We study a two-phase, budget-con strained, network-planning problem with multiple hub types and demand scenarios. In each phase, we install (or move) capacitated hubs on selected buildings. We allocate hubs to realize...
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We study a two-phase, budget-con strained, network-planning problem with multiple hub types and demand scenarios. In each phase, we install (or move) capacitated hubs on selected buildings. We allocate hubs to realized demands, under technological constraints. We present a greedy algorithm to maximize expected demand covered and computationally study its performance. (C) 2004 Elsevier B.V. All rights reserved.
Least privilege means only the necessary privileges are needed to complete a task for users. This is one of the most important principles in RBAC model. Currently, how to assign roles to users to achieve this principl...
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ISBN:
(纸本)9783642226151
Least privilege means only the necessary privileges are needed to complete a task for users. This is one of the most important principles in RBAC model. Currently, how to assign roles to users to achieve this principle is still not solved. In this paper, the least privilege problem is proved to be NP-hard, and an approximation algorithm is given. The simulation result shows that with the algorithm, each user can acquire its privilege to perform its job with the least privilege principle.
Unmanned aerial vehicle fleets demand effective task assignment since the task scope of each vehicle depends on the task-specific equipment it carries. Here, we propose real-time task assignment for multiple multipurp...
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Unmanned aerial vehicle fleets demand effective task assignment since the task scope of each vehicle depends on the task-specific equipment it carries. Here, we propose real-time task assignment for multiple multipurpose unmanned aerial vehicles using a greedy algorithm. The greedy algorithm does not require complex mathematical modeling and can respond to real-time situational changes due to its fast computation. The proposed algorithm uses the total flight time of all unmanned aerial vehicles as the objective function so that each vehicle completes all tasks within its available battery level. To increase the diversity of solutions, the algorithm is dually computed in the forward and backward directions from the starting and destination points, respectively. We apply the algorithm to water resource management and compare the results with those of mixed integer linear programming and the genetic algorithm. While the flight-time cost is similar across algorithms, computation is 3-8 times faster for the proposed algorithm than for the genetic algorithm. This advantage increases as the number of tasks and/or unmanned aerial vehicles increases. The proposed algorithm's consideration of remaining battery levels when selecting randomly unmanned aerial vehicles also results in a higher average remaining battery level than the other algorithms. These results suggest that the proposed algorithm shows promise and feasibility for application to real-time task assignment scenarios for multiple multipurpose unmanned aerial vehicles.
The autonomous task success of an unmanned aerial vehiclel (UAV) or its military specialization called the unmanned combat aerial vehicle (UCAV) has a direct relationship with the planned path. However, planning a pat...
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The autonomous task success of an unmanned aerial vehiclel (UAV) or its military specialization called the unmanned combat aerial vehicle (UCAV) has a direct relationship with the planned path. However, planning a path for a UAV or UCAV system requires solving a challenging problem optimally by considering the different objectives about the enemy threats protecting the battlefield, fuel consumption or battery usage and kinematic constraints on the turning maneuvers. Because of the increasing demands to the UAV systems and game-changing roles played by them, developing new and versatile path planning algorithms become more critical and urgent. In this study, a greedy algorithm named as the Back-and-Forth (BaF) was designed and introduced for solving the path planning problem. The BaF algorithm gets its name from the main strategy where a heuristic approach is responsible to generate two predecessor paths, one of which is calculated from the start point to the target point, while the other is calculated in the reverse direction, and combines the generated paths for utilizing their advantageous line segments when obtaining more safe, short and maneuverable path candidates. The performance of the BaF was investigated over three battlefield scenarios and twelve test cases belonging to them. Moreover, the BaF was integrated into the workflow of a well-known meta-heuristic, artificial bee colony (ABC) algorithm, and detailed experiments were also carried out for evaluating the possible contribution of the BaF on the path planning capabilities of another technique. The results of the experiments showed that the BaF algorithm is able to plan at least promising or generally better paths with the exact consistency than other tested meta-heuristic techniques and runs nine or more times faster as validated through the comparison between the BaF and ABC algorithms. The results of the experiments further proved that the integration of the BaF boosts the performance of the ABC and
A 2-matching of a graph G is a spanning subgraph with maximum degree two. The size of a 2-matching U is the number of edges in U and this is at least n-(U) where n is the number of vertices of G and denotes the number...
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A 2-matching of a graph G is a spanning subgraph with maximum degree two. The size of a 2-matching U is the number of edges in U and this is at least n-(U) where n is the number of vertices of G and denotes the number of components. In this article, we analyze the performance of a greedy algorithm 2greedy for finding a large 2-matching on a random 3-regular graph. We prove that with high probability, the algorithm outputs a 2-matching U with (U) = Theta(n(1/5)).
We study the facial structure of polyhedra associated with the classical class-teacher time-table problem. After the presentation of some first results we introduce the class of partition inequalities and show that th...
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We study the facial structure of polyhedra associated with the classical class-teacher time-table problem. After the presentation of some first results we introduce the class of partition inequalities and show that they are facet-defining for the general, monotone time-table polyhedron. Then we show that for two classes, two teachers and an arbitrary number of lime periods, the time-table problem can be solved by the greedy algorithm. A polyhedral description for the associated monotone polytope is given based on nonnegativity, assignment and partition inequalities. The completeness of the linear system is established via the concept of total dual integrality. Finally, we conclude with some open questions and related problems.
The problem of maximizing a given set function with a cardinality constraint has widespread applications. A number of algorithms have been provided to solve the maximization problem when the set function is monotone a...
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The problem of maximizing a given set function with a cardinality constraint has widespread applications. A number of algorithms have been provided to solve the maximization problem when the set function is monotone and submodular. However, reality-based set functions may not be submodular and may involve large-scale and noisy data sets. In this paper, we present the Stochastic-Lazier-greedy algorithm (SLG) to solve the corresponding nonsubmodular maximization problem and offer a performance guarantee of the algorithm. The guarantee is related to a submodularity ratio, which characterizes the closeness of to submodularity. Our algorithm also can be viewed as an extension of several previous greedy algorithms.
We present a new differential compression algorithm that combines the hash value techniques and suffix array techniques of previous work. The term '' differential compression '' refers to encoding a fi...
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We present a new differential compression algorithm that combines the hash value techniques and suffix array techniques of previous work. The term '' differential compression '' refers to encoding a file (a version file) as a set of changes with respect to another file (a reference file). Previous differential compression algorithms can be shown empirically to run in linear time, but they have certain drawbacks;namely, they do not find the best matches for every offset of the version file. Our algorithm, hsadelta (hash suffix array delta), finds the best matches for every offset of the version file, with respect to a certain granularity and above a certain length threshold. The algorithm has two variations depending on how we choose the block size. We show that if the block size is kept fixed, the compression performance of the algorithm is similar to that of the greedy algorithm, without the associated expensive space and time requirements. If the block size is varied linearly with the reference file size, the algorithm can run ill linear time and constant space. We also show empirically that the algorithm performs better than other state-of-the-art differential compression algorithms in terms of compression and is comparable in speed.
Currently, the demand for tourism is increasing, but traditional tour group routes have been unable to meet individual needs. This paper proposes a novel personalized recommendation method for tour routes based on cro...
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Currently, the demand for tourism is increasing, but traditional tour group routes have been unable to meet individual needs. This paper proposes a novel personalized recommendation method for tour routes based on crowd sensing. First, we utilize ArcMap to model a real road network. We then propose a novel scoring mechanism for points of interest, including an interest label matching score and crowd sensing score, and implement a user -personalized multi -constraint interest model. Based on whether a user has must -see scenic spots, we propose a variable neighborhood greedy tour recommendation algorithm for users with no must -see scenic spots and a single -multiple point of interest two -stage greedy tour route recommendation algorithm for users with must -see scenic spots. We collected real data regarding 200 attractions, 881 restaurants, 570 hotels and 28 mature travel routes in Beijing from Ctrip, Dianping and Tuniu. We perform case analysis on Beijing dataset and comparative experiments on Beijing and public datasets with the existing algorithms. The experimental results demonstrate that our algorithm has superior rationality and efficiency.
greedy algorithms are among the most elementary ones in theoretical computer science and understanding the conditions under which they yield an optimum solution is a widely studied problem. Greedoids were introduced b...
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greedy algorithms are among the most elementary ones in theoretical computer science and understanding the conditions under which they yield an optimum solution is a widely studied problem. Greedoids were introduced by Korte and Lovasz at the beginning of the 1980s as a generalization of matroids. One of the basic motivations of the notion was to extend the theoretical background behind greedy algorithms beyond the well-known results on matroids. Indeed, many well-known algorithms of a greedy nature that cannot be interpreted in a matroid-theoretical context are special cases of the greedy algorithm on greedoids. Although this algorithm turns out to be optimal in surprisingly many cases, no general theorem is known that explains this phenomenon in all these cases. Furthermore, certain claims regarding this question that were made in the original works of Korte and Lovasz turned out to be false only most recently. The aim of this paper is to revisit and straighten out this question: we summarize recent progress and we also prove new results in this field. In particular, we generalize a result of Korte and Lovasz and thus we obtain a sufficient condition for the optimality of the greedy algorithm that covers a much wider range of known applications than the original one.
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