Constructing a strict total order for alternatives characterized by multiple criteria: An extension

Dehnokhalaji, Akram; Korhonen, Pekka; Köksalan, Murat; Nasrabadi, Nasim; Öztürk, Diclehan Tezcaner; Wallenius, Jyrki

doi:10.1002/nav.21573

Cited by 7 publications

(19 citation statements)

References 19 publications

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“…More specifically, the methodology defined in [27], [29], [30] is adopted for this purpose in the proposed algorithm. It constructs convex cones based on the DM's preference and finds the relations between the solutions and the all cones to classify the solutions into ordered classes.…”

Section: A Cone Dominated Sortingmentioning

confidence: 99%

Cone Dominance-Based Interactive Evolutionary Multiobjective Algorithm for QoS-Driven Service Selection Problem

Purevsuren

Rehman

Chen

et al. 2014

2014 IEEE 17th International Conference on Computational Science and Engineering

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QoS-driven Service Selection (QSS) problem can be modeled as an optimization problem which possesses multiple objectives. The QSS problem is a NP-hard problem in the combinatorial optimization field. The existing approaches for solving this problem are inefficient for multi-objective scenarios. To address the multiple objective scenario, we propose an interactive evolutionary multi-objective optimization (EMO) algorithm based on the cone dominance. This algorithm requires periodical interaction from the user to get their preference information. The preference information drives the search process toward a preferred part of the search space. The performance of the proposed algorithm is assessed for two and three objectives problem sets and the results demonstrate its ability to converge to the most preferred point. The evaluation of results indicate that the proposed approach is more efficient compared to NSGA-II in terms of the number of iterations required to reach the preferred point.

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Section: A Cone Dominated Sortingmentioning

confidence: 99%

Cone Dominance-Based Interactive Evolutionary Multiobjective Algorithm for QoS-Driven Service Selection Problem

Purevsuren

Rehman

Chen

et al. 2014

2014 IEEE 17th International Conference on Computational Science and Engineering

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“…In the two papers outlined below (Dehnokhalaji et al 2011(Dehnokhalaji et al , 2014, we use the convex cone idea to generate a partial order and a total order of a finite set of alternatives. Ranking has many possible applications.…”

Section: Rankingmentioning

confidence: 99%

“…An important issue in DEA is to rank order efficient units. Based on the approach proposed by Dehnokhalaji et al (2014) for constructing a total order for alternatives evaluated in terms of multiple criteria, Dehnokhalaji et al (2017) developed a method for ranking units in DEA. They implemented and modified the radial model formulated by Dehnokhalaji et al (2014) to obtain a non-radial measure, which is more stable in practice.…”

Section: Dehnokhalaji Et Al (2017)mentioning

confidence: 99%

“…Based on the approach proposed by Dehnokhalaji et al (2014) for constructing a total order for alternatives evaluated in terms of multiple criteria, Dehnokhalaji et al (2017) developed a method for ranking units in DEA. They implemented and modified the radial model formulated by Dehnokhalaji et al (2014) to obtain a non-radial measure, which is more stable in practice. The main idea of their model is to construct preference cones and preference polyhedra in the context of DEA and then find the distance of the unit under assessment to the convex cone/the polyhedral in terms of L − 1 norm.…”

Section: Dehnokhalaji Et Al (2017)mentioning

confidence: 99%

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Using convex preference cones in multiple criteria decision making and related fields

et al. 2019

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This paper reviews our own and colleagues' research on using convex preference cones in multiple criteria decision making and related fields. The original paper by Korhonen, Wallenius, and Zionts was published in Management Science in 1984. We first present the underlying theory, concepts, and method. Then we discuss applications of the theory, particularly for finding the most preferred alternative, finding a partial and total rank ordering of alternatives, as well as developing algorithms for solving multi-objective integer and other optimization problems.

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“… The ideas presented in Dehnokhalaji, Korhonen, Köksalan, Nasrabadi, and Wallenius () and Dehnokhalaji et al. () will further improve the rank order.…”

mentioning

confidence: 99%

Impact of Number of Interactions, Different Interaction Patterns, and Human Inconsistencies on Some Hybrid Evolutionary Multiobjective Optimization Algorithms*

et al. 2015

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We investigate the impact of the number of human–computer interactions, different interaction patterns, and human inconsistencies in decision maker responses on the convergence of an interactive, evolutionary multiobjective algorithm recently developed by the authors. In our context “an interaction” means choosing the best and worst solutions among a sample of six solutions. By interaction patterns we refer to whether preference questioning is more front‐, center‐, rear‐, or edge‐loaded. As test problems we use two‐ to four‐objective knapsack problems, multicriteria scheduling problems, and multiobjective facility location problems. In the tests, two different preference functions are used to represent actual decision maker preferences, linear and Chebyshev. The results indicate that it is possible to obtain solutions that are very good or even nearly optimal with a reasonable number of interactions. The results also indicate that the algorithm is robust to minor inconsistencies in decision maker responses. There is also surprising robustness toward different patterns of interaction with the decision maker. The results are of interest to the evolutionary multiobjective (EMO) community actively developing hybrid interactive EMO approaches.

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Constructing a strict total order for alternatives characterized by multiple criteria: An extension

Cited by 7 publications

References 19 publications

Cone Dominance-Based Interactive Evolutionary Multiobjective Algorithm for QoS-Driven Service Selection Problem

Cone Dominance-Based Interactive Evolutionary Multiobjective Algorithm for QoS-Driven Service Selection Problem

Using convex preference cones in multiple criteria decision making and related fields

Impact of Number of Interactions, Different Interaction Patterns, and Human Inconsistencies on Some Hybrid Evolutionary Multiobjective Optimization Algorithms*

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