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The deployment of a field hospital can play an important role in the response to an emergency. This paper is concerned with the management of emergency staff to a field hospital from a roster of volunteers with different characteristics. This paper aims to propose a mathematical optimisation model that selects the necessary profiles of the roster according to several criteria and provides travel planning taking into account the total cost of the operation.

This study uses a multi-criteria optimisation model to take into account the preferences of the three main stakeholders involved in the deployment of the field hospital: the cooperation organisation, the staff and the end users. The model considers the possibility of using commercial or chartered flights, allows staff to indicate their preferred availability, considers the grading of volunteers according to their skills and training and provides a final flight schedule for all the medical personnel needed to operate the field hospital. Compromise programming is used to provide a Pareto optimal solution, which is compared with solutions provided by Goal programming.

The model has been validated using data from the operation in a case study of the deployment of the Spanish START hospital in Turkey 2023, demonstrating the practical utility of the model in similar operations.

The study innovates by considering a multi-criteria model that takes into account the main actors involved in the response – cooperation organisation, staff and end users – in an integrated way and proposes new measures of efficiency.

Production of waste has been increased exponentially due to world industrialization and urban and machine life expansion. On the other hand, the outbreak of the COVID-19 coronavirus quickly became a global crisis. This crisis has added a large amount of waste to urban waste. The purpose of this study is to create cooperation between municipal waste collector contractors.

Thus, a mathematical model is proposed under uncertain conditions, which includes the volume of municipal waste and infectious waste including personal protective equipment and used equipment for patients. To reduce total costs, the results are evaluated with four cooperative game theory methods such as Shapley value, t value, core center and least core. Ultimately, the saved cost by cooperation in each coalition is allocated fairly among the contractors. Finally, a comparison was made between the solution methods based on the value of the objective function and the solution time.

The results indicate that the proposed cooperative method increases cost savings and reduces the fine of residual waste. Therefore, it can be mentioned that this kind of cooperation would finally result in more incentives for contractors to form larger coalitions. Genetic algorithms were used to solve the large-scale model.

The proposed model boosts the current understanding of waste management in the COVID-19 pandemic. The paper adds additional value by unveiling some key future research directions. This guidance may demonstrate possible existing and unexplored gaps so that researchers can direct future research to develop new processes.