In this paper, we study novel variants of the well‐known two‐echelon vehicle routing problem in which a truck works on the first echelon to transport parcels and a fleet of drones to intermediate depots while in the second echelon, the drones are used to deliver parcels from intermediate depots to customers. The objective is to minimize the completion time instead of the transportation cost as in classical two‐echelon vehicle routing problems. Depending on the context, a drone can be launched from the truck at an intermediate depot once (single‐trip drone) or several times (multiple‐trip drone). Mixed‐integer linear programming models are first proposed to formulate mathematically the problems and solve to optimality small‐sized instances. To handle larger instances, a metaheuristic based on the idea of greedy randomized adaptive search procedure is introduced. The main novel feature of our metaheuristic lies in the design of initial solution construction and local search operators, which can cover all the decision layers of the problems and run in scriptOfalse(1false)$\mathcal{O}(1)$ using additional data structures. Experimental results obtained on instances of different contexts are reported and analyzed.

The traveling salesman problem (TSP) is the most well-known problem in combinatorial optimization which has been studied for many decades. This paper focuses on dealing with one of the most difficult TSP variants named the quadratic traveling salesman problem (QTSP) that has numerous planning applications in robotics and bioinformatics. The goal of QTSP is similar to TSP which finds a cycle visiting all nodes exactly once with minimum total costs. However, the costs in QTSP are associated with three vertices traversed in succession (instead of two like in TSP). This leads to a quadratic objective function that is much harder to solve. To efficiently solve the problem, we propose a hybrid genetic algorithm including a local search procedure for intensification and a new mutation operator for diversification. The local search is composed of a restricted double-bridge move (a variant of 4-Opt); and we show the neighborhood can be evaluated in O(n^2), the same complexity as for the classical TSP. The mutation phase is inspired by a ruin-and-recreate scheme. Experimental results conducted on benchmark instances show that our method significantly outperforms state-of-the-art algorithms in terms of solution quality. Out of 800 considered instances, it finds 437 new best-known solutions.

The Flying Adhoc (FANET) network is focused on the use of mobile airborne objects and makes it possible to form selforganizing networks, which can provide channels of information interaction between these objects and not be limited.