1,720,984 research outputs found
A Matheuristic for Multi-Depot Multi-Trip Vehicle Routing Problems
No full textStarting from a real-life application, in this short paper, we propose the original Multi-Depot Multi-Trip Vehicle Routing Problem with Total Completion Times minimization (MDMT-VRP-TCT). For it, we propose a mathematical formulation as a MILP, design a matheuristic framework to quickly solve it, and experimentally test its performance. It is worth noting that this problem is original as in the literature its characteristics (i.e., multi-depot, multi-trip and total completion time) can be found separately, but never all together. Moreover, regardless of the application, our solution works in any case in which a multi-depot multi-trip vehicle routing problem must be solved
Link recommendation for social influence maximization
No full textSocial link recommendation systems, like "People-you-may-know" on Facebook, "Who-to-follow" on Twitter, and "Suggested-Accounts" on Instagram assist the users of a social network in establishing new connections with other users.While these systems are becoming more and more important in the growth of social media, they tend to increase the popularity of users that are already popular. Indeed, since link recommenders aim to predict user behavior, they accelerate the creation of links that are likely to be created in the future and, consequently, reinforce social bias by suggesting few (popular) users, giving few chances to most users to create new connections and increase their popularity. In this article, we measure the popularity of a user by means of her social influence, which is her capability to influence other users' opinions, and we propose a link recommendation algorithm that evaluates the links to suggest according to their increment in social influence instead of their likelihood of being created. In detail, we give a 1 - ∈ factor approximation algorithm for the problem of maximizing the social influence of a given set of target users by suggesting a fixed number of new connections considering the Linear Threshold model asmodel for diffusion.We experimentally showthat,with fewnewlinks and small computational time, our algorithm is able to increase by far the social influence of the target users. We compare our algorithm with several baselines and show that it is the most effective one in terms of increased influence
Exploiting social influence to control elections based on positional scoring rules
No full textHerein, we present Linear Threshold Ranking (LTR), an extension of the Linear Threshold Model (Kempe et al., KDD 2003). LTR models the spread of a message supporting a target candidate in a social network and how social influence affects the preferences of the voters who receive it, in elections based on positional scoring rules. The problem of election control through social influence requires finding a bounded subset of nodes to be the initial spreaders of this message to maximize the Margin of Victory of a target candidate against the most voted opponent. We prove the problem is NP-hard in LTR. By showing the equivalence of LTR with alternative stochastic processes and then exploiting submodularity, we provide a [Formula presented] approximation algorithm. We achieve similar results also in the destructive variation of LTR, where the message undermines a target candidate, negatively influencing the voters' preference on that candidate
Vote for me! Election control via social influence in arbitrary scoring rule voting systems
No full textOnline social networks are used to diffuse opinions and ideas among users, enabling a faster communication and a wider audience. The way in which opinions are conditioned by social interactions is usually called social influence. Social influence is extensively used during political campaigns to advertise and support candidates. We consider the problem of exploiting social influence in a network of voters to change their opinion about a target candidate with the aim of increasing his chance to win or lose the election in a wide range of voting systems. We introduce the Linear Threshold Ranking, a natural and powerful extension of the well-established Linear Threshold Model, which describes the change of opinions taking into account the amount of exercised influence. We are able to maximize the score of a target candidate up to a factor of 1-1/e by showing submodularity. We exploit such property to provide a 1/3(1 - l/e)-approximation algorithm for the constructive election control problem and a 1/2(1 - l/e)-approximation algorithm for the destructive control problem. The algorithm can be used in arbitrary scoring rule voting systems, including plurality rule and borda count
Election control through social influence with voters’ uncertainty
No full textThe problem of election control through social influence consists in finding a set of nodes in a social network of voters to be the starters of a political campaign aimed at supporting a particular target candidate. The voters reached by the campaign change their views on the candidates. The goal is to model the spread of the campaign in such a way as to maximize the chances of winning for the target candidate. Herein, differently from previous work, we consider that each voter is associated with a probability distribution over the candidates modeling the likelihood of the voter to vote for each candidate. In a first model we propose, we prove that, under the Gap-ETH, the problem cannot be approximated to within a factor better than 1 / no(1), where n is the number of voters. In a second model, which is a slight relaxation of the first one, the problem instead admits a constant-factor approximation algorithm. Finally, we present simulations on both synthetic and real networks, comparing the results of our algorithm with those obtained by a standard greedy algorithm for Influence Maximization
On the Scheduling of Conflictual Deliveries in a last-mile delivery scenario with truck-carried drones
No full textIn this paper, we investigate the symbiosis between a truck and multiple drones in a last-mile package delivery scenario, introducing the Scheduling Conflictual Deliveries Problem (SCDP). From the main depot, a truck takes care of transporting a fleet of drones that will be used to deliver packages to customers. The route of the truck is predefined. Each delivery is associated with the energy cost of a drone, a reward that characterizes the priority of the delivery, and an interval between two points of the truck's route: the point from which the drone departs (launch point) and the point at which the drone returns to the truck (rendezvous point). The objective of the SCDP is to find a scheduling for the drones that maximizes the overall reward subject to the drone's battery capacity while ensuring that the same drone performs deliveries whose delivery intervals do not intersect. After showing that SCDP is an NP-hard problem, we devise an Integer Linear Programming (ILP) formulation for it. Furthermore, we devise a pseudo-polynomial time optimal algorithm for the single drone case and additional approximation algorithms for both the single and multiple drones case. Finally, we thoroughly compare the performances of our presented algorithms on different synthetic datasets
Speeding up Routing Schedules on Aisle Graphs with Single Access
Full text linkIn this article, we study the orienteering aisle-graph single-access problem (OASP), a variant of the orienteering problem for a robot moving in a so-called single-access aisle graph, i.e., a graph consisting of a set of rows that can be accessed from one side only. Aisle graphs model, among others, vineyards or warehouses. Each aisle-graph vertex is associated with a reward that a robot obtains when it visits the vertex itself. As the energy of the robot is limited, only a subset of vertices can be visited with a fully charged battery. The objective is to maximize the total reward collected by the robot with a battery charge. We first propose an optimal algorithm that solves the OASP in O (m 2n 2) time for aisle graphs with a single access consisting of m rows, each with n vertices. With the goal of designing faster solutions, we propose four greedy suboptimal algorithms that run in at most O(mn\(m + n)) time. For two of them, we guarantee an approximation ratio of 1 2(1-1 e), where e is the base of the natural logarithm, on the total reward by exploiting the well-known submodularity property. Experimentally, we show that these algorithms collect more than 80% of the optimal reward
Energy-Constrained Delivery of Goods with Drones under Varying Wind Conditions
Full text linkIn this paper, we study the feasibility of sending drones to deliver goods from a depot to a customer by solving what we call the Mission-Feasibility Problem (MFP). Due to payload constraints, the drone can serve only one customer at a time. To this end, we propose a novel framework based on time-dependent cost graphs to properly model the MFP and tackle the delivery dynamics. When the drone moves in the delivery area, the global wind may change thereby affecting the drone's energy consumption, which in turn can increase or decrease. This issue is addressed by designing three algorithms, namely: (i) compute the route of minimum energy once, at the beginning of the mission, (ii) dynamically reconsider the most convenient trip towards the destination, and (iii) dynamically select only the best local choice. We evaluate the performance of our algorithms on both synthetic and real-world data. The changes in the drone's energy consumption are reflected by changes in the cost of the edges of the graphs. The algorithms receive the new costs every time the drone flies over a new vertex, and they have no full knowledge in advance of the weights. We compare them in terms of the percentage of missions that are completed with success (the drone delivers the goods and comes back to the depot), with delivered (the drone delivers the goods but cannot come back to the depot), and with failure (the drone neither delivers the goods nor comes back to the depot)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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