Inverse multiple criteria sorting problem

Mousseau, Vincent; Özpeynirci, Özgür; Özpeynirci, Selin

doi:10.1007/s10479-017-2420-8

Cited by 18 publications

(18 citation statements)

References 33 publications

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“…Ters çok kriterli sıralama problemi literatüre ilk kez 2015 yılında sunulan bir konferans bildirisi [23] ile tanıtılmış, konuyla ilgili tek makale [7] 2018 yılında yayınlanmıştır. Bu çalışma ile önerilen problemin iki amaçlı durumu ilk kez ele alınmaktadır.…”

Section: Ters çOk Kri̇terli̇ Siralama Problemi̇ (Inverse Multi-criteria Sorting Problem)unclassified

İki amaçlı ters çok kriterli sıralama problemi için hedef programlama modeli

Ecer

Kabak

Dağdeviren

2020

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Bi-objective inverse multiple criteria sorting problem  Linear multiple criteria sorting model  Goal programming approach to obtain compromise solution between conflicting objectives Some kind of multi-criteria decision-making problems are interested in the assignment of objects to predetermined classes. Based on the existence of preference order between object classes, these problems are called as multi-criteria sorting or classification problems. Inverse multi-criteria sorting problem deals with the improvement of class assignments for objects to more preferred classes. In this study, a bi-objective formulation for inverse multi-criteria ranking problem is proposed. Solution to the model for a building energy efficiency labels improvement application is obtained by using goal programming approach to find a compromise solution between the improvement plan cost and the number of objects assigned to preferred classes. Figure A. Steps of modelling and solution process for the proposed model Purpose: The main aim of this study is to propose a new model for inverse multi-criteria sorting problem in order to extend the problem into multi-objective optimization context. To do so, a bi-objective model is proposed and solution to the model is obtained by goal programming approach. Theory and Methods: Inverse multi-criteria sorting problem aims to determine improvement strategies for assignment of objects to more preferred classes in sorting applications. The proposed model in this study is a multi-objective integer linear programming model. To obtain a compromise solution between two objectives, goal programming approach is utilized as the solution methodology. Results: A numerical example on the improvement of building energy efficiency labels is presented. According to the criteria used in the BEP-TR model, which measures the energy efficiency of buildings in Turkey, improvement plan is developed for energy labels of ten buildings. Conclusion: The first multi-objective model for inverse multi-criteria sorting problem is proposed in this study. Solution results seems promising for application of the problem in different areas.

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Section: Ters çOk Kri̇terli̇ Siralama Problemi̇ (Inverse Multi-criteria Sorting Problem)unclassified

İki amaçlı ters çok kriterli sıralama problemi için hedef programlama modeli

Ecer

Kabak

Dağdeviren

2020

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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“…Classification has been widely studied in the literature because of its applicability to a wide variety of problems (Alsabti, Ranka, & Singh, ; Bodley & Utgoff, ; Breiman, Friedman, & Stone, ; Breslow, ; Duda, Hart, & Stork, ; Friedman, ; Gehrke, Ganti, Ramakrishnan, & Loh, ; James, ; Quinlan, ; Zopounidis & Doumpos, ). The multicriteria inverse classification problem (Aggarwal et al., , ; Li et al., ) stands on the idea that changes in the independent variables of a system solution are searched so that it can be classified into a more desirable class with respect to the given system criteria (Lin et al., ; Mannino & Koushik, ; Mousseau et al., ; Pendharkar, ). Specifically, in this work we aim to identify a set of protective actions to reduce the vulnerability of a (group of) safety‐critical system(s) under budget limitations.…”

Section: Inverse Classification Problem For Protective Actions Identimentioning

confidence: 99%

“…In particular, the empirical classification model developed in Wang et al. () is tailored to address the corresponding inverse (classification) problem (Aggarwal, Chen, & Han, , ; Ahuja & Orlin, , ; Heuberger, ; Li, Zhou, & Zhang, ; Lin, Kuo, Hsieh, & Wang, ; Mannino & Koushik, ; Mousseau, Özpeynirci, & Özpeynirci, ; Mousseau & Slowinski, ; Pendharkar, ), i.e., the problem of determining a set of protective actions (Doumpos & Zopounidis, ; Larsson, ), which can effectively reduce the vulnerability class of (a group of) safety‐critical systems (Aven & Flage, ), taking into account a specified set of constraints (e.g., budget limits; Aggarwal et al., ).…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we are still concerned with intentional hazards (i.e., those related to malevolent acts) and address issue (iii) above, i.e., the definition of the actions to undertake for reducing the level of system vulnerability. In particular, the empirical classification model developed in Wang et al (2013) is tailored to address the corresponding inverse (classification) problem (Aggarwal, Chen, & Han, 2006, 2010Ahuja & Orlin, 2001, 2002Heuberger, 2004;Li, Zhou, & Zhang, 2012;Lin, Kuo, Hsieh, & Wang, 2009;Mannino & Koushik, 2000;Mousseau,Özpeynirci, &Özpeynirci, 2018;Mousseau & Slowinski, 1998;Pendharkar, 2002), i.e., the problem of determining a set of protective actions Larsson, 1992), which can effectively reduce the vulnerability class of (a group of) safety-critical systems (Aven & Flage, 2009), taking into account a specified set of constraints (e.g., budget limits; Aggarwal et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Identification of Protective Actions to Reduce the Vulnerability of Safety‐Critical Systems to Malevolent Intentional Acts: An Optimization‐Based Decision‐Making Approach

et al. 2019

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An empirical classification model based on the Majority Rule Sorting (MR‐Sort) method has been previously proposed by the authors to evaluate the vulnerability of safety‐critical systems (in particular, nuclear power plants [NPPs]) with respect to malevolent intentional acts. In this article, the model serves as the basis for an analysis aimed at determining a set of protective actions to be taken (e.g., increasing the number of monitoring devices, reducing the number of accesses to the safety‐critical system) in order to effectively reduce the level of vulnerability of the safety‐critical systems under consideration. In particular, the problem is here tackled within an optimization framework: the set of protective actions to implement is chosen as the one minimizing the overall level of vulnerability of a group of safety‐critical systems. In this context, three different optimization approaches have been explored: (i) one single classification model is built to evaluate and minimize system vulnerability; (ii) an ensemble of compatible classification models, generated by the bootstrap method, is employed to perform a “robust” optimization, taking as reference the “worst‐case” scenario over the group of models; (iii) finally, a distribution of classification models, still obtained by bootstrap, is considered to address vulnerability reduction in a “probabilistic” fashion (i.e., by minimizing the “expected” vulnerability of a fleet of systems). The results are presented and compared with reference to a fictitious example considering NPPs as the safety‐critical systems of interest.

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“…Deciding which additional courses to open to improve students' grades is a real‐life application of the inverse multiple criteria sorting problem. Mousseau, Özpeynirci, and Özpeynirci (2018) provide a detailed example of improving students' grades with additional courses, and other real‐life examples of inverse multiple criteria sorting problem.…”

Section: Introductionmentioning

confidence: 99%

An interactive approach for inverse multiple criteria sorting problem

Özpeynirci

Mousseau

2020

Multi Criteria Decision Anal

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Multiple criteria sorting is the problem of assigning objects that are evaluated on multiple criteria to ordered classes. In inverse multiple criteria sorting problem, the assignment of objects to classes is known. The decision maker (DM) can implement actions to improve the scores of objects on criteria, thus enabling the assignment of objects to better classes. In inverse multiple criteria sorting problems, two types of questions arise: (1) What is the set of actions that will result in the desired classification with minimum cost? and 2) What is the set of actions to obtain the best classification without exceeding a predetermined budget? There exist two versions for each question: (i) Simple version, where the parameters of the sorting method are known and (ii) Robust version, where the parameters are not fully known. In this article, we focus on the case where the DM does not provide any budget limitation or desired classifications a priori. By implementing more actions, that is, by spending more money, better classifications may be obtained. We aim to analyse the trade‐off between cost and classification, and determine the set of actions that will result in the most preferred cost‐classification compromise by the DM. We develop an approach that proceeds interactively with the DM, and determines such most preferred set of actions. The developed approach assumes the simple version and uses MR‐Sort as sorting model. We provide a detailed illustrative example on a real life problem and present the progress of the interactive algorithm.

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Inverse multiple criteria sorting problem

Cited by 18 publications

References 33 publications

İki amaçlı ters çok kriterli sıralama problemi için hedef programlama modeli

İki amaçlı ters çok kriterli sıralama problemi için hedef programlama modeli

Identification of Protective Actions to Reduce the Vulnerability of Safety‐Critical Systems to Malevolent Intentional Acts: An Optimization‐Based Decision‐Making Approach

An interactive approach for inverse multiple criteria sorting problem

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