This study explores the effectiveness of quantum approaches in addressing combinatorial optimization problems, arising in the logistics domain. In particular, we concentrate on the two-level Facility Location Problem, which is known to be NP-hard and therefore unable to be solved in a polynomial amount of time. Due to the difficulties in addressing these problems, we explore the potential of quantum annealing techniques to solve the Quantum Unconstrained Binary Optimization formulation, using the D-Wave solver. Furthermore, given that this formulation is still underperforming for large instances, we propose a method to preprocess the logistic network. This method has been developed with the intention of reducing the size of the logistic network, thus allowing for improved system performance as the size of the instances increases. We demonstrate the efficacy of our proposed solution approach through the execution of computational experiments. The objective of these experiments is to validate the performance of quantum annealing with our preprocessing network techniques.

Due to the variability in energy production from renewable sources such as solar and wind, maintaining the stability of power grids in renewable energy systems presents several challenges. Accurate prediction of both renewable energy generation and energy consumption is crucial to addressing these issues effectively. Artificial intelligence (AI) techniques can significantly enhance the accuracy of these predictions by analyzing large and complex datasets. A review of previous research highlights the importance of selecting the most appropriate AI technique, as different approaches have been proposed and evaluated using various performance metrics. This study used a multi-criteria decision-making method to rank AI prediction techniques. Extreme Gradient Boosting, Random Forest, Long Short-Term Memory, and Artificial Neural Networks emerged as the highest-ranked among the evaluated techniques. These four techniques were applied to data sets to predict energy consumption and solar energy production.

There is a definite possibility that, in a recent future, Unmanned Aerial Vehicles (UAVs) will form the backbone of any smart city in terms of automation and networking. One approach to extend the UAVs’ resources spectrum is to provide a mean for them to opportunistically recharge and connect to otherwise unreachable networks: provide Training and Recharge Areas (TRAs). In these dedicated areas, the UAVs could dock to Energy and Data Dispensers (EDD) devices to resupply their batteries and exploit a high-speed connection. To autonomously move through the smart city while accomplishing a set of given tasks but, at the same time, consider visiting the EDDs, is part of a tridimensional trajectory planning problem that needs to be addressed. In this paper, we formally define the combinatorial optimization problem representing the trajectory planning. We consider the case in which more than one UAV can be connected with the same EDD at the same time, by properly addressing the assignment of the bandwidth. Through simulative investigation, realistic values for the solution of the optimization problem are found. The behavior of the proposed model is compared with an ”online” approach that does not require the same resources and knowledge and whose evaluation and comparison with the ”offline” approach are performed through network simulation.

Abstract This paper addresses the two-dimensional irregular bin packing problem, whose main aim is to allocate a given set of irregular pieces to larger rectangular containers (bins), while minimizing the number of bins required to contain all pieces. To solve the problem under study a dynamic hierarchical hyper-heuristic approach is proposed. The main idea of the hyper-heuristics is to search the space of low-level heuristics for solving computationally difficult problems. The proposed approach is “dynamic” since the low-level heuristic to be executed is chosen on the basis of the main characteristics of the instance to be solved. The term “hierarchical” is used to indicate the fact that the main hyper-heuristic can execute either simple heuristics or can run in a “recursive fashion” a hyper-heuristic. The developed solution strategy is evaluated empirically by performing extensive experiments on irregular packing benchmark instances. A comparison with the state-of-the-art approaches is also carried out. The computational results are very encouraging.

Abstract We address the problem of routing a fleet of trucks equipped with unmanned aerial vehicles, commonly known as drones, to perform deliveries in last‐mile delivery process. The customers can be served by either a truck or a drone within the respective time window of each. Each capacitated truck carries drones that can be launched to perform deliveries. The drone takes off from a truck located either at a customer or at the depot and it must land on the same truck after visiting a customer to be served. The aim is to serve all customers at minimum cost, under time window, capacity, and flying endurance constraints. We formulate the problem as a mixed integer linear program (MILP) and develop a heuristic procedure where a two‐phase strategy is embedded in a multi‐start framework. The computational results are carried out on instances generated by starting from vehicle routing problem with time windows benchmarks. We analyze the behavior of the considered transportation system by mean of the solutions provided by the MILP. The proposed formulation is able to solve instances with up to 15 customers. The solutions of the MILP are used as benchmark to assess the effectiveness of the proposed heuristic procedure.

This paper formulates the Sport Event Filming with Connectivity Constraints (SEF-C2) problem. The SEF-C2problem is an event coverage problem, which exploits a team of Unmanned Aerial Vehicles (UAVs) over a limited field in order to track the movements of an object (e.g., of the ball) and to deliver a video stream of the events (e.g., ball passes, goals) to the spectators meeting certain timeliness and video quality criteria. Assuming a priori knowledge of the whole sequence of actions, first a novel mathematical model is introduced that determines a sequence of movements for the UAVs, such that the timeliness of the filming is maximized and the total traveled distance is minimized. Then, dynamic, artificial potential function based, distributed UAV movement schemes that have no a priori knowledge of the sequence of game actions are proposed to optimize networking performance. Extensive simulations are used to analyze the performance in terms of video transmission quality and show that the proposed schemes outperform existing schemes.

We study the robust constrained shortest path problem under resource uncertainty. After proving that the problem is in the strong sense for arbitrary uncertainty sets, we focus on budgeted uncertainty sets introduced by Bertsimas and Sim (2003) and their extension to variable uncertainty by Poss (2013). We apply classical techniques to show that the problem with capacity constraints can be solved in pseudopolynomial time. However, we prove that the problem with time windows is in the strong sense when is not fixed, using a reduction from the independent set problem. We introduce then new robust labels that yield dynamic programming algorithms for the problems with time windows and capacity constraints. The running times of these algorithms are pseudopolynomial when is fixed, exponential otherwise. We present numerical results for the problem with time windows which show the effectiveness of the label-setting algorithm based on the new robust labels. Our numerical results also highlight the reduction in price of robustness obtained when using variable budgeted uncertainty instead of classical budgeted uncertainty. © 2015 Wiley Periodicals, Inc.NETWORKS, Vol. 66(2), 98–111 2015

In this paper, we introduce two families of distributed algorithms to control the movement of groups of flying robots that are monitoring an event by moving over the field where the event takes place, while optimizing some specific objective. In order to show the effectiveness of our algorithms, we formulate the Sport Event Filming (SEF) problem. The objective of the problem is to maximize the satisfaction of event viewers while minimizing the distance traveled by the camera-drones. We propose two families of solutions to solve the dynamic version of the problem, where the flying robots do not have any knowledge of the input sequence and move in reaction to the movements of the protagonists of the event. The first family (Nearest Neighbor)is based on a technique used in robotic systems, whereas the second family (Ball Movement Interception) is designed based on specific characteristics of the SEF problem. We present extensive simulation results for both families in terms of average viewer satisfaction and travelled distance for the flying robots, when several parameters vary.

This article surveys the main contributions that have appeared in the scientific literature addressing resource constrained shortest path problems. The aim of this work is twofold: to give a structured survey of the literature on this topic; to provide a starting point for researchers who want to address the problems at hand. The study is focused on the relevant contributions dealing with exact solution approaches. © 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 62(3), 183‐200 2013

Energy awareness for computation and protocol management is becoming a crucial factor in the design of protocols and algorithms. On the other hand, in order to support node mobility, scalable routing strategies have been designed and these protocols try to consider the path duration in order to respect some QoS constraints and to reduce the route discovery procedures. Often energy saving and path duration and stability can be two contrasting efforts and trying to satisfy both of them can be very difficult. In this paper, a novel routing strategy is proposed. This proposed approach tries to account for link stability and for minimum drain rate energy consumption. In order to verify the correctness of the proposed solution a biobjective optimization formulation has been designed and a novel routing protocol called Link-stAbility and Energy aware Routing protocols (LAER) is proposed. This novel routing scheme has been compared with other three protocols: PERRA, GPSR, and E-GPSR. The protocol performance has been evaluated in terms of Data Packet Delivery Ratio, Normalized Control Overhead, Link duration, Nodes lifetime, and Average energy consumption.