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Record W4406935154 · doi:10.1108/jhlscm-02-2025-164

Guest editorial: Emergency response logistics

2025· editorial· en· W4406935154 on OpenAlexaff
Tobias Andersson Granberg, Hossein Baharmand

Bibliographic record

VenueJournal of Humanitarian Logistics and Supply Chain Management · 2025
Typeeditorial
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsEngineering Link (Canada)
Fundersnot available
KeywordsEmergency responseDisaster responseBusinessMedical emergencyEmergency managementMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

Effective, efficient and timely emergency logistics management is essential for saving lives and reducing suffering. This is true for all four phases often used to define emergency management, i.e. mitigation, preparedness, response and recovery. However, it may be argued that it is especially important during the response phase, in the immediate aftermath of an emergency, when a short response time often is crucial to minimize the negative consequences. Thus, the continuous improvement of emergency response logistics should be prioritized both in academia and in practice.As our world becomes more interconnected and vulnerable to a range of calamities, the challenges that hinder efficient emergency response logistics have become increasingly multifaceted. The impact of emergencies, the rapidity with which emergency response teams must act, the need for data-driven decision-making in complex environments and the essential role of various actors, from first responders to volunteers, all underscore the importance of advancing our understanding and methodologies in this realm.There are several critical challenges to emergency response logistics: Recent technological advancements could provide promising solutions to challenges within emergency response logistics. For instance, the utilization of modern vehicles and equipment, including unmanned aerial, surface or ground vehicles, robots, exoskeletons, extended reality gear, sensors and communication networks, can facilitate the response process, while decreasing the risk for rescue workers. Moreover, integrating artificial intelligence (AI), big data analytics, natural language processing and optimization methodologies in emergency response logistics could support increasing efficiency and effectiveness of operations. One example is coupling AI technology with unmanned aerial vehicles in search and rescue operations to process live data obtained from sensors for dynamic route planning and, thereby, optimizing the response.To reap the potential benefits of technological advancements in emergency response logistics, there is a need for evidence-based research on how new technologies could be integrated within existing resources and work practices. It is important that the technologies for emergency response logistics are not developed in isolation from practice so that the requirements of end users (e.g. first responders) and their operational contexts are carefully considered in the design and development phases. Complementing training of response personnel and effective change management should be planned. It is also important to develop means for evaluation of the impact, enabling an objective assessment of new resources and practices.Apart from technological advancements, increased in- and cross-sector collaboration can also improve the efficiency and effectiveness of emergency response logistics. This may include sharing resources (e.g. equipment and information systems), systematic involvement of volunteers and facilitating their integration and collaboration with responders. Resources from the private sector can represent critical assets in emergencies, and preestablished public-private partnerships will speed up access to such resources and assets.This special issue aims at creating a confluence of diverse research that targets emergency response logistics. Guest editors for the special issue belong to two top research centers in Sweden and Norway, which focus on crisis and emergency management: Center for Advanced Research in Emergency Response (CARER – Sweden) and Centre for Integrated Emergency Management (CIEM – Norway). Members from the centers have a long history of collaboration in research, teaching and service to academia. This special issue is an outcome of another successful collaboration between the two centers.The guest editors received 18 manuscripts in total to be considered for the special issue. After careful assessment regarding both scope and quality, 10 manuscripts were sent out for external review. Finally, four papers were accepted.The four selected papers showcase both the breadth and depth of the research topic. They span over emergency types and extent from daily emergencies like small fires and cardiac arrest cases, to earthquakes and prolonged droughts. In one case, the focus is to improve the first response time, counting even the seconds, while in another, still focusing on responding to an emergency, a slow onset and the prolonged emergency timeline makes it necessary to develop a robust and cost-effective response chain. Methods used include case studies, interviews, workshops and a survey, as well as optimization modeling and utilization of geographical information systems, emphasizing the need for multidisciplinary research within emergency logistics. All four papers also study real-world problem, collecting primary data or using existing data connected to real emergency events. This is essential for result validity and quick utilization of the results in practice.In “Medical staff planning for field hospital deployments: the START hospital,” Martin-Campo, Ortuño and Ruiz-Gonzalez study the problem of deploying field hospitals as part of the response to a large-scale emergency. Using a multi-criteria optimization model, they match skills and preferences by medical personnel, to the requirements of the emergency, planning both personnel schedules and flights to get them to the field hospital. They present a case study based on the deployment of a START hospital in Turkey, in response to the earthquakes in February 2023, and use data from this to validate the model. Both data and code implementation of the model are available for download, to facilitate quick utilization of the work, and enable continuous improvements. The paper nicely illustrates many of the resource management challenges associated with emergency response logistics, and how optimization modeling can be used to address some of them.Also using optimization modeling to address a resource management problem, but for a different type of emergency is “An integrated model approach for disaster impact reduction: lessons from a slow onset disaster in Chile” by Gomez-Schwartz, Castillo-Vergara, González and Espíndola Arellano. After many years of drought, the water reservoirs in the Coquimbo Region in Chile are running dry, which means that portable water must be transported to the affected population. While the focus here might not be to minimize the response time and the immediate saving of lives, the prolonged emergency state makes it equally important to ensure efficient and effective logistics management. The authors develop a route planning model for water distribution trucks, and collect field data encompassing 5,519 households in 15 municipalities in the Coquimbo Region. The paper highlights the challenges and the importance of proper data collection methods, for the practical usability of planning models. Here, each municipality had different ways of managing local transport operations, which means high variability in input data. Thus, the authors emphasize that “Collaboration between academic institutions, governments and local communities is essential.”Collaboration is also an important theme in “Digitalized co-production of emergency response: ICT-enabled dispatch and coordination of volunteers at the emergency site” by Pilemalm, Follin and Prytz, but here the emphasis is on collaboration between volunteers and professional emergency response organizations. The study looks closely at initiatives in Sweden where the municipal fire and rescue services recruit volunteers to provide a quick, but very basic, first response. The focus is on requirements for dispatch technology (e.g., an app for alerting the volunteers), and a design proposal is given. The paper effectively illustrates how some of the challenges regarding uncertainty, coordination and communication in emergency response logistics can be addressed, and also advocates the possibility of using the same system and resources for both frequent emergencies and large-scale emergency events.Shared use of resources is applicable for both frequent and larger emergencies, but perhaps most common for the latter. In “How to enhance company engagement in public-private emergency collaborations in the supply of essential goods,” Zienau, Lüttenberg, Wiens, Hansen, Diehlmann and Schultmann, study expectations and motivation for companies to participate in emergency response and crises management. Using a survey, they analyze responses from 398 German companies, finding, e.g. that many of the companies responding in the survey want to help, but only when an emergency has happened, and not with preventive actions. Still, there is interest in forming public-private emergency collaborations, especially if monetary compensation or relaxed regulations are offered. While the focus on the services offered was providing goods, transportation capacity, storage and planning services, it should be possible to adapt the results to services including first response and lifesaving measures, complementing volunteer first responders with company first responders.This special issue has exemplified some of the challenges and potential remedies existing within emergency response logistics. However, there are a multitude of future research directions. We elaborate on some of these directions in the following.The integration of technology plays a significant role in improving coordination efforts. Advanced software solutions that allow for real-time data tracking and resource management can significantly enhance communication among team members, ensuring that all parties are informed of the current situation on the ground. Furthermore, such tools enable simulations of various emergency scenarios, allowing teams to identify potential logistical bottlenecks and improve preparedness for disaster response. Moreover, the deployment of mobile applications for communication among volunteers and professional responders can enhance on-site collaboration, ensuring a rapid and organized response. These areas still need more evidence-based research that can support responders on the ground.Effective inventory management methodologies are crucial for maintaining adequate supplies during emergencies. These methodologies can help responders stockpile necessary resources, such as food and water, especially in regions prone to major incidents like tsunamis or flash floods. Moreover, addressing challenges related to the Location Allocation Problem and the Vehicle Routing Problem can streamline the process of resource allocation, ensuring that emergency supplies promptly reach the affected. Furthermore, queuing models can be used to optimize the flow of individuals in distribution points and healthcare facilities, particularly during large-scale emergencies such as pandemics. We believe that these areas need more research to address responders’ needs in emergency situations.Post-event reviews and lessons learned from previous emergencies should inform ongoing training and preparedness efforts. Emergency responders should prioritize evaluating their responses to past incidents to identify strengths and weaknesses, allowing for the refinement of future response strategies. Moreover, regular training sessions for both volunteers and professional responders will ensure that all parties are equipped with the skills and knowledge necessary to handle emergencies effectively.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.239
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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