Clustered maximum weight clique problem: Algorithms and empirical analysis

Malladi, Krishna T.; Mitrović-Minić, Snežana; Punnen, Abraham P.

doi:10.1016/j.cor.2017.04.002

Cited by 14 publications

(20 citation statements)

References 21 publications

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“…The authors developed an algorithm based on ant colony optimization with two heuristics for constraint handling and compared the obtained solutions with those gained from a CPLEX solver. Malladi et al [4] proved that the SIASP is equivalent to a piece-wise linear clustered maximum weight clique problem. The authors developed a matheuristic based on a K-swap neighborhood search and tabu search algorithm to obtain a schedule.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Appendix A.1 ELECTRE-III ELECTRE [4,6] is an abrreviation for Elimination and Choice Expressing Reality, and there are multiple versions focusing on either selection, ranking, or sorting. ELECTRE belongs to the outranking class of MCDM, and mainly consists of two phases; first, the construction of one or several outranking relations, which in a comprehensive way compares each pair of information; second, an exploitation procedure that analyses the recommendations obtained in the first phase.…”

Section: Fundingmentioning

confidence: 99%

“…In the Satellite Image Acquisition Scheduling Problem (SIASP), the optimal schedule is defined as the set of requests that do not violate any physical constraints, while considering the numerous objectives associated with the decision-making environment [1][2][3][4]. Such objectives include maximizing customer satisfaction and maximizing the number of acquired high quality images with the consideration of the uncertainty of cloud coverage.…”

Section: Introductionmentioning

confidence: 99%

“…Having obtained the infeasibility matrices, we now face the challenge of constructing the feasibility constraint. From Section 2 it is found that the infeasibility constraint is generally included through a pairwise infeasibility constraint or by modelling the SIASP as a maximum clique problem, see, for example [1,4]. Either way the infeasibility constraint is one of the most computationally expensive constraints to consider.…”

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confidence: 99%

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Multi Criteria Decision Making for the Multi-Satellite Image Acquisition Scheduling Problem

Vasegaard

Picard

Hennart

et al. 2020

Sensors

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The multi-satellite image acquisition scheduling problem is traditionally seen as a complex optimization problem containing a generic objective function that represents the priority structure of the satellite operator. However, the majority of literature neglect the collective and contemporary effect of factors associated with the operational goal in the objective function, i.e., uncertainty in cloud cover, customer priority, image quality criteria, etc. Consequently, the focus of the article is to integrate a real-time scoring approach of imaging attempts that considers these aspects. This is accomplished in a multi-satellite planning environment, through the utilization of the multi-criteria decision making (MCDM) models, Elimination and Choice Expressing Reality (ELECTRE-III) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the formulation of a binary linear programming model. The two scoring approaches belong to different model classes of MCDM, respectively an outranking approach and a distance to ideal point approach, and they are compared with a naive approach. Numerical experiments are conducted to validate the models and illustrate the importance of criteria neglected in previous studies. The results demonstrate the customized behaviour allowed by MCDM methods, especially the ELECTRE-III approach.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Fundingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Multi Criteria Decision Making for the Multi-Satellite Image Acquisition Scheduling Problem

Vasegaard

Picard

Hennart

et al. 2020

Sensors

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“…This rule, together with a similar rule for allocating weights to edges for the edge-weighted variant of the problem, is very widely used [2][3][4][20][21][22]25,27,29,32,34,[41][42][43]45,55,61,62,65,66, and many more], often as the only way of evaluating a solver. It has also recently been adopted for large sparse graphs [10,20,29,62], and for benchmarking the minimum weight dominating set problem [63].…”

Section: Current Practices In Benchmarkingmentioning

confidence: 99%

On Maximum Weight Clique Algorithms, and How They Are Evaluated

McCreesh

Prosser

Simpson

et al. 2017

Lecture Notes in Computer Science

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Abstract. Maximum weight clique and maximum weight independent set solvers are often benchmarked using maximum clique problem instances, with weights allocated to vertices by taking the vertex number mod 200 plus 1. For constraint programming approaches, this rule has clear implications, favouring weight-based rather than degree-based heuristics. We show that similar implications hold for dedicated algorithms, and that additionally, weight distributions affect whether certain inference rules are cost-effective. We look at other families of benchmark instances for the maximum weight clique problem, coming from winner determination problems, graph colouring, and error-correcting codes, and introduce two new families of instances, based upon kidney exchange and the Research Excellence Framework. In each case the weights carry much more interesting structure, and do not in any way resemble the 200 rule. We make these instances available in the hopes of improving the quality of future experiments.

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