IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v195y2009i2p412-426.html
   My bibliography  Save this article

An interactive desirability function method to multiresponse optimization

Author

Listed:
  • Jeong, In-Jun
  • Kim, Kwang-Jae

Abstract

Multiresponse optimization problems often involve incommensurate and conflicting responses. To obtain a satisfactory compromise in such a case, a decision maker (DM)'s preference information on the tradeoffs among the responses should be incorporated into the problem. This paper proposes an interactive method based on the desirability function approach to facilitate the preference articulation process. The proposed method allows the DM to adjust any of the preference parameters, namely, the shape, bound, and target of a desirability function in a single, integrated framework. The proposed method would be highly effective in generating a compromise solution that is faithful to the DM's preference structure.

Suggested Citation

  • Jeong, In-Jun & Kim, Kwang-Jae, 2009. "An interactive desirability function method to multiresponse optimization," European Journal of Operational Research, Elsevier, vol. 195(2), pages 412-426, June.
    • Handle: RePEc:eee:ejores:v:195:y:2009:i:2:p:412-426
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(08)00222-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. A. M. Geoffrion & J. S. Dyer & A. Feinberg, 1972. "An Interactive Approach for Multi-Criterion Optimization, with an Application to the Operation of an Academic Department," Management Science, INFORMS, vol. 19(4-Part-1), pages 357-368, December.
    2. Kim, Kwang-Jae & Lin, Dennis K.J., 2006. "Optimization of multiple responses considering both location and dispersion effects," European Journal of Operational Research, Elsevier, vol. 169(1), pages 133-145, February.
    3. JosÉ Figueira & Salvatore Greco & Matthias Ehrogott, 2005. "Multiple Criteria Decision Analysis: State of the Art Surveys," International Series in Operations Research and Management Science, Springer, number 978-0-387-23081-8, April.
    4. Kwang‐Jae Kim & Dennis K. J. Lin, 2000. "Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability functions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 311-325.
    5. Murat Köksalan & Robert D. Plante, 2003. "Interactive Multicriteria Optimization for Multiple-Response Product and Process Design," Manufacturing & Service Operations Management, INFORMS, vol. 5(4), pages 334-347, May.
    6. Korhonen, Pekka & Moskowitz, Herbert & Wallenius, Jyrki, 1992. "Multiple criteria decision support - A review," European Journal of Operational Research, Elsevier, vol. 63(3), pages 361-375, December.
    7. Korhonen, Pekka J. & Laakso, Jukka, 1986. "A visual interactive method for solving the multiple criteria problem," European Journal of Operational Research, Elsevier, vol. 24(2), pages 277-287, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, Guihong & Zhao, Zhihong & Xu, Haoran & Zhang, Jinping & Kong, Xiangjun & Yuan, Lijuan, 2022. "A robust assessment method of recoverable geothermal energy considering optimal development parameters," Renewable Energy, Elsevier, vol. 201(P1), pages 426-440.
    2. Ouyang, Linhan & Ma, Yizhong & Wang, Jianjun & Tu, Yiliu, 2017. "A new loss function for multi-response optimization with model parameter uncertainty and implementation errors," European Journal of Operational Research, Elsevier, vol. 258(2), pages 552-563.
    3. Nitidetch Koohathongsumrit & Pongchanun Luangpaiboon, 2022. "An integrated FAHP–ZODP approach for strategic marketing information system project selection," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 1792-1809, September.
    4. Shin, Sangmun & Kongsuwon, Pauline & Cho, Byung Rae, 2010. "Development of the parametric tolerance modeling and optimization schemes and cost-effective solutions," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1728-1741, December.
    5. Lee, Dong-Hee & Kim, Kwang-Jae & Köksalan, Murat, 2011. "A posterior preference articulation approach to multiresponse surface optimization," European Journal of Operational Research, Elsevier, vol. 210(2), pages 301-309, April.
    6. Shi, Liangxing & Lin, Dennis K.J. & Peterson, John J., 2016. "A confidence region for the ridge path in multiple response surface optimization," European Journal of Operational Research, Elsevier, vol. 252(3), pages 829-836.
    7. He, Zhen & Zhu, Peng-Fei & Park, Sung-Hyun, 2012. "A robust desirability function method for multi-response surface optimization considering model uncertainty," European Journal of Operational Research, Elsevier, vol. 221(1), pages 241-247.
    8. Wang, Jiacheng & Tan, Xianfeng & Zhao, Zhihong & Chen, Jinfan & He, Jie & Shi, Qipeng, 2024. "Coupled thermo-hydro-mechanical modeling on geothermal doublet subject to seasonal exploitation and storage," Energy, Elsevier, vol. 293(C).
    9. Wang, Jianjun & Ma, Yizhong & Ouyang, Linhan & Tu, Yiliu, 2016. "A new Bayesian approach to multi-response surface optimization integrating loss function with posterior probability," European Journal of Operational Research, Elsevier, vol. 249(1), pages 231-237.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. J. Cabello & M. Luque & F. Miguel & A. Ruiz & F. Ruiz, 2014. "A multiobjective interactive approach to determine the optimal electricity mix in Andalucía (Spain)," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 109-127, April.
    2. M Köksalan & E Karasakal, 2006. "An interactive approach for multiobjective decision making," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(5), pages 532-540, May.
    3. Park, K. Sam & Shin, Dong Eun, 2012. "Interactive multiobjective optimization approach to the input–output design of opening new branches," European Journal of Operational Research, Elsevier, vol. 220(2), pages 530-538.
    4. Thomas L. Saaty, 2013. "The Modern Science of Multicriteria Decision Making and Its Practical Applications: The AHP/ANP Approach," Operations Research, INFORMS, vol. 61(5), pages 1101-1118, October.
    5. Mariano Luque & Rafael Caballero & Julian Molina & Francisco Ruiz, 2007. "Equivalent Information for Multiobjective Interactive Procedures," Management Science, INFORMS, vol. 53(1), pages 125-134, January.
    6. Murat Köksalan & Robert D. Plante, 2003. "Interactive Multicriteria Optimization for Multiple-Response Product and Process Design," Manufacturing & Service Operations Management, INFORMS, vol. 5(4), pages 334-347, May.
    7. Aksoy, Yasemin & Butler, Timothy W. & Minor, Elliott D., 1996. "Comparative studies in interactive multiple objective mathematical programming," European Journal of Operational Research, Elsevier, vol. 89(2), pages 408-422, March.
    8. Ishizaka, Alessio & Siraj, Sajid, 2018. "Are multi-criteria decision-making tools useful? An experimental comparative study of three methods," European Journal of Operational Research, Elsevier, vol. 264(2), pages 462-471.
    9. Hsiu-Wen Chen & Weng Kee Wong & Hongquan Xu, 2012. "An augmented approach to the desirability function," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 599-613, July.
    10. Korhonen, Pekka & Yu, GuangYuan, 1997. "A reference direction approach to multiple objective quadratic-linear programming," European Journal of Operational Research, Elsevier, vol. 102(3), pages 601-610, November.
    11. Nigussie, Yalemzewd & van der Werf, Edwin & Zhu, Xueqin & Simane, Belay & van Ierland, Ekko C., 2018. "Evaluation of Climate Change Adaptation Alternatives for Smallholder Farmers in the Upper Blue-Nile Basin," Ecological Economics, Elsevier, vol. 151(C), pages 142-150.
    12. Peter Bogetoft & Kurt Nielsen, 2005. "Internet Based Benchmarking," Group Decision and Negotiation, Springer, vol. 14(3), pages 195-215, May.
    13. Zhang, Weihua & Reimann, Marc, 2014. "A simple augmented ∊-constraint method for multi-objective mathematical integer programming problems," European Journal of Operational Research, Elsevier, vol. 234(1), pages 15-24.
    14. Lee, Dong-Hee & Kim, Kwang-Jae & Köksalan, Murat, 2011. "A posterior preference articulation approach to multiresponse surface optimization," European Journal of Operational Research, Elsevier, vol. 210(2), pages 301-309, April.
    15. Kaliszewski, Ignacy & Michalowski, Wojtek, 1999. "Searching for psychologically stable solutions of multiple criteria decision problems," European Journal of Operational Research, Elsevier, vol. 118(3), pages 549-562, November.
    16. Nikolaos Argyris & Alec Morton & José Rui Figueira, 2014. "CUT: A Multicriteria Approach for Concavifiable Preferences," Operations Research, INFORMS, vol. 62(3), pages 633-642, June.
    17. M. Makowski, 1994. "Methodology and a Modular Tool for Multiple Criteria Analysis of LP Models," Working Papers wp94102, International Institute for Applied Systems Analysis.
    18. Behnam Malakooti, 2015. "Double Helix Value Functions, Ordinal/Cardinal Approach, Additive Utility Functions, Multiple Criteria, Decision Paradigm, Process, and Types (Z Theory I)," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(06), pages 1353-1400, November.
    19. Kaliszewski, Ignacy, 2004. "Out of the mist--towards decision-maker-friendly multiple criteria decision making support," European Journal of Operational Research, Elsevier, vol. 158(2), pages 293-307, October.
    20. Rafael Lazimy, 2013. "Interactive Polyhedral Outer Approximation (IPOA) strategy for general multiobjective optimization problems," Annals of Operations Research, Springer, vol. 210(1), pages 73-99, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:195:y:2009:i:2:p:412-426. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.