A TOPSIS–Based Cooperative Strategy for Solving Imprecise MCDM Problems

Juan Carlos Figueroa-García; Roman Neruda; Yurilev Chalco-Cano; Carlos Alberto Franco Franco

doi:10.1007/978-3-032-29000-7_10

A TOPSIS–Based Cooperative Strategy for Solving Imprecise MCDM Problems

Juan Carlos Figueroa-García
, Roman Neruda
, Yurilev Chalco-Cano
, Carlos Alberto Franco Franco

Department of Mathematics

Universidad Distrital Francisco José de Caldas
Charles University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The imprecise TOPSIS method has attracted attention in the last years from different perspectives. Most of current methods are based on choosing a set of crisp scores such as midpoints, expected values, pessimistic/optimistic scores, etc. from a set of imprecise ones to then compute the TOPSIS method and select the best alternative. So, to deal with both homogeneous decisions/information we propose a cooperative strategy for integrating partial TOPSIS decisions into a context-based convex method to finally provide an aggregated decision. The proposed strategy is explained and applied to an application example.

Original language	English
Title of host publication	Information Processing and Management of Uncertainty in Knowledge-Based Systems - 21st International Conference, IPMU 2026, Proceedings
Editors	Barbara Vantaggi, Davide Petturiti, Giulianella Coletti, Thierry Denoeux, Anne Laurent, Enrique Miranda, Jesús Medina, Bernadette Bouchon-Meunier, Ronald R. Yager
Publisher	Springer Nature
Pages	135-143
Number of pages	9
ISBN (Print)	9783032289995
DOIs	https://doi.org/10.1007/978-3-032-29000-7_10
State	Published - 2026
Event	21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2026 - Rome, Italy Duration: 15 Jun 2026 → 19 Jun 2026

Publication series

Name	Communications in Computer and Information Science
Volume	3021 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2026
Country/Territory	Italy
City	Rome
Period	15/06/26 → 19/06/26

Access to Document

10.1007/978-3-032-29000-7_10

Cite this

Figueroa-García, J. C., Neruda, R., Chalco-Cano, Y., & Franco Franco, C. A. (2026). A TOPSIS–Based Cooperative Strategy for Solving Imprecise MCDM Problems. In B. Vantaggi, D. Petturiti, G. Coletti, T. Denoeux, A. Laurent, E. Miranda, J. Medina, B. Bouchon-Meunier, & R. R. Yager (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems - 21st International Conference, IPMU 2026, Proceedings (pp. 135-143). (Communications in Computer and Information Science; Vol. 3021 CCIS). Springer Nature. https://doi.org/10.1007/978-3-032-29000-7_10

Figueroa-García, Juan Carlos ; Neruda, Roman ; Chalco-Cano, Yurilev et al. / A TOPSIS–Based Cooperative Strategy for Solving Imprecise MCDM Problems. Information Processing and Management of Uncertainty in Knowledge-Based Systems - 21st International Conference, IPMU 2026, Proceedings. editor / Barbara Vantaggi ; Davide Petturiti ; Giulianella Coletti ; Thierry Denoeux ; Anne Laurent ; Enrique Miranda ; Jesús Medina ; Bernadette Bouchon-Meunier ; Ronald R. Yager. Springer Nature, 2026. pp. 135-143 (Communications in Computer and Information Science).

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abstract = "The imprecise TOPSIS method has attracted attention in the last years from different perspectives. Most of current methods are based on choosing a set of crisp scores such as midpoints, expected values, pessimistic/optimistic scores, etc. from a set of imprecise ones to then compute the TOPSIS method and select the best alternative. So, to deal with both homogeneous decisions/information we propose a cooperative strategy for integrating partial TOPSIS decisions into a context-based convex method to finally provide an aggregated decision. The proposed strategy is explained and applied to an application example.",

author = "Figueroa-Garc{\'i}a, \{Juan Carlos\} and Roman Neruda and Yurilev Chalco-Cano and \{Franco Franco\}, \{Carlos Alberto\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2026 ; Conference date: 15-06-2026 Through 19-06-2026",

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editor = "Barbara Vantaggi and Davide Petturiti and Giulianella Coletti and Thierry Denoeux and Anne Laurent and Enrique Miranda and Jes{\'u}s Medina and Bernadette Bouchon-Meunier and Yager, \{Ronald R.\}",

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Figueroa-García, JC, Neruda, R, Chalco-Cano, Y & Franco Franco, CA 2026, A TOPSIS–Based Cooperative Strategy for Solving Imprecise MCDM Problems. in B Vantaggi, D Petturiti, G Coletti, T Denoeux, A Laurent, E Miranda, J Medina, B Bouchon-Meunier & RR Yager (eds), Information Processing and Management of Uncertainty in Knowledge-Based Systems - 21st International Conference, IPMU 2026, Proceedings. Communications in Computer and Information Science, vol. 3021 CCIS, Springer Nature, pp. 135-143, 21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2026, Rome, Italy, 15/06/26. https://doi.org/10.1007/978-3-032-29000-7_10

A TOPSIS–Based Cooperative Strategy for Solving Imprecise MCDM Problems. / Figueroa-García, Juan Carlos; Neruda, Roman; Chalco-Cano, Yurilev et al.
Information Processing and Management of Uncertainty in Knowledge-Based Systems - 21st International Conference, IPMU 2026, Proceedings. ed. / Barbara Vantaggi; Davide Petturiti; Giulianella Coletti; Thierry Denoeux; Anne Laurent; Enrique Miranda; Jesús Medina; Bernadette Bouchon-Meunier; Ronald R. Yager. Springer Nature, 2026. p. 135-143 (Communications in Computer and Information Science; Vol. 3021 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A TOPSIS–Based Cooperative Strategy for Solving Imprecise MCDM Problems

AU - Figueroa-García, Juan Carlos

AU - Neruda, Roman

AU - Chalco-Cano, Yurilev

AU - Franco Franco, Carlos Alberto

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026

Y1 - 2026

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AB - The imprecise TOPSIS method has attracted attention in the last years from different perspectives. Most of current methods are based on choosing a set of crisp scores such as midpoints, expected values, pessimistic/optimistic scores, etc. from a set of imprecise ones to then compute the TOPSIS method and select the best alternative. So, to deal with both homogeneous decisions/information we propose a cooperative strategy for integrating partial TOPSIS decisions into a context-based convex method to finally provide an aggregated decision. The proposed strategy is explained and applied to an application example.

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ER -

Figueroa-García JC, Neruda R, Chalco-Cano Y, Franco Franco CA. A TOPSIS–Based Cooperative Strategy for Solving Imprecise MCDM Problems. In Vantaggi B, Petturiti D, Coletti G, Denoeux T, Laurent A, Miranda E, Medina J, Bouchon-Meunier B, Yager RR, editors, Information Processing and Management of Uncertainty in Knowledge-Based Systems - 21st International Conference, IPMU 2026, Proceedings. Springer Nature. 2026. p. 135-143. (Communications in Computer and Information Science). doi: 10.1007/978-3-032-29000-7_10

A TOPSIS–Based Cooperative Strategy for Solving Imprecise MCDM Problems

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