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A Novel Coordinated TOPSIS Based on Coefficient of Variation

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  • Pengyu Chen

    (School of Geography & Resource Science, Neijiang Normal University, Neijiang 641100, China)

Abstract

Coordinated Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a significant improvement of TOPSIS, which take into account the coordination level of attributes in the decision-making or assessment. However, in this study, it is found that the existing coordinated TOPSIS has some limitations and problems, which are listed as follows. (1) It is based on modified TOPSIS, not the original TOPSIS. (2) It is inapplicable when using vector normalization. (3) The calculation formulas of the coordination degree are incorrect. (4) The coordination level of attributes is interrelated with the weights. In this paper, the problems of the existing coordinated TOPSIS are explained and revised, and a novel coordinated TOPSIS based on coefficient of variation is proposed to avoid the limitations. Comparisons of the existing, revised, and proposed coordinated TOPSIS are carried out based on two case studies. The comparison results validate the feasibility of the proposed coordinated TOPSIS.

Suggested Citation

  • Pengyu Chen, 2019. "A Novel Coordinated TOPSIS Based on Coefficient of Variation," Mathematics, MDPI, vol. 7(7), pages 1-17, July.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:7:p:614-:d:247447
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    References listed on IDEAS

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    8. Huang, Wencheng & Shuai, Bin & Sun, Yan & Wang, Yang & Antwi, Eric, 2018. "Using entropy-TOPSIS method to evaluate urban rail transit system operation performance: The China case," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 292-303.
    9. Maria Rosaria Guarini & Fabrizio Battisti & Anthea Chiovitti, 2018. "A Methodology for the Selection of Multi-Criteria Decision Analysis Methods in Real Estate and Land Management Processes," Sustainability, MDPI, vol. 10(2), pages 1-28, February.
    10. Mulliner, Emma & Malys, Naglis & Maliene, Vida, 2016. "Comparative analysis of MCDM methods for the assessment of sustainable housing affordability," Omega, Elsevier, vol. 59(PB), pages 146-156.
    11. Edmundas Kazimieras Zavadskas & Abbas Mardani & Zenonas Turskis & Ahmad Jusoh & Khalil MD Nor, 2016. "Development of TOPSIS Method to Solve Complicated Decision-Making Problems — An Overview on Developments from 2000 to 2015," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 645-682, May.
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    2. Qi Wen & Caiting Shen & Siyou Xia & Yongsheng Wang, 2022. "Analysis of spatiotemporal variations and effects of regional functions in rural China," Growth and Change, Wiley Blackwell, vol. 53(3), pages 1414-1435, September.
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    4. Elissaios Sarmas & Vangelis Marinakis & Haris Doukas, 2022. "A data-driven multicriteria decision making tool for assessing investments in energy efficiency," Operational Research, Springer, vol. 22(5), pages 5597-5616, November.
    5. Zhang, Dong & Zhang, Rui & Zhang, Bin & Zheng, Yu & An, Zhoujian, 2023. "Environment dominated evaluation modeling and collocation optimization of a distributed energy system based on solar and biomass energy," Renewable Energy, Elsevier, vol. 202(C), pages 1226-1240.
    6. Susmaga, Robert & Szczȩch, Izabela & Zielniewicz, Piotr & Brzezinski, Dariusz, 2023. "MSD-space: Visualizing the inner-workings of TOPSIS aggregations," European Journal of Operational Research, Elsevier, vol. 308(1), pages 229-242.

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