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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 aimed at exploring support for older people in protracted displacement in Nigeria, emphasising the available support and the gap in the support provided to them.

Qualitative interviews were used to collect data from ten displaced older people in New-Kuchingoro internally displaced persons (IDP) camp Abuja, Nigeria. The collected data were analysed thematically with NVivo 12.

Results show that older people received material, emotional and psychological supports from their families, neighbours, friends, religious organisations and non-governmental organisations. Several gaps were identified in the support provided to displaced older people. For example, their special nutritional or medical needs were neglected, and their support was primarily material, sporadic and spontaneous, as there is no existing framework guiding the care and support of older people in displacement.

This study provides a foundation for further research on older people in displacement, an area that has received minimal scholarly attention.

The paper recommends that researchers and displaced persons’ care providers should pay more attention to the peculiar support needs of this less visible vulnerable group and adopt the internal displacement policy for the long-term protection of older people in displacement.

Older people in displacement remain less visible as humanitarian aid programmes and research focus more on women and children. This lack of attention may put older people in displacement at more risk as their peculiar needs may not be met. To the best of the authors’ knowledge, this study is the first to examine the gap in support for older people in displacement in Nigeria.