An integrated decision making approach for assessing healthcare waste treatment technologies from a multiple stakeholder. (January 2017)

• Record Type: Journal Article
• Authors: Shi, Hua; Liu, Hu-Chen; Li, Ping; Xu, Xue-Guo
• Is Part Of: Waste management. Volume 59(2017)
• Page Start: 508
• Page End: 517
• Publication Date: 2017-01
• Subjects: Healthcare waste management -- Cloud model -- MABAC method -- HCW treatment technology; Hazardous wastes -- Periodicals; Refuse and refuse disposal -- Periodicals; 363.728
• DOI: 10.1016/j.wasman.2016.11.016
• Languages: English
• ISSNs: 0956-053X

Abstract

Highlights: An integrated method for HCW treatment technology selection is proposed. Decision makers' weights are computed based on uncertainty and divergence degrees. Cloud model is used to deal with uncertain linguistic assessments of decision makers. An extended MABAC is utilized for ranking HCW disposal alternatives. An empirical case is given to illustrate the procedure of the proposed framework. Abstract: With increased worldwide awareness of environmental issues, healthcare waste (HCW) management has received much attention from both researchers and practitioners over the past decade. The task of selecting the optimum treatment technology for HCWs is a challenging decision making problem involving conflicting evaluation criteria and multiple stakeholders. In this paper, we develop an integrated decision making framework based on cloud model and MABAC method for evaluating and selecting the best HCW treatment technology from a multiple stakeholder perspective. The introduced framework deals with uncertain linguistic assessments of alternatives by using interval 2-tuple linguistic variables, determines decision makers' relative weights based on the uncertainty and divergence degrees of every decision maker, and obtains the ranking of all HCW disposal alternatives with the aid of an extended MABAC method. Finally, an empirical example from Shanghai, China, is provided to illustrate the feasibility and effectiveness of the proposed approach. Results indicate that the methodology being proposed is more suitable and effective to handle the HCW treatment technology selection problem under vague and uncertain information environment.