Use of delivery drones for humanitarian operations: analysis of adoption barriers among logistics service providers from the technology acceptance model perspective.

Delivery drones are yet to be adopted as a systematic delivery system for humanitarian operations but have the potential to substantially increase the efficiency and effectiveness of future delivery options. Thus, we analyse the impact of factors affecting the adoption of delivery drones by logistics service providers for humanitarian operations. A conceptual model of potential barriers to adoption and development is created using the Technology Acceptance Model theory involving security, perceived usefulness, perceived ease of use and attitude as factors that affect the intention to use. We validate the model using empirical data collected from 103 respondents by the 10 leading logistics firms located in China between May and August 2016. through a survey to examine factors currently affecting the intention/non-intention to adopt delivery drones. The results show that ease of use and addressing key security considerations about the drone, the delivery package and the recipient are crucial for adopting the technology as a specialized delivery option for logistics service providers. This is the first study of its kind and contributes to understanding the operational, supply chain and behavioural factors in the adoption of drones by logistics services providers for humanitarian operations.

A multi-echelon dynamic cold chain for managing vaccine distribution.

While cold chain management has been part of healthcare systems, enabling the efficient administration of vaccines in both urban and rural areas, the COVID-19 virus has created entirely new challenges for vaccine distributions. With virtually every individual worldwide being impacted, strategies are needed to devise best vaccine distribution scenarios, ensuring proper storage, transportation and cost considerations. Current models do not consider the magnitude of distribution efforts needed in our current pandemic, in particular the objective that entire populations need to be vaccinated. We expand on existing models and devise an approach that considers the needed extensive distribution capabilities and special storage requirements of vaccines, while at the same time being cognizant of costs. As such, we provide decision support on how to distribute the vaccine to an entire population based on priority. We do so by conducting predictive analysis for three different scenarios and dividing the distribution chain into three phases. As the available vaccine doses are limited in quantity at first, we apply decision tree analysis to find the best vaccination scenario, followed by a synthetic control analysis to predict the impact of the vaccination programme to forecast future vaccine production. We then formulate a mixed-integer linear programming (MILP) model for locating and allocating cold storage facilities for bulk vaccine production, followed by the proposition of a heuristic algorithm to solve the associated objective functions. The application of the proposed model is evaluated by implementing it in a real-world case study. The optimized numerical results provide valuable decision support for healthcare authorities.