A novel multi-criteria decision making method for assessing health-care waste treatment technologies based on D numbers. (May 2018)

• Record Type: Journal Article
• Authors: Xiao, Fuyuan
• Is Part Of: Engineering applications of artificial intelligence. Volume 71(2017:Nov.)
• Page Start: 216
• Page End: 225
• Publication Date: 2018-05
• Subjects: Health-care waste (HCW) -- HCW management -- HCW treatment technology -- D numbers -- Multi-criteria decision making; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.03.002
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Health-care waste (HCW) management is a complex problem influenced by many aspects, such as, economic, environment, technical, social, etc. A selection of the best treatment technology for HCW management can be regarded as a complex multi-criteria decision making problem where a number of alternatives and multiple evaluation criteria are need to be considered. In addition, decision makers often express their personal assessments by using multi-granularity linguistic term sets. Due to the involvement of human judgment, various uncertainties are introduced in the HCW process. One critical issue of the HCW treatment technology selection is the representation and handling of uncertain information. In response, a novel multi-criteria decision making method is proposed for the HCW treatment technology selection problem based on an effective representation model of uncertain information, called D numbers. In the proposal, the assessment results of HCW treatment are expressed and modeledby D numbers. It provides a new framework for the HCW treatment technology selection problem in which it was effective and feasible to handle MCDM problems under various uncertainties environment. Comparing with the existing method, the proposed method is clear and concise. Finally, an empirical case study in Shanghai, China is illustrated to validate the feasibility and applicability of the proposed method. As shown in the results, the proposed method selects steam sterilization as the optimal technology to deal with the health-care wastes which is consistent with the existing work where fewer pollutants are discharged and non-hazardous residues are produced so that it decreases more impacts on the environment and public health. The experimental results demonstrate that the proposed method can handle the HCW treatment technology selection problem effectively under complex and uncertain environments.