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Health-Care Waste Treatment Technology Selection Using the Interval 2-Tuple Induced TOPSIS Method

Author

Listed:
  • Chao Lu

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Jian-Xin You

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Hu-Chen Liu

    (School of Management, Shanghai University, Shanghai 200444, China
    School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Ping Li

    (Zhoupu Hospital Affiliated to Shanghai University of Medicine & Health Sciences, Shanghai 201318, China)

Abstract

Health-care waste (HCW) management is a major challenge for municipalities, particularly in the cities of developing nations. Selecting the best treatment technology for HCW can be regarded as a complex multi-criteria decision making (MCDM) issue involving a number of alternatives and multiple evaluation criteria. In addition, decision makers tend to express their personal assessments via multi-granularity linguistic term sets because of different backgrounds and knowledge, some of which may be imprecise, uncertain and incomplete. Therefore, the main objective of this study is to propose a new hybrid decision making approach combining interval 2-tuple induced distance operators with the technique for order preference by similarity to an ideal solution (TOPSIS) for tackling HCW treatment technology selection problems with linguistic information. The proposed interval 2-tuple induced TOPSIS (ITI-TOPSIS) can not only model the uncertainty and diversity of the assessment information given by decision makers, but also reflect the complex attitudinal characters of decision makers and provide much more complete information for the selection of the optimum disposal alternative. Finally, an empirical example in Shanghai, China is provided to illustrate the proposed decision making method, and results show that the ITI-TOPSIS proposed in this paper can solve the problem of HCW treatment technology selection effectively.

Suggested Citation

  • Chao Lu & Jian-Xin You & Hu-Chen Liu & Ping Li, 2016. "Health-Care Waste Treatment Technology Selection Using the Interval 2-Tuple Induced TOPSIS Method," IJERPH, MDPI, vol. 13(6), pages 1-16, June.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:6:p:562-:d:71471
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    References listed on IDEAS

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    Cited by:

    1. Abdullah Al-Barakati & Pratibha Rani, 2024. "Assessment of healthcare waste treatment methods using an interval-valued intuitionistic fuzzy double normalization-based multiple aggregation approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(8), pages 19397-19424, August.
    2. Sheng-Li Si & Xiao-Yue You & Hu-Chen Liu & Jia Huang, 2017. "Identifying Key Performance Indicators for Holistic Hospital Management with a Modified DEMATEL Approach," IJERPH, MDPI, vol. 14(8), pages 1-17, August.
    3. Wuyong Qian & Zhou-Jing Wang & Kevin W. Li, 2016. "Medical Waste Disposal Method Selection Based on a Hierarchical Decision Model with Intuitionistic Fuzzy Relations," IJERPH, MDPI, vol. 13(9), pages 1-13, September.
    4. Xiayu Tong & Zhou-Jing Wang, 2016. "A Group Decision Framework with Intuitionistic Preference Relations and Its Application to Low Carbon Supplier Selection," IJERPH, MDPI, vol. 13(9), pages 1-16, September.
    5. Adis Puška & Željko Stević & Dragan Pamučar, 2022. "Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(9), pages 11195-11225, September.
    6. Torkayesh, Ali Ebadi & Rajaeifar, Mohammad Ali & Rostom, Madona & Malmir, Behnam & Yazdani, Morteza & Suh, Sangwon & Heidrich, Oliver, 2022. "Integrating life cycle assessment and multi criteria decision making for sustainable waste management: Key issues and recommendations for future studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).

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