock prepositioning, facility location and evacuation planning in the humanitarian supply chain (HSC) network design.

This study applied three optimization techniques; classical approach (CA), pattern search algorithm (PSA) and Genetic Algorithm (GA) to solve the proposed mathematical model. The proposed mathematical model attempts to minimize the total relief items supply chain cost and evacuation chain cost of the HSC. A real case study of cyclone Fani, 2019 in Orissa, India is applied to validate the proposed mathematical model and to show the performance of the model.

The results demonstrate that heuristic approach; PSA performs better and optimal solutions are obtained in almost all the cases as compared to the GA and CA.

This study is limited to deterministic demands in the affected regions, and different scenarios of the disaster events are not considered.

The finding reveals that the proposed model can help the humanitarian stakeholders in making decisions on facility location, relief distribution and evacuation planning in disaster relief operations.

The results of this study may offer managerial insights to practitioners and humanitarian logisticians who are engaged in HSC implementation.

The purpose of this work is the development of a structured case study design process for developing case studies in humanitarian logistics, in particular for short-term predictable disaster situations like floods and hurricanes. Moreover, useful public sources are presented in order to enable researchers to find relevant data for their case studies more easily.

A structured framework for case study design is set up, splitting the process into different steps and phases.

The framework is applied to an illustrative example, where case studies with different numbers and levels of detail of scenarios are designed based on the three-day forecast for hurricane Harvey in 2017. The corresponding solutions demonstrate the relevance of using as much forecast information as possible in case study building, and in particular in scenario design, in order to get useful and appropriate results.

The case study design process is mostly suitable for short-term predictable disasters, but can also be adapted to other types of disasters. The process has been applied to one specific hurricane here which serves as an example.

Also for practitioners, the results of this work are highly relevant, as constructing realistic cases using real data will lead to more useful results. Moreover, it is taken into account in the case study design process that relief agencies are regularly confronted with disasters in certain areas and hence need to define the basic planning situation and parameters “once and for all” and on a long-term basis, whereas disaster specific data from forecasts are only available within a short time frame.

The new design process can be applied by researchers as well as practitioners, and the publicly available data sources will be useful to the community.

Over the last decade, temporary supply chains (TSCs) have become a well-recognized logistics model. In TSCs, supply chain members are organized for an ad hoc project; they pool resources in order to make the project successful. Although it might be perceived that TSCs are unstable due to their temporary nature, this paper aims to discuss how TSCs can be managed so as to be both stable and agile, while achieving the stated objectives; since the stability-agility context could be really challenging in humanitarian and peacekeeping supply chains, this is the one that has been selected.

The authors reviewed the literature, research reports and electronic documents on humanitarian and peacekeeping supply chains, to understand the main challenges in terms of managerial and social impacts of logistical operations in a disaster context.

The disaster context is very peculiar, since it requires tremendous agility when a natural or man-made catastrophe hits, so that as many lives as possible can be saved and that the situation could get back rapidly to a relatively normal level. The paper shows that TSCs require an advanced level of time and organizational stability of the human and material resources involved in order to be highly flexible. In other words, an efficient TSC relies on “anticipated responsiveness”, a major managerial challenge in the years to come.

The paper clarifies the management of humanitarian and peacekeeping supply chains and identifies the importance of anticipation capability to improve logistical responsiveness.

The purpose of this paper is to improve disaster management models, have an optimal distribution of assets, reduce human suffering in a crisis and find a good solution for warehouse locations, distribution points, inventory levels and costs, considering the uncertainty of a wide range of variables, to serve as a support model for decision making in real situations.

A model is developed based on the recent models. It includes structured and non-structured data (historical knowledge) from a humanitarian perspective. This model considers the uncertainty in a landslide and flood area and it is applied in a representative Peruvian city.

The proposed model can be used to determine humanitarian aid supply and its distribution with uncertainty, regarding the affected population and its resilience. This model presents a different point of view from the efficiency of the logistics perspective, to identify the level of trust between all the stakeholders (public, private and academic). The finding provides a new insight in disaster management to cover the gap between applied research and human behavior in crisis.

In this study the access of reliable information is limited.

This paper provides an operation model with uncertainty in a humanitarian crisis and a decision-making tool with some recommendation for further public policies.

This study presents a model for decision makers in a low-income zone and highlights the importance of preparedness in the humanitarian system. This paper expands the discussion of how the mathematical models and human behaviors interact with different perspectives in a humanitarian crisis.

The purpose of this paper is to provide a decision support framework for locations identification to address network design in the domain of disaster relief supply chains. The solution approach is then applied to a real-life case about Indonesia.

An approach integrating geographic information system technology and fuzzy analytical hierarchy process has been used.

For the Indonesian case, distribution centers should be located in Pekanbaru, Surabaya, Banjarmasin, Ambon, Timika, and Manado.

The main limitation of this work is that facilities being sited are incapacitated. Inclusion of constraints over capacity would elevate the framework to a further level of sophistication, enabling virtual pool of inventory that can be used to adsorb fluctuation in the demand due to disasters.

The use case provided in this paper shows a practical example of applicability for the proposed framework. This study is able to support worldwide decision makers facing challenges related with disaster relief chains resilience. In order to achieve efficiency and effectiveness in relief operations, strategic logistics planning in preparedness is key. Hence, initiatives in disaster preparedness should be enhanced.

It adds value to the previous literature on humanitarian logistics by providing a real-life case study as use case for the proposed methodology. It can guide decision makers in designing resilient humanitarian response, worldwide. Moreover, a combination of recommendations from humanitarian logistics practitioners with established models in facility location sciences provides an interdisciplinary solution to this complex exercise.