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Multi-choice goal programming with utility functions

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  • Chang, Ching-Ter

Abstract

Goal programming (GP) has been, and still is, the most widely used technique for solving multiple-criteria decision problems and multiple-objective decision problems by finding a set of satisfying solutions. However, the major limitation of goal programming is that can only use aspiration levels with scalar value for solving multiple objective problems. In order to solve this problem multi-choice goal programming (MCGP) was proposed by Chang (2007a). Following the idea of MCGP this study proposes a new concept of level achieving in the utility functions to replace the aspiration level with scalar value in classical GP and MCGP for multiple objective problems. According to this idea, it is possible to use the skill of MCGP with utility functions to solve multi-objective problems. The major contribution of using the utility functions of MCGP is that they can be used as measuring instruments to help decision makers make the best/appropriate policy corresponding to their goals with the highest level of utility achieved. In addition, the above properties can improve the practical utility of MCGP in solving more real-world decision/management problems.

Suggested Citation

  • Chang, Ching-Ter, 2011. "Multi-choice goal programming with utility functions," European Journal of Operational Research, Elsevier, vol. 215(2), pages 439-445, December.
    • Handle: RePEc:eee:ejores:v:215:y:2011:i:2:p:439-445
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    References listed on IDEAS

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    4. Hocine, Amin & Zhuang, Zheng-Yun & Kouaissah, Noureddine & Li, Der-Chiang, 2020. "Weighted-additive fuzzy multi-choice goal programming (WA-FMCGP) for supporting renewable energy site selection decisions," European Journal of Operational Research, Elsevier, vol. 285(2), pages 642-654.
    5. Yuanzhong Li & Xinbang Cao & Shaojian Qu & Ying Ji & Zilong Xia, 2022. "Cost Sharing in Insurance Communities: A Hybrid Approach Based on Multiple-Choice Objective Programming and Cooperative Games," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
    6. Esin Balci & Sezin Balci & Aysun Sofuoglu, 2022. "Multi-purpose reverse logistics network design for medical waste management in a megacity: Istanbul, Turkey," Environment Systems and Decisions, Springer, vol. 42(3), pages 372-387, September.
    7. Gezen, Mesliha & Karaaslan, Abdulkerim, 2022. "Energy planning based on Vision-2023 of Turkey with a goal programming under fuzzy multi-objectives," Energy, Elsevier, vol. 261(PA).
    8. Gang Lin & Honglei Xu & Shaoli Wang & Conghua Lin & Fan Zhang & Junxiang Zhu, 2024. "Navigating Uncertainty: A Framework for Optimising Public Transport Networks’ Performance," Sustainability, MDPI, vol. 16(3), pages 1-23, February.
    9. Vandana Goyal & Namrata Rani & Deepak Gupta, 2022. "FGP approach to quadratically constrained multi-objective quadratic fractional programming with parametric functions," OPSEARCH, Springer;Operational Research Society of India, vol. 59(2), pages 594-602, June.
    10. Farshad Noravesh & Kristiaan Kerstens, 2022. "Some connections between higher moments portfolio optimization methods," Papers 2201.00205, arXiv.org.
    11. Zaiwu Gong & Chao Xu & Francisco Chiclana & Xiaoxia Xu, 2017. "Consensus Measure with Multi-stage Fluctuation Utility Based on China’s Urban Demolition Negotiation," Group Decision and Negotiation, Springer, vol. 26(2), pages 379-407, March.
    12. Chang, Ching-Ter & Chung, Cheng-Kung & Sheu, Jiuh-Biing & Zhuang, Zheng-Yun & Chen, Huang-Mu, 2014. "The optimal dual-pricing policy of mall parking service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 223-243.
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    14. Mondal, Arijit & Giri, Binoy Krishna & Roy, Sankar Kumar, 2023. "An integrated sustainable bio-fuel and bio-energy supply chain: A novel approach based on DEMATEL and fuzzy-random robust flexible programming with Me measure," Applied Energy, Elsevier, vol. 343(C).
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    16. Najafzad, Hamid & Davari-Ardakani, Hamed & Nemati-Lafmejani, Reza, 2019. "Multi-skill project scheduling problem under time-of-use electricity tariffs and shift differential payments," Energy, Elsevier, vol. 168(C), pages 619-636.

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