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This study, a practice forum article, aims to presents the lessons learned and the development of a discrete event simulation model to support the funerary system management of São Paulo City, Brazil, during the COVID-19 pandemic.

A discrete event simulation model was developed by the authors as soon as the pandemic affected the city of São Paulo, Brazil. Based on the model, several scenarios with varying minimum, median and peak demands (i.e. the number of deaths) were tested and evaluated. The lessons learned from the scenario analysis and implementation of the decision-making of the city government of São Paulo are discussed in this article.

The lessons learned about the coordination, inventory management and other operational characteristics in funerary logistics during the pandemic are shared with a model, which quantifies the demand for vehicles, coffins, graves and teams in the cemeteries in different simulated scenarios.

The São Paulo State Civil Defense used this information during the pandemic to prepare the funerary system of the municipality.

The study presents methods to mitigate the sanitary, environmental and psychosocial problems related to the funerary system.

Studies on funerary systems are scarce. This study presents the results that supported the dimensioning of the funerary system during the pandemic and operational lessons about the logistics to support decision-making in future events.

This paper aims to present the internalization process by which Venezuelan migrants and refugees are resettled. Using system dynamics, the authors model a Brazilian humanitarian operation (“Acolhida” – Welcome), simulate the internalization process, propose policies and provide lessons learned for future migratory operations.

Using system dynamics simulation, the authors use Acolhida Operation’s historical data to recreate the reception and resettlement process of Venezuelan migrants and refugees. The authors identify the main bottlenecks in the system and propose policies to respond to scenarios according to the number of internalization vacancies, that is, available places in Brazil where migrants and refugees can be resettled. Finally, based on interviews with former decision-makers, the model represents a first attempt to convert the pressure of public opinion on authorities into temporary shelters as a way of reducing the number of unassisted people.

The results confirm that internalization vacancies are the main constraint when resettling Venezuelan migrants and refugees. Had the internalization program been promoted since the operation’s beginning, there would have been fewer unassisted people in Roraima and fewer shelters. The pressure-converting mechanism presented in this study, although incipient, constitutes a first attempt to support decision-makers in determining when to build temporary shelters.

This study can be useful to public authorities and humanitarian organizations when developing policies to enhance resettlement in migratory crises. In Acolhida’s case, the internalization program should continue to be the operation’s priority and can be enhanced by investing more resources to create internalization vacancies while maintaining logistical capacities.

The authors suggest policies to improve the Acolhida internalization program: give more people the choice to relocate in other cities, increase turnover in shelters and provide a more efficient and effective response to Venezuelan migration in Roraima.

Although a number of studies have applied system dynamics to humanitarian operations, few models have focused on migratory emergencies, such as those occurring in northern Brazil. The model is applied to the largest humanitarian operation carried out in the Brazilian territory and provides decision-makers with valuable insights and alternatives for better implementation in the future. Furthermore, this study narrows the gap between the social sciences and modeling and simulation techniques by proposing ways of predicting migratory implications in the construction of shelters and resettlement policies.

The purpose of this paper is to present a literature review of humanitarian logistics (HL) that aims to identify trends and suggest some directions for future research.

This conceptual paper develops a research framework for literature review through qualitative and quantitative content analysis. First, previous literature reviews in HL are updated and detailed. Then, seven classification criteria are added to earlier ones in order to advance the literature analysis.

The conclusions identify some literature gaps and research opportunities. The main conclusions are the need for more studies into the disaster recovery phase and the need for closer relationships between academia and humanitarian organizations to increase the number of applied research.

The literature is limited to academic peer-reviewed journals because of their academic relevance, accessibility, and ease of searching.

Help potential researchers to set up a research agenda for future work.

Reinforce earlier calls to increase truly applied research and improve social impact of the field.

In total, 228 papers that were published in the HL area are reviewed, giving rise to the most extensive literature review in this area. New dimensions for literature review in HL are proposed, which give some new insights into potential research directions.

This paper aims to design a vulnerability assessment model considering the multidimensional and systematic approach to disaster risk and vulnerability. This model serves to both risk mitigation and disaster preparedness phases of humanitarian logistics.

A survey of 27,218 households in Pueblo Rico and Dosquebradas was conducted to obtain information about disaster risk for landslides, floods and collapses. We adopted a cross entropy-based approach for the measure of disaster vulnerability (Kullback–Leibler divergence), and a maximum-entropy estimation for the reconstruction of risk a priori categorization (logistic regression). The capabilities approach of Sen supported theoretically our multidimensional assessment of disaster vulnerability.

Disaster vulnerability is shaped by economic, such as physical attributes of households, and health indicators, which are in specific morbidity indicators that seem to affect vulnerability outputs. Vulnerability is heterogeneous between communities/districts according to formal comparisons of Kullback–Leibler divergence. Nor social dimension, neither chronic illness indicators seem to shape vulnerability, at least for Pueblo Rico and Dosquebradas.

The results need a qualitative or case study validation at the community/district level.

We discuss how risk mitigation policies and disaster preparedness strategies can be driven by empirical results. For example, the type of stock to preposition can vary according to the disaster or the kind of alternative policies that can be formulated on the basis of the strong relationship between morbidity and disaster risk.

Entropy-based metrics are not widely used in humanitarian logistics literature, as well as empirical data-driven techniques.