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The study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it considers the criteria to be evaluated for warehouse location selection. It is aimed to determine a warehouse location that will serve the disaster victims most efficiently in case of a disaster by making an application for the province of Izmir, where a massive earthquake hit in 2020.

The paper proposes a fuzzy best–worst method to evaluate the alternative locations for the warehouse. The method considers the linguistic evaluations of the decision-makers and provides an advantage in terms of comparison consistency. The alternatives were identified through interviews and discussions with a group of experts in the fields of humanitarian aid and disaster relief operations. The group consists of academics and a vice-governor, who had worked in Izmir. The results of a previously conducted questionnaire were also used in determining these locations.

It is shown how the method will be applied to this problem, and the most effective location for the disaster logistics warehouse in Izmir has been determined.

This study contributes to disaster preparedness and brings a solution to the organization of the logistics services in Izmir.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

This study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.

This paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.

The findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.

The researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

Due to the unknown location, size and timing of disasters, the rapid response required by humanitarian operations (HO) faces high uncertainty and limited time to raise funds. These harsh realities make HO challenging. This study aims to systematically capture the complex dynamic relationships between operations in humanitarian settings.

To achieve this goal, the authors undertook a systematic review of the extant academic literature linking HO to system dynamics (SD) simulation.

The research reviews 88 papers to propose a taxonomy of different topics covered in the literature; a framework represented through a causal loop diagram (CLD) to summarise the taxonomy, offering a view of operational activities and their linkages before and after disasters; and a research agenda for future research avenues.

As the authors provide an adequate representation of reality, the findings can help decision makers understand the problems faced in HO and make more effective decisions.

While other reviews on the application of SD in HO have focused on specific subjects, the current research presents a broad view, summarising the main results of a comprehensive CLD.

Conflict and violence are the main drivers of globally escalating humanitarian needs. Local grassroots initiatives are pivotal in distributing humanitarian supplies in the acute response phase until more established humanitarian aid organizations can enter. Nevertheless, scant research exists regarding the role of grassroots associations in providing humanitarian assistance during a military conflict. The purpose of this paper is to understand the role of grassroots associations and identify important themes for effective operations.

This paper adopts a case-study approach of three Ukrainian grassroots associations that began operating in the immediate days of the full-scale invasion of Ukraine. The findings are based on analyzing primary sources, including interviews with Ukrainian volunteers, and are supported by secondary sources.

Grassroots associations have local contacts and a contextual understanding of population needs and can respond more rapidly and effectively than large intergovernmental agencies. Four critical themes regarding the operations of grassroots associations emerged: information management, inventory management, coordination and performance measurement. Grassroots humanitarian response operations during conflict are challenged by personal security risks, the unpredictability of unsolicited supplies, emerging volunteer roles, dynamic transportation routes and shifting demands.

Grassroots responses are central to humanitarian responses during the acute phase of a military conflict. By examining the operations of grassroots associations in the early months of the 2022 war in Ukraine, the authors provide a unique perspective on humanitarian logistics. Nonetheless, more inclusive models of humanitarian responses are needed to harness the capacities and resilience of grassroots operations in practice.

The purpose of this study is to review the role of knowledge management (KM) in disaster management and crisis. Disaster causes many detrimental impacts on human lives through loss of life and damage to properties. KM has been shown to dampen the impact of the disaster on the utilization of knowledge among agencies involved and the local communities impacted by disasters.

Through a bibliometric methodology (co-citation, bibliographic coupling and co-word analysis), this study presents significant themes in the past, current and future predictions on the role of KM in disaster management. In this review paper, 437 publications were retrieved from the Web of Science and analyzed through VOSviewer software to visualize and explore the knowledge map on the subject domain.

Findings suggest that the significant themes derived are centralized to disaster preparedness during disaster and disaster postrecovery. This review presents a state-of-art bibliometric analysis of the crucial role of KM in building networks and interconnection among relevant players and stakeholders involved in disaster management.

The main implication of this study is how the authorities, stakeholders and local community can integrate the KM system within the three stages of disasters and the crucial role of technologies and social media in facilitating disaster management.

To the best of the authors’ knowledge, this is the first study to present a bibliometric analysis in mapping KM’s past, present and future trends in disaster management.

This conversation highlights the need to rethink how we approach disaster risk reduction in different South Asian contexts.

This paper is based on the webinar held as part of Asia Week at the University of Copenhagen which was organised by Asian Dynamics Initiative and Copenhagen Centre for Disaster Research on the September 12, 2023.

The prominent themes emerging from this conversation represents hybridity, self-rule and self-recovery. Along with this we suggest a fundamental turn to ensuring hope, solidarity and empathy is part of a post-colonial future.

The conversation contributes to the ongoing discussions on moving away from colonial practices in disaster risk reduction and disaster studies broadly.

To assess which partnerships were most critical during the recovery planning process following Hurricanes Maria and Irma. We discuss the roles and impact of different types of partners, barriers and facilitators to partnerships and lessons in collaboration during the development of the economic and disaster recovery plan for Puerto Rico.

The Homeland Security Operational Analysis Center (HSOAC) was tasked with assisting the Puerto Rican government with an assessment of damages from Hurricanes Maria and Irma and the development of the Recovery Plan. During the process, a small team compiled and coded a database of meetings with non-HSOAC partners. The team was divided into sector teams that mirrored FEMA’s Recovery Support Functions. Each sector completed two surveys identifying high impact partners and their roles and contributions, as well as barriers and facilitators to partnerships.

A total of 1,382 engagements were recorded across all sectors over seven months. The most frequently identified high impact partners were federal and Puerto Rican governmental organizations partners. NGOs and nonprofits were noted as key partners in obtaining community perspective. Sector teams cited a lack of trust and difficulty identifying partners as barriers to partner engagement. Given the expedited nature of disaster response, establishing partnerships before disasters occur may help facilitate community input. Early networking, increased transparency and defining roles and responsibilities may increase trust and effectiveness among partnerships.

To our knowledge, this is one of the few studies that quantifies and illustrates the partnerships formed and their contributions during recovery planning, and lessons learned.

This study aims to deepen the understanding of the challenges and implications entailed by deploying mobile clinics in conflict zones to reach populations affected by violence and cut off from health-care services.

This research combines an integrated literature review and an instrumental case study. The literature review comprises two targeted reviews to provide insights: one on conflict zones and one on mobile clinics. The case study describes the process and challenges faced throughout a mobile clinic deployment during and after the Iraq War. The data was gathered using mixed methods over a two-year period (2017–2018).

Armed conflicts directly impact the populations’ health and access to health care. Mobile clinic deployments are often used and recommended to provide health-care access to vulnerable populations cut off from health-care services. However, there is a dearth of peer-reviewed literature documenting decision support tools for mobile clinic deployments.

This study highlights the gaps in the literature and provides direction for future research to support the development of valuable insights and decision support tools for practitioners.