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This paper proposes an approximation algorithm for the MCDS problem in wireless sensor networks.
This paper proposes an approximation model based on queuing network theory to analyze the impact of order batching and picking area zoning on the mean order throughput time in a pick-and-pass order picking system.
In our example, N and n are likely very large and computation of q k by enumeration is computationally tedious, however, Levin (1981) proposes an approximation approach based on Edgeworth expansions, and provides bounds for the true probabilities.
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Kaplan et al. [12] proposed an approximation algorithm that achieves an approximation guarantee of 2/3.
To solve it, Kuipers et al. [16] propose an approximation algorithm and a tunable heuristic algorithm.
We propose an approximation slippage model with one terrain and robot dependent parameter.
Recently, Seol and Cheun [4] proposed an approximation based on minimizing the average error.
They proposed an approximation algorithm for choosing a minimal subset of candidate locations where SGs may be deployed.
In this paper, we propose an approximation method for mining frequent itemsets over the sliding window of a data stream.
Moreover, we propose an approximation of the mixed criteria which amounts to solving a single scalar nonlinear equation.
First we establish a lower bound on the errors of all approximations and then we propose an approximation which is asymptotically efficient in the sense of this bound.
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