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Greedy algorithm

About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.


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Journal ArticleDOI
TL;DR: This work proves a lower bound on the efficiency of a distributed scheduling algorithm by first assuming that all of the traffic only uses one hop of the network and proves that the lower bound is tight in the sense that, for any fraction larger than the lowerbound, it can find a topology and an arrival rate vector within the fraction of the capacity region such that the network is unstable under a greedy scheduling policy.
Abstract: We consider the problem of distributed scheduling in wireless networks subject to simple collision constraints. We define the efficiency of a distributed scheduling algorithm to be the largest number (fraction) such that the throughput under the distributed scheduling policy is at least equal to the efficiency multiplied by the maximum throughput achievable under a centralized policy. For a general interference model, we prove a lower bound on the efficiency of a distributed scheduling algorithm by first assuming that all of the traffic only uses one hop of the network. We also prove that the lower bound is tight in the sense that, for any fraction larger than the lower bound, we can find a topology and an arrival rate vector within the fraction of the capacity region such that the network is unstable under a greedy scheduling policy. We then extend our results to a more general multihop traffic scenario and show that similar scheduling efficiency results can be established by introducing prioritization or regulators to the basic greedy scheduling algorithm

179 citations

Journal ArticleDOI
TL;DR: For the min sum vertex cover version of the problem, it is shown that it can be approximated within a ratio of 2, and is NP-hard to approximate within some constant ρ > 1.
Abstract: The input to the min sum set cover problem is a collection of n sets that jointly cover m elements. The output is a linear order on the sets, namely, in every time step from 1 to n exactly one set is chosen. For every element, this induces a first time step by which it is covered. The objective is to find a linear arrangement of the sets that minimizes the sum of these first time steps over all elements. We show that a greedy algorithm approximates min sum set cover within a ratio of 4. This result was implicit in work of Bar-Noy, Bellare, Halldorsson, Shachnai, and Tamir (1998) on chromatic sums, but we present a simpler proof. We also show that for every e > 0, achieving an approximation ratio of 4 – e is NP-hard. For the min sum vertex cover version of the problem (which comes up as a heuristic for speeding up solvers of semidefinite programs) we show that it can be approximated within a ratio of 2, and is NP-hard to approximate within some constant ρ > 1.

178 citations

Journal ArticleDOI
TL;DR: This paper describes the first ant colony optimization (ACO) approach applied to nurse scheduling, analyzing a dynamic regional problem which is currently under discussion at the Vienna hospital compound.

178 citations

Journal ArticleDOI
TL;DR: A new metric to assess the impact on power grid without solving complete power flow equations is proposed and a safeguard of profit is incorporated in the model to protect service providers from severe financial losses.
Abstract: This paper studies the problem of stochastic dynamic pricing and energy management policy for electric vehicle (EV) charging service providers. In the presence of renewable energy integration and energy storage system, EV charging service providers must deal with multiple uncertainties—charging demand volatility, inherent intermittency of renewable energy generation, and wholesale electricity price fluctuation. The motivation behind our work is to offer guidelines for charging service providers to determine proper charging prices and manage electricity to balance the competing objectives of improving profitability, enhancing customer satisfaction, and reducing impact on power grid in spite of these uncertainties. We propose a new metric to assess the impact on power grid without solving complete power flow equations. To protect service providers from severe financial losses, a safeguard of profit is incorporated in the model. Two algorithms—stochastic dynamic programming (SDP) algorithm and greedy algorithm (benchmark algorithm)—are applied to derive the pricing and electricity procurement policy. A Pareto front of the multi-objective optimization is derived. Simulation results show that using SDP algorithm can achieve up to 7% profit gain over using greedy algorithm. Additionally, we observe that the charging service provider is able to reshape spatial-temporal charging demands to reduce the impact on power grid via pricing signals.

178 citations

Journal ArticleDOI
TL;DR: A new hybrid algorithmic nature inspired approach based on Particle Swarm Optimization (PSO), Greedy Randomized Adaptive Search Procedure (GRASP) and Expanding Neighborhood Search (ENS) Strategy is proposed for the solution of the PTSP.

176 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023350
2022690
2021809
2020939
20191,006
2018967