In this paper, we present an optimization model for the management of the green area in order to find the optimal green surface to absorb CO 2 emissions of industrialized cities. We obtain a minimization problem and the related variational inequality. We study the Lagrange theory to better understand the process that regulates the possible increase in green space. Then, we propose a computational procedure, based on the Euler method, to find the optimal solution to the variational inequality associated with our minimization problem and, finally, some numerical examples based on real scenarios.

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In this paper, we present a multi-tiered network-based optimization model describing the provision of services by network slices of 5G-Service providers (e.g. through Unmanned Aerial Vehicles (UAVs) organized as Flying Ad hoc Networks (FANET)), taking into account the security levels of each provider. The three levels of the network consist of the infrastructure layers, which contain resources needed to execute a service, the slices layer, where services are served for the services layer, which represents the upper layer of the network and consists of services or applications required by users or devices. The objective of the proposed model is to establish the optimal flows between network layers and the optimal security levels in order to maximize the providers’ profits, given by the difference between the revenues obtained by the sale of services and the rental of their resources and the costs. Numerical experiments are performed and solved with a new nature-inspired genetic algorithm adapted to the optimization 5G network problem.

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In this paper, we propose a two-stage stochastic optimization model for the provision services in a multi-tiered network, consisting in user or devices on the ground requiring services to controller UAVs in flight. The requested services are executed by a fleet of pre-existing and additional UAVs. The possible occurrence of disaster scenarios and the related uncertainty and severity could cause an unexpected and sudden increase in demand. Hence, the aim of the proposed model is to optimize the management of the pre-existing and additional resources in order to maximize the total profit of service providers and, simultaneously, minimize the expected loss related to a possible unmet demand. A variational approach is proposed, and some numerical examples are performed to validate the effectiveness of our model.