A probabilistic greedy search algorithm for combinatorial optimisation with application to the set covering problem

Haouari, Mohamed; Chaouachi, Jouhaina

doi:10.1057/palgrave.jors.2601366

Cited by 39 publications

(22 citation statements)

References 27 publications

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“…Random and probabilistic greedy approximate algorithms [7,8,9] produce better solutions than the classical greedy algorithm for set covering problem. Randomized greedy algorithm used by Grossman and Wool [2] is same as classical greedy algorithm except that ties are broken at random and the basic algorithm is repeated N times and returns the best solution among the N solutions.…”

Section: Algorithm Gmc(sk)mentioning

confidence: 99%

Big Step Greedy Heuristic for Maximum Coverage Problem

Chandu¹

2015

IJCA

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This paper proposes a greedy heuristic called Big step greedy heuristic and investigates its application to compute approximate solution for maximum coverage problem. Greedy algorithms construct the solution in multiple steps, the classical greedy algorithm for maximum coverage problem, in each step selects one set that contains the greatest number of uncovered elements. The Big step greedy heuristic, in each step selects p (1 <= p <= k) sets such that the union of selected p sets contains the greatest number of uncovered elements by evaluating all the possible p-combinations of given sets. When p=k Big step greedy algorithm behaves like an exact algorithm that computes optimal solution by evaluating all possible k-combinations of the given sets. When p=1 it behaves like the classical greedy algorithm. The Big step greedy heuristic can be combined with local search methods to compute better approximate solution.

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Section: Algorithm Gmc(sk)mentioning

confidence: 99%

Big Step Greedy Heuristic for Maximum Coverage Problem

Chandu¹

2015

IJCA

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“…An adopted strategy to conserve sensor energy is based on an approximate data collection approach [6]. This approach attempts to exploit the spatial correlations among the sensor readings by selecting a subset of representative sensor nodes (referred to as R-nodes) to report their readings to the sink and estimating the readings of the remaining sensors as the readings of the corresponding R-nodes [7] [6] [8] [9] [10] [11] [12].…”

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confidence: 99%

“…Liu et al [9] formulated the problem as a clique-covering problem that partition the sensor nodes into cliques with similar readings. It is reported in [6] that the clique-covering based method, named EEDC, performs better than the snapshot query in term of the number of R-nodes selected. More recently, Hung et al [6] viewed the problem of selecting R-nodes as an energy-aware set covering problem.…”

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confidence: 99%

“…It is reported in [6] that the clique-covering based method, named EEDC, performs better than the snapshot query in term of the number of R-nodes selected. More recently, Hung et al [6] viewed the problem of selecting R-nodes as an energy-aware set covering problem. In this problem, sensor nodes with higher energy and wider data coverage ranges are selected as the R-nodes for approximate data collection.…”

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confidence: 99%

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Extending Network Lifetime of Wireless Sensor Networks

Amiri¹

2015

IJCNC

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“…Mcgovern et al [34] used a greedy algorithm for a combinatorial optimization analysis of the unary NP-complete disassembly line balancing problem. Other analysis on similar problems with greedy algorithms can be found in the research work of Agnihotri [35], Angelopoulos [36], among others [37,38].…”

Section: Empirical Studies Of Span Of Controlmentioning

confidence: 99%

A Quantitative Approach to the Organizational Design Problem

Mena¹

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This research takes a quantitative approach to this problem and formulates it as a binary integer model, which is used as a tool to study the organizational design issue. This model considers a range of requirements affecting management and supervision of a given set of jobs in a company. These decision variables include allocation of jobs to workers, considering complexity and compatibility of each job with respect to workers, and the requirement of management for planning, execution, training, and control activities in a hierarchical organization. The objective of the model is minimal operations cost, which is the sum of supervision costs at each level of the hierarchy, and the costs of workers assigned to jobs. vi The model is intended for application in the make-to-order industries as a design tool. It could also be applied to make-to-stock companies as an evaluation tool, to assess the optimality of their current organizational structure.Extensive experiments were conducted to validate the model, to study its behavior, and to evaluate the impact of changing parameters with practical problems. This research proposes a meta-heuristic approach to solving large-size problems, based on the concept of greedy algorithms and the Meta-RaPS algorithm. The proposed heuristic was evaluated with two measures of performance: solution quality and computational speed.The quality is assessed by comparing the obtained objective function value to the one achieved by the optimal solution. The computational efficiency is assessed by comparing the computer time used by the proposed heuristic to the time taken by a commercial software system. Test results show the proposed heuristic procedure generates good solutions in a time-efficient manner.

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A probabilistic greedy search algorithm for combinatorial optimisation with application to the set covering problem

Cited by 39 publications

References 27 publications

Big Step Greedy Heuristic for Maximum Coverage Problem

Big Step Greedy Heuristic for Maximum Coverage Problem

Extending Network Lifetime of Wireless Sensor Networks

A Quantitative Approach to the Organizational Design Problem

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