Ranking and selection for multiple performance measures using incomplete preference information

Mattila, Ville; Virtanen, Kai

doi:10.1016/j.ejor.2014.10.028

Cited by 20 publications

(10 citation statements)

References 31 publications

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“…In this section, the application of the MOCBA-p procedure (Mattila and Virtanen 2013) in solving the decoy location problem is described. First, let us formally state the problem at hand.…”

Section: Simulation Model Providing Distance Objectivesmentioning

confidence: 99%

“…Following the procedure, the probability of correctly selecting these combinations is maximized. Detailed description of the procedure is given in Mattila and Virtanen (2013).…”

Section: Determining Pairwise Non-dominated Location Combinationsmentioning

confidence: 99%

“…, n and at least one of the inequalities is strict. Details of the justification for the use of the rules as well as the description of the rules are omitted in this paper for brevity but they are available in Mattila and Virtanen (2013).…”

Section: Determining Pairwise Non-dominated Location Combinationsmentioning

confidence: 99%

“…In order to protect the radar successfully, some of the decoys may have to be sacrificed. The problem is solved by using a stochastic simulation model as well as a multi-objective R&S procedure called MOCBA-p presented in Mattila and Virtanen (2013). The simulation model describes the capability of the ARM's seeker to locate the radar.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing locations of decoys for protecting surface-based radar against anti-radiation missile with multi-objective ranking and selection

Mattila¹,

Virtanen²,

Muttilainen³

et al. 2014

Proceedings of the Winter Simulation Conference 2014

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This paper considers the decoy location problem, i.e., the problem of determining optimal locations for decoys that protect a surface-based radar against an anti-radiation missile. The objectives of the problem are to simultaneously maximize distances between the missile's detonation point and the radar as well as the decoys. The problem is solved using a stochastic simulation model providing the distances as well as a ranking and selection procedure called MOCBA-p. In the procedure, location combinations are evaluated through a multi-attribute utility function with incomplete preference information regarding weights related to the objectives. In addition, multi-objective computing budget allocation is used for allocating simulation replications such that the best combinations are selected correctly with high confidence. Numerical experiments presented in the paper illustrate the suitability of MOCBA-p for solving the decoy location problem. It provides computational advantages over an alternative procedure while also enabling ease of determining the weights.

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“…In this section, the application of the MOCBA-p procedure (Mattila and Virtanen 2013) in solving the decoy location problem is described. First, let us formally state the problem at hand.…”

Section: Simulation Model Providing Distance Objectivesmentioning

confidence: 99%

“…Following the procedure, the probability of correctly selecting these combinations is maximized. Detailed description of the procedure is given in Mattila and Virtanen (2013).…”

Section: Determining Pairwise Non-dominated Location Combinationsmentioning

confidence: 99%

Section: Determining Pairwise Non-dominated Location Combinationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing locations of decoys for protecting surface-based radar against anti-radiation missile with multi-objective ranking and selection

Mattila¹,

Virtanen²,

Muttilainen³

et al. 2014

Proceedings of the Winter Simulation Conference 2014

View full text Add to dashboard Cite

show abstract

“…On the other hand, because of complexity of decision problems and the limited ability of human pairwise comparisons, DMs may be unable to provide their preferences over some pairs of decision alternatives. As such, incomplete judgments are resulted and decision making problems with incomplete preference relations have been received more and more attention in recent years (Mattila & Virtanen, 2015;Punkka & Salo, 2013;Wang & Li, 2015). The consistency of preference relations is a crucial foundation for estimating missing values and obtaining a reasonable and reliable decision result (Brunelli, Canal, & Fedrizzi, 2013;Brunelli & Fedrizzi, 2015;Kou, Ergu, & Shang, 2014).…”

Section: Introductionmentioning

confidence: 99%

A note on “A goal programming model for incomplete interval multiplicative preference relations and its application in group decision-making”

Wang

2015

European Journal of Operational Research

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Technology Resolution Criterion of Uncertainty in Intelligent Distributed Decision Support Systems

Pavlov

Zakharov

2019

Intelligent Distributed Computing XIII

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Ranking and selection for multiple performance measures using incomplete preference information

Cited by 20 publications

References 31 publications

Optimizing locations of decoys for protecting surface-based radar against anti-radiation missile with multi-objective ranking and selection

Optimizing locations of decoys for protecting surface-based radar against anti-radiation missile with multi-objective ranking and selection

A note on “A goal programming model for incomplete interval multiplicative preference relations and its application in group decision-making”

Technology Resolution Criterion of Uncertainty in Intelligent Distributed Decision Support Systems

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