Novel Intuitionistic Fuzzy Fault Tree Analysis for Effective Infectious Medical Waste Management

With an increasing population, the number of healthcare issues is also increasing due to various critical diseases. To treat these diseases, different types of medical facilities are required, which finally produce a large quantity of medical waste. Such medical waste may be harmful or even dangerous to people as well as the environment if inadequately treated. To prevent the spread of such diseases in a healthy civil society, an effective medical waste management system is required to be developed. Generally, to develop an effective medical waste management system, identification of the most critical incidents is needed, which requires a large quantity of data that may not be available. In this case, the problem is associated with ambiguity and uncertainty due to a variety of practical and financial reasons. So, the main objective of the paper is to analyze any infectious medical waste management system under uncertainty and identification of the critical incidents of its failure. The proposed study is actually based on this motivation. The paper proposes an intuitionistic fuzzy fault tree analysis (FFTA) method that quantifies data uncertainty through trapezoidal intuitionistic fuzzy numbers (TrIFN) while novel arithmetic operations are applied for computing the top incident failure possibility. To develop these novel operations, the weakest t-norm is applied to detract the accumulating circumstances of fuzziness, while Algebraic t-norm and t-conorm are used to estimate membership and non-membership degrees, respectively, of top event failure possibility in terms of trapezoidal intuitionistic fuzzy numbers (TrIFN). A Hamming distance-based ranking method has been developed and then applied for the identification of critical incidents. These are the primary contributions of the proposed study in the paper. The proposed intuitionistic fuzzy fault tree analysis (FFTA) method has been applied to investigate the failure phenomenon of an infectious medical waste management system under uncertainty. The effectiveness of the proposed method is shown by comparing the results with four existing fault tree methods. The findings may be helpful to develop an efficient medical waste management system.