A hybrid MOO/MCDM optimization approach to improve decision-making in multiobjective optimization
Multiobjective optimization (MOO) and multicriteria decision-making (MCDM) are critical disciplines in operations research, aiming to assist decision-makers in making the best decisions in complex problems. Nevertheless, the hybridization of the process has yet to be explored. In this case, the hybr...
- Autores:
-
Neira Rodado, Dionicio
Jimenez Delgado, Genett
Crespo, Fernando
Morales Espinosa, Roberto Antonio
Plazas Alvarez, Jonny Rafael
Hernandez, Hugo
- Tipo de recurso:
- Conferencia (Ponencia)
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/14242
- Acceso en línea:
- https://hdl.handle.net/11323/14242
https://repositorio.cuc.edu.co/
- Palabra clave:
- MCDM
Multiobjective Optimization
Pareto set
TOPSIS
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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A hybrid MOO/MCDM optimization approach to improve decision-making in multiobjective optimization |
| title |
A hybrid MOO/MCDM optimization approach to improve decision-making in multiobjective optimization |
| spellingShingle |
A hybrid MOO/MCDM optimization approach to improve decision-making in multiobjective optimization MCDM Multiobjective Optimization Pareto set TOPSIS |
| title_short |
A hybrid MOO/MCDM optimization approach to improve decision-making in multiobjective optimization |
| title_full |
A hybrid MOO/MCDM optimization approach to improve decision-making in multiobjective optimization |
| title_fullStr |
A hybrid MOO/MCDM optimization approach to improve decision-making in multiobjective optimization |
| title_full_unstemmed |
A hybrid MOO/MCDM optimization approach to improve decision-making in multiobjective optimization |
| title_sort |
A hybrid MOO/MCDM optimization approach to improve decision-making in multiobjective optimization |
| dc.creator.fl_str_mv |
Neira Rodado, Dionicio Jimenez Delgado, Genett Crespo, Fernando Morales Espinosa, Roberto Antonio Plazas Alvarez, Jonny Rafael Hernandez, Hugo |
| dc.contributor.author.none.fl_str_mv |
Neira Rodado, Dionicio Jimenez Delgado, Genett Crespo, Fernando Morales Espinosa, Roberto Antonio Plazas Alvarez, Jonny Rafael Hernandez, Hugo |
| dc.subject.proposal.eng.fl_str_mv |
MCDM Multiobjective Optimization Pareto set TOPSIS |
| topic |
MCDM Multiobjective Optimization Pareto set TOPSIS |
| description |
Multiobjective optimization (MOO) and multicriteria decision-making (MCDM) are critical disciplines in operations research, aiming to assist decision-makers in making the best decisions in complex problems. Nevertheless, the hybridization of the process has yet to be explored. In this case, the hybridization of the decision process is analyzed to evaluate the solution set obtained with this approach and compare it against the solutions obtained with Pareto Set. This novel approach shows that according to the decision-maker preferences, solutions could be in this solution set despite not being included in the Pareto Set. This approach gives alternatives to decision-makers without moving apart much from the best solution. A flow shop is used as a numerical example to compare the Pareto Set and hybrid approach outcomes. |
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2023 |
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2023-11-21 |
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2025-05-16T20:05:44Z |
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2025-05-16T20:05:44Z |
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Documento de Conferencia |
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Neira-Rodado, D., Jimenez-Delgado, G., Crespo, F., Morales Espinosa, R.A., Plazas Alvarez, J.R., Hernandez, H. (2023). A Hybrid MOO/MCDM Optimization Approach to Improve Decision-Making in Multiobjective Optimization. In: Mori, H., Asahi, Y., Coman, A., Vasilache, S., Rauterberg, M. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14056. Springer, Cham. https://doi.org/10.1007/978-3-031-48044-7_8 |
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https://hdl.handle.net/11323/14242 |
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10.1007/978-3-031-48044-7_8 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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Neira-Rodado, D., Jimenez-Delgado, G., Crespo, F., Morales Espinosa, R.A., Plazas Alvarez, J.R., Hernandez, H. (2023). A Hybrid MOO/MCDM Optimization Approach to Improve Decision-Making in Multiobjective Optimization. In: Mori, H., Asahi, Y., Coman, A., Vasilache, S., Rauterberg, M. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14056. Springer, Cham. https://doi.org/10.1007/978-3-031-48044-7_8 10.1007/978-3-031-48044-7_8 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
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Drumond, P., de Araújo Costa, I.P., Moreira, M.Â.L., Dos Santos, M., Gomes, C.F.S., Do Nascimento Maêda, S.M. Strategy study to prioritize marketing criteria: An approach in the light of the DEMATEL method (2022) Procedia Comput. Sci., 199, pp. 448-455. Quezada, L.E., López-Ospina, H.A., Ortiz, C., Oddershede, A.M., Palominos, P.I., Jofré, P.A. A DEMATEL-based method for prioritizing strategic projects using the perspectives of the Balanced Scorecard (2022) Int. J. Prod. Econ., 249. Wang, P., Zhu, Z., Huang, S. The use of improved TOPSIS method based on experimental design and Chebyshev regression in solving MCDM problems (2017) J. Intell. Manuf., 28 (1), pp. 229-243. Çelikbilek, Y., Tüysüz, F. An in-depth review of theory of the TOPSIS method: An experimental analysis (2020) J. Manag. Anal., 7 (2), pp. 281-300. Ortiz-Barrios, M.A., de Felice, F., Parra Negrete, K., Aleman Romero, B., Yaruro Arenas, A., Petrillo, A. An AHP-topsis integrated model for selecting the most appropriate tomography equipment (2016) Int. J. Inf. Technol. Decis. Mak., 15 (4), pp. 861-885. Rodriguez, J.V., Rodado, D.N., Parody, A., Crespo, F., Brugés-Ballesteros, L. (2021) Addressing the Permutational Flow Shop Scheduling Problem through Constructive Heuristics: A Statistical Comparison, pp. 169-181. Marler, R., Arora, J. Survey of multi-objective optimization methods for engineering (2004) Struct. Multidisc. Optim., 26, pp. 369-395. Aranda, J.A., Orjuela, J.A. Optimización multiobjetivo en la gestión de cadenas de suministro de biocombustibles. Una revisión de la literature Ingenierıa, 20 (1), pp. 37-63. Gunantara, N. A review of multi-objective optimization: Methods and its applications Cogent Eng, 5 (1). Ehrgott, M., Wiecek, M.M. Multiobjective programming (2005) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 667-722. Springer, Cham Gunantara, N., Hendrantoro, G. Multi-objective cross-layer optimization with Pareto method for relay selection in multihop wireless adhoc networks (2013) WSEAS Trans. Commun., 12 (3), pp. 85-95. Shiklaa, A., Agarwalb, P., Rana, R.S., Purohit, R. Applications of TOPSIS Algorithm on various manufacturing process: A review (2017) Mat. Today Proc., (4), pp. 5320-5329. Reich, J., Kinra, A., Kotzab, H., Brusset, X. Stategic global supply chain network design-how decisions analysis combining MILP and AHP on al Pareto front can improve decision-making (2021) Int. J. Prod. Res., 59 (5), pp. 1557-1572. Hamdan, S., Cheaitou, A. Supplier selection and order allocation with green criteria: An MCDM and multi objective optimization approach (2017) Comput. Oper. Res., 81, pp. 282-304. Kilic, H.S., Yalcin, A.S. Modified two-phase fuzzy goal programming integrated with IF-TOPSIS for green supplier selection (2020) Appl. Soft Comput., 93. |
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© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Neira Rodado, DionicioJimenez Delgado, GenettCrespo, FernandoMorales Espinosa, Roberto AntonioPlazas Alvarez, Jonny RafaelHernandez, Hugo2025-05-16T20:05:44Z2025-05-16T20:05:44Z2023-11-21Neira-Rodado, D., Jimenez-Delgado, G., Crespo, F., Morales Espinosa, R.A., Plazas Alvarez, J.R., Hernandez, H. (2023). A Hybrid MOO/MCDM Optimization Approach to Improve Decision-Making in Multiobjective Optimization. In: Mori, H., Asahi, Y., Coman, A., Vasilache, S., Rauterberg, M. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14056. Springer, Cham. https://doi.org/10.1007/978-3-031-48044-7_8https://hdl.handle.net/11323/1424210.1007/978-3-031-48044-7_8Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Multiobjective optimization (MOO) and multicriteria decision-making (MCDM) are critical disciplines in operations research, aiming to assist decision-makers in making the best decisions in complex problems. Nevertheless, the hybridization of the process has yet to be explored. In this case, the hybridization of the decision process is analyzed to evaluate the solution set obtained with this approach and compare it against the solutions obtained with Pareto Set. This novel approach shows that according to the decision-maker preferences, solutions could be in this solution set despite not being included in the Pareto Set. This approach gives alternatives to decision-makers without moving apart much from the best solution. A flow shop is used as a numerical example to compare the Pareto Set and hybrid approach outcomes.12 páginasapplication/pdfengSpringer VerlagGermanyhttps://link.springer.com/chapter/10.1007/978-3-031-48044-7_8A hybrid MOO/MCDM optimization approach to improve decision-making in multiobjective optimizationDocumento de Conferenciahttp://purl.org/coar/resource_type/c_8544http://purl.org/coar/resource_type/c_8042http://purl.org/coar/resource_type/c_c94fTextinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/redcol/resource_type/WPinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a852023-07-23/2023-07-28Copenhagen, DenmarkDrumond, P., de Araújo Costa, I.P., Moreira, M.Â.L., Dos Santos, M., Gomes, C.F.S., Do Nascimento Maêda, S.M. Strategy study to prioritize marketing criteria: An approach in the light of the DEMATEL method (2022) Procedia Comput. Sci., 199, pp. 448-455.Quezada, L.E., López-Ospina, H.A., Ortiz, C., Oddershede, A.M., Palominos, P.I., Jofré, P.A. A DEMATEL-based method for prioritizing strategic projects using the perspectives of the Balanced Scorecard (2022) Int. J. Prod. Econ., 249.Wang, P., Zhu, Z., Huang, S. The use of improved TOPSIS method based on experimental design and Chebyshev regression in solving MCDM problems (2017) J. Intell. Manuf., 28 (1), pp. 229-243.Çelikbilek, Y., Tüysüz, F. An in-depth review of theory of the TOPSIS method: An experimental analysis (2020) J. Manag. Anal., 7 (2), pp. 281-300.Ortiz-Barrios, M.A., de Felice, F., Parra Negrete, K., Aleman Romero, B., Yaruro Arenas, A., Petrillo, A. An AHP-topsis integrated model for selecting the most appropriate tomography equipment (2016) Int. J. Inf. Technol. Decis. Mak., 15 (4), pp. 861-885.Rodriguez, J.V., Rodado, D.N., Parody, A., Crespo, F., Brugés-Ballesteros, L. (2021) Addressing the Permutational Flow Shop Scheduling Problem through Constructive Heuristics: A Statistical Comparison, pp. 169-181.Marler, R., Arora, J. Survey of multi-objective optimization methods for engineering (2004) Struct. Multidisc. Optim., 26, pp. 369-395.Aranda, J.A., Orjuela, J.A. Optimización multiobjetivo en la gestión de cadenas de suministro de biocombustibles. Una revisión de la literature Ingenierıa, 20 (1), pp. 37-63.Gunantara, N. A review of multi-objective optimization: Methods and its applications Cogent Eng, 5 (1).Ehrgott, M., Wiecek, M.M. Multiobjective programming (2005) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 667-722. Springer, ChamGunantara, N., Hendrantoro, G. Multi-objective cross-layer optimization with Pareto method for relay selection in multihop wireless adhoc networks (2013) WSEAS Trans. Commun., 12 (3), pp. 85-95.Shiklaa, A., Agarwalb, P., Rana, R.S., Purohit, R. Applications of TOPSIS Algorithm on various manufacturing process: A review (2017) Mat. Today Proc., (4), pp. 5320-5329.Reich, J., Kinra, A., Kotzab, H., Brusset, X. Stategic global supply chain network design-how decisions analysis combining MILP and AHP on al Pareto front can improve decision-making (2021) Int. J. Prod. Res., 59 (5), pp. 1557-1572.Hamdan, S., Cheaitou, A. Supplier selection and order allocation with green criteria: An MCDM and multi objective optimization approach (2017) Comput. Oper. Res., 81, pp. 282-304.Kilic, H.S., Yalcin, A.S. Modified two-phase fuzzy goal programming integrated with IF-TOPSIS for green supplier selection (2020) Appl. Soft Comput., 93.MCDMMultiobjective OptimizationPareto setTOPSISPublicationORIGINALA Hybrid MOO MCDM Optimization Approach to Improve Decision Making in Multiobjective Optimization.pdfA Hybrid MOO MCDM Optimization Approach to Improve Decision Making in Multiobjective Optimization.pdfapplication/pdf141445https://repositorio.cuc.edu.co/bitstreams/401ce95d-a691-44f1-89ba-16a02bf10970/download13c75bc5589bdd4bb2b5e593fdcea847MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/b5e1f03a-71f9-489a-a98c-bc18ce09b7ab/download73a5432e0b76442b22b026844140d683MD52TEXTA Hybrid MOO MCDM Optimization Approach to Improve Decision Making in Multiobjective Optimization.pdf.txtA Hybrid MOO MCDM Optimization Approach to Improve Decision Making in Multiobjective Optimization.pdf.txtExtracted texttext/plain5247https://repositorio.cuc.edu.co/bitstreams/ea89f370-388c-41a0-9f9d-6cd8b10887c0/downloadfbe75af0ee64f92cdf5639adbf40bfb4MD53THUMBNAILA Hybrid MOO 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>
 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