We present a greedy 0.5-approximation algorithm for allocation indivisible jobs in a multiprocessor system. The algorithm uses an ordering of processors according to the non-decreasing of size, and two orderings of it...
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ISBN:
(纸本)9781728129877
We present a greedy 0.5-approximation algorithm for allocation indivisible jobs in a multiprocessor system. The algorithm uses an ordering of processors according to the non-decreasing of size, and two orderings of items: in nonincreasing utility order and in nonincreasing order of the utility/size ratio. These orderings create two lexicographic orderings on the set I × J (here I is the set of jobs and J is the set of processors). Based on these lexicographic orderings, the algorithm creates an admissible allocation by looking through the pairs (i, j) ∈ I × J in the corresponding order and allocating the job i to processor j if this job is not allocated yet and can be allocated to processor j. The algorithm chooses the best of the two obtained solutions. This algorithm is 0.5-approximate and has running time O(mn) (without sorting), where m and n are the sizes of the sets I and J correspondingly.
<正>In this paper,we address the scheduling problem with rejection in which we can choose a subset of jobs to *** not to process any job incurs a corresponding *** consider the following problem for the first time:s...
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<正>In this paper,we address the scheduling problem with rejection in which we can choose a subset of jobs to *** not to process any job incurs a corresponding *** consider the following problem for the first time:scheduling with rejection to minimize the total weighted completion time with the constraint of total penalties on identical parallel machines,where the number of identical parallel machines is *** show that it is NP-hard and design a pseudo-polynomial time algorithm as well as an FPTAS through dynamic programming.
Given a graph G = (V, E) and a requirement function r: W1 x W2 → R+ for two families W1, W2 ⊆ 2V - {θ}, we consider the problem (called area-to-area edge-connectivity augmentation problem) of augmenting G by a small...
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ISBN:
(纸本)9781920682750
Given a graph G = (V, E) and a requirement function r: W1 x W2 → R+ for two families W1, W2 ⊆ 2V - {θ}, we consider the problem (called area-to-area edge-connectivity augmentation problem) of augmenting G by a smallest number of new edges so that the resulting graph G satisfies δG (X) ≥ r(W1, W2) for all X ⊆ V, W1 ∈ W1, and W2 ∈ W2 with W1 ⊆ X ⊆ V - W2, where δG(X) denotes the degree of a vertex set X in G. This problem can be regarded as a natural generalization of the global, local, and node-to-area edge-connectivity augmentation *** this paper, we show that there exists a constant c such that the problem is inapproximable within a ratio of clog α(W1, W2), unless P=NP, even restricted to the directed global node-to-area edge-connectivity augmentation or undirected local node-to-area edge-connectivity augmentation, where α(W1, W2) denotes the number of pairs W1 ∈ W1 and W2 ∈ W2 with r(W1, W2) > 0. We also provide an O(log α (W1, W2))-approximation algorithm for the area-to-area edge-connectivity augmentation problem. This together with the negative result implies that the problem is Θ(log α (W1, W2))-approximable, unless P=NP, which solves open problems for node-to-area edge-connectivity augmentation (Ishii et al. 2008, Ishii and Hagiwara 2006, Miwa and Ito 2004).Furthermore, we characterize the node-to-area and area-to-area edge-connectivity augmentation problems as the augmentation problems with modulotone and extended modulotone functions.
Two on-line algorithms for variable-size and variable-cost bin packing problems are *** first algo- rithm,based on the well known HARMONIC algorithm,han- dles the general case with competitive ratio ρ<*** sec- ond...
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Two on-line algorithms for variable-size and variable-cost bin packing problems are *** first algo- rithm,based on the well known HARMONIC algorithm,han- dles the general case with competitive ratio ρ<*** sec- ond one focuses on a special case which gives an even smaller ratio.
The multiple knapsack problem is to pack some items into given knapsacks, such that the sum of the knapsack profits is maximized. This paper is concerned with a variant of the multiple knapsack problem, called the mul...
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The multiple knapsack problem is to pack some items into given knapsacks, such that the sum of the knapsack profits is maximized. This paper is concerned with a variant of the multiple knapsack problem, called the multiple knapsack problem with compatible bipartite graph (MKPCBG), where two items can be packed into the same knapsack only if their corresponding vertices are adjacent in the given compatible bipartite graph. Under two different objectives, we prove that the MKPCBG problem is strongly NP-hard, design some 1/2-approximation algorithms, and design two optimal algorithms for the special case where all knapsacks have the same capacity.
In this paper, we studied the minimum latency conflict-free many-to-one data aggregation scheduling problem in multi-channel multi-hop wireless sensor networks: Given locations of all sensors and a base station, some ...
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ISBN:
(纸本)9781612842325
In this paper, we studied the minimum latency conflict-free many-to-one data aggregation scheduling problem in multi-channel multi-hop wireless sensor networks: Given locations of all sensors and a base station, some sensors which are called as sources, find a schedule such that data from all sources can be transmitted to the base station without any conflict and the latency is minimized. In this model, each sensor has three parameters which are transmission range r, interference range ar and carrier sensing range (beta)r where a, and beta are constant. There are lambda >=1 available channels for communications. We designed an approximation algorithm with ratio ([a/lambda] + 11 [b/lambda]) This work improves our previous work when lambda velence 1. Extensive simulations valuate the performance of the algorithm.
This article is devoted to nonlinear single-path routing problems, which are known to be NP-hard even in the simplest cases. For solving these problems, we propose an algorithm inspired from Game Theory in which indiv...
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This article is devoted to nonlinear single-path routing problems, which are known to be NP-hard even in the simplest cases. For solving these problems, we propose an algorithm inspired from Game Theory in which individual flows are allowed to independently select their path to minimize their own cost function. We design the cost function of the flows so that the resulting Nash equilibrium of the game provides an efficient approximation of the optimal solution. We establish the convergence of the algorithm and show that every optimal solution is a Nash equilibrium of the game. We also prove that if the objective function is a polynomial of degree d >= 1, then the approximation ratio of the algorithm is (2(1/d) - 1)(-d). Experimental results show that the algorithm provides single-path routings with modest relative errors with respect to optimal solutions, while being several orders of magnitude faster than existing techniques. (C) 2016 Wiley Periodicals, Inc.
We consider the Moran process, as generalized by Lieberman, Hauert and Nowak (Nature, 433:312-316, 2005). A population resides on the vertices of a finite, connected, undirected graph and, at each time step, an indivi...
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ISBN:
(纸本)9781611972108
We consider the Moran process, as generalized by Lieberman, Hauert and Nowak (Nature, 433:312-316, 2005). A population resides on the vertices of a finite, connected, undirected graph and, at each time step, an individual is chosen at random with probability proportional to its assigned "fitness" value. It reproduces, placing a copy of itself on a neighbouring vertex chosen uniformly at random, replacing the individual that was there. The initial population consists of a single mutant of fitness r > 0 placed uniformly at random, with every other vertex occupied by an individual of fitness 1. The main quantities of interest are the probabilities that the descendants of the initial mutant come to occupy the whole graph (fixation) and that they die out (extinction); almost surely, these are the only possibilities. In general, exact computation of these quantities by standard Markov chain techniques requires solving a system of linear equations of size exponential in the order of the graph so is not feasible. We show that, with high probability, the number of steps needed to reach fixation or extinction is bounded by a polynomial in the number of vertices in the graph. This bound allows us to construct fully polynomial randomized approximation schemes (FPRAS) for the probability of fixation (when r > 1) and of extinction (for all r > 0).
A two-stage supply chain scheduling problem is considered, where the first stage is job production and the second stage is job delivery. The focus is on the study of the integration of production scheduling with deliv...
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A two-stage supply chain scheduling problem is considered, where the first stage is job production and the second stage is job delivery. The focus is on the study of the integration of production scheduling with delivery of finished products to customers. In our considered model each job can be processed on either of two identical machines, and then delivered by a vehicle to a customer location. We present an improved algorithm with the worst-case performance ratio 14/9 + ε, which improves the known upper bounds of 2 and 5/3 in [Chang, Y.C. and Lee, C.Y., E. J. O. R., 158 (2004), pp. 470-487; Zhong, W., Dosa, G. and Tan, Z.Y., E. J. O. R., 182(2007), pp. 1057-1072].
The problem of maximizing the sum of a constrained submodular and a supermodular function has many applications such as social networks, machine learning, and artificial intelligence. In this article, we study the mon...
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The problem of maximizing the sum of a constrained submodular and a supermodular function has many applications such as social networks, machine learning, and artificial intelligence. In this article, we study the monotone submodular + supermodular maximization problem under a cardinality constraint and a p-system constraint, respectively. For each problem, we provide a stochastic algorithm and prove the approximation ratio of each algorithm theoretically. Since the algorithm of the latter problem can also solve the former problem, we do some numerical experiments of the two algorithms to compare the time as well as the quality of the two algorithms in solving the former problem.
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