Ruteo de helicópteros modelado con tasa de falla estocástica en flotas heterogéneas (2020)
The use of helicopters in the logistics sector is very frequent to transport people and products to places that, due to geographical conditions, it is difficult to access by other kind of transport. The vehicle routing problem is a combinatorial optimization problem that aims to optimize the deliveries of any type of transport and it has been widely studied in literature since 1950s. Despite the vast quantity of studies found in VRP, it is important to consider simultaneously different real-industry characteristics in the problem making its solution closer to reality. Therefore, this project aims to solve the helicopters routing problem that minimizes the total traveling time simultaneously, considering: the use of a heterogeneous fleet, stochastic failure and repair times, demands of pickup and delivery, and helicopters maximum storage capacity. The problem is solved in three phases. Firstly, a Mixed Linear Programming (MILP) is proposed for the deterministic case. Secondly a Tabu Search algorithm is developed for the deterministic case. Thirdly, a simheuristic that hybridizes Tabu Search and Monte Carlo simulation procedures is designed to solve the stochastic counterpart of the problem. The performance of the simheuristic for small instances was calculated in comparison with the simulation of the deterministic solutions of MILP model. Additionally, for medium and large instances, the performance of simheuristic is evaluated in comparison with the simulation of deterministic solutions given by a modified nearest neighbour algorithm. Results show that the simheuristic improves an average of 18,56% the results of expected traveling times of simulated solutions of MILP model, and improves an average of 27.66% the simulated solutions of nearest neighbour algorithm.