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Online retailers invest an enormous amount of funds in delivering products to customers. In recent years, these delivery costs have increased as a result of changes in fuel costs, which has brought new challenges to retailers in terms of offering competitive prices. Many retailers have begun to utilize a drone-based aerial delivery system as an alternative solution to overcome the problems related to the high transportation costs and traffic jams in large cities. This study provides a mathematical model for minimizing the total costs of the aerial delivery system concerned with refuel stations, warehouses, drone procurement, and transportation. The waiting time of the customers is restricted based on the M/G/K queueing system. The fuel stations and warehouses are the main components of the network. The demand (occurring at the lowest level) is ultimately satisfied via launch stations (the network's highest level). Refuel stations support drones along their long routes between the launch stations and demand points. To account for the different levels of the facilities, a multi-level facility location approach is utilized. Moreover, the nondeterministic nature of the problem is tackled using fuzzy variables. The ultimate mathematical model is a congested fuzzy capacitated multi-level facility location problem that is solved by the possibilistic approach.

In recent years, small unmanned aerial vehicles have been used to deliver medicine and goods as a solution to severe traffic jams and to serve the purpose of fast and effective delivery, especially for medical and emergency applications where time is vital. On the other hand, in the competitive market of today, retailers are considering the use of drones to minimize the customers' waiting times and as a way to lower their transportation costs. This study aims to develop a biobjective mathematical model to account for the optimum number and spatial location of facilities among a set of candidate locations such that the total travel distance, costs, and lost demand are minimized simultaneously. It is assumed that the demand occurrence follows a Poisson distribution and is uniformly distributed along the network edges. The proposed biobjective capacitated facility location model is NP-hard, thus nondominated sorting genetic algorithm II and reference-point based nondominated sorting genetic algorithm are applied to solve the problem. The performance of the algorithms, quality of solutions, and the results are investigated and discussed.

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Considering the recent lockdowns and travel bans due to COVID-19, novel tourism strategies are necessary to face the increasing need for innovative products and services and to ensure long-term sustainable growth. This study looks into the potential use of drones in providing online virtual tours of open-space tourist attractions. To do so, a novel mixed-integer linear mathematical model is developed to optimally determine the number and location of required facilities and the number of drones assigned to each center. The model is applied to a case study of Rome by selecting six historic sites as the tourist attractions and considering several candidate locations for establishing the facilities. The results of different potential scenarios imply that the project is profitable, even if the demand for virtual tours is low.

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Following a large-scale disaster in a highly populated city, one can expect a large number of injured people to need urgent operation. If it can be assumed that the city in question is likely to experience a disaster (although the time of its occurrence is unknown), pre-disaster measures should be taken to mitigate the consequences of the disaster. This study aims to minimize the fatality rate through the proper assignment of operating room personnel to hospitals, such that both the expected value of the functioning operating rooms and the expected value of the service level are maximized. Service level is defined as a variable that has a negative relationship to the distance a patient is expected to travel in order to receive the required medical services. The probability of survival, for both personnel and operating rooms, depends on several stochastic factors. A bi-objective mixed integer nonlinear model is proposed for the problem. A simulated annealing algorithm, particle swarm and a genetic algorithm are developed to solve the model using a weighted metric method. The model is applied to a possible worst-case earthquake scenario in Tehran. The results show that the proposed approach significantly improves the performance of post-disaster emergency relief.

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