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It is essential to provide drinking water to affected population directly after a disaster. The purpose of this paper is to develop an optimization methodology that helps in the distribution of drinking water in post-disaster situations.

The research was conducted on two phases: phase 1 aims at identifying an appropriate way to deliver drinking water to refugee camps from external sources, considering required drinking water quantities and four possible sources of water with respect to cost and risk assessments. Phase 2 investigates drinking water distribution within a refugee camp using covering models. The MCLP–optimal number of facilities model is proposed to ensure that the water is distributed and delivered to all individuals in a camp with minimum number of water storage tanks required. A control policy is proposed to ensure the fair distribution of water to all targeted individuals.

Al-Za’atari refugee camp, located in northeast of Jordan, was considered as the case study for this research. The result showed that the appropriate way to deliver water to the camp is by using tanker–trucks, and a minimum number of five tanks are required to distribute water to individuals inside the camp with respect to tank locations and the allocation of tank of each area.

The proposed methodology is essential in decision making for the distribution of drinking water in refugee camps in short-term needs. The model adds important value to the literature as the proposed problem has no solution in the literature before.

Refugee camps can be severely struck by pandemics, like potential COVID-19 outbreaks, due to high population densities and often only base-level medical infrastructure. Fast responding medical systems can help to avoid spikes in infections and death rates as they allow the prompt isolation and treatment of patients. At the same time, the normal demand for emergency medical services has to be dealt with as well. The overall goal of this study is the design of an emergency service system that is appropriate for both types of demand.

A spatial hypercube queuing model (HQM) is developed that uses queuing-theory methods to determine locations for emergency medical vehicles (also called servers). Therefore, a general optimization approach is applied, and subsequently, virus outbreaks at various locations of the study areas are simulated to analyze and evaluate the solution proposed. The derived performance metrics offer insights into the behavior of the proposed emergency service system during pandemic outbreaks. The Za'atari refugee camp in Jordan is used as a case study.

The derived locations of the emergency medical system (EMS) can handle all non-virus-related emergency demands. If additional demand due to virus outbreaks is considered, the system becomes largely congested. The HQM shows that the actual congestion is highly dependent on the overall amount of outbreaks and the corresponding case numbers per outbreak. Multiple outbreaks are much harder to handle even if their cumulative average case number is lower than for one singular outbreak. Additional servers can mitigate the described effects and lead to enhanced resilience in the case of virus outbreaks and better values in all considered performance metrics.

Some parameters that were assumed for simplification purposes as well as the overall model should be verified in future studies with the relevant designers of EMSs in refugee camps. Moreover, from a practitioners perspective, the application of the model requires, at least some, training and knowledge in the overall field of optimization and queuing theory.

The model can be applied to different data sets, e.g. refugee camps or temporary shelters. The optimization model, as well as the subsequent simulation, can be used collectively or independently. It can support decision-makers in the general location decision as well as for the simulation of stress-tests, like virus outbreaks in the camp area.

The study addresses the research gap in an optimization-based design of emergency service systems for refugee camps. The queuing theory-based approach allows the calculation of precise (expected) performance metrics for both the optimization process and the subsequent analysis of the system. Applied to pandemic outbreaks, it allows for the simulation of the behavior of the system during stress-tests and adds a further tool for designing resilient emergency service systems.

The purpose of this paper is to present the initial results of the Camp Performance Indicator (CPI) system to illustrate the importance of self-reliance of refugee camp dwellers with regard to infrastructure and service investments.

Data, derived from a field trip to Zaatari in autumn 2016 and thorough literature research, were taken to develop a new CPI system. The findings from the literature research were merged with available camp data to validate each other.

Self-reliance is a fundamental human right and anchored in the UN sustainable development goals. Yet, presented findings reveal that even in one of the most modern refugee camps in the world – Zaatari – the level of self-reliance is rather low. However, organisations and humanitarian logisticians can influence self-reliance by identifying clearly where challenges are.

Data from a diverse range of reports were extracted. As most of these reports lack reliable and comparative quantitative data, the limitation of the study must be taken into account. So far data were only validated on one case study. To develop the tool further, more data need to be taken into account.

To this point, there is no performance measurement tool available focusing on self-reliance of encamped refugees. In addition, no academic research has measured the interrelation between the level of investments in infrastructure and services and the improvement of the lives of camp residents, especially regarding the level of self-reliance.

Due to the randomness and unpredictability of many disasters, it is essential to be prepared to face difficult conditions after a disaster to reduce human casualties and meet the needs of the people. After the disaster, one of the most essential measures is to deliver relief supplies to those affected by the disaster. Therefore, this paper aims to assign demand points to the warehouses as well as routing their related relief vehicles after a disaster considering convergence in the border warehouses.

This research proposes a multi-objective, multi-commodity and multi-period queueing-inventory-routing problem in which a queuing system has been applied to reduce the congestion in the borders of the affected zones. To show the validity of the proposed model, a small-size problem has been solved using exact methods. Moreover, to deal with the complexity of the problem, a metaheuristic algorithm has been utilized to solve the large dimensions of the problem. Finally, various sensitivity analyses have been performed to determine the effects of different parameters on the optimal response.

According to the results, the proposed model can optimize the objective functions simultaneously, in which decision-makers can determine their priority according to the condition by using the sensitivity analysis results.

The focus of the research is on delivering relief items to the affected people on time and at the lowest cost, in addition to preventing long queues at the entrances to the affected areas.