期刊
JOURNAL OF CLEANER PRODUCTION
卷 272, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2020.122807
关键词
Waste disposal; Waste treatment; Intuitionistic fuzzy sets; Disposable medical wastes; Divergence measure; Evaluation based on distance from average solution method; Health-care waste management
Nowadays, health-care waste management is a challenging issue for both the public and government sectors because it contains infectious, radioactive, or hazardous waste. Selecting a suitable method for health-care waste disposal is known as a complex decision-making problem due to the existence of several multiple criteria that may in conflict with each other. In this paper, we propose a novel method called evaluation based on distance from average solution framework based on parametric divergence measures with the context of intuitionistic fuzzy sets to evaluate and rank the health-care waste disposal alternative. To do this, first, novel parametric intuitionistic fuzzy divergence measures are developed, and various desired properties have also been examined. Second, the decision experts are evaluated, and the criteria weights are computed by developed parametric divergence measure method. Third, the preference order of the alternatives is illustrated by the developed framework. The proposed framework is based on the positive and negative distances from the average solution. Moreover, the health-care waste disposal alternative selection problem is considered to elucidate the applicability of the proposed framework. Four health-care waste disposal alternatives, including incineration, steam sterilization, microwave and landfill disposal, are considered in this study. The health-care waste disposal alternatives considered in this study include. The outcome illustrates that, the steam sterilization was the highest appraisal score (0.7025) and therefore, it should be chosen as the most suitable health-care waste disposal method in this study. Also, a comparison with existing approaches is shown to illustrate the validity and practicability of the developed framework. (C) 2020 Elsevier Ltd. All rights reserved.
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