Induction approach via P-Graph to rank clean technologies. Issue 1 (January 2020)

• Record Type: Journal Article
• Authors: Low, C.X.; Ng, W.Y.; Putra, Z.A.; Aviso, K.B.; Promentilla, M.A.B.; Tan, R.R.
• Is Part Of: Heliyon. Volume 6:Issue 1(2020)
• Publication Date: 2020-01
• Subjects: Chemical engineering -- Optimal selection -- Simple additive weighting -- Clean technologies -- Induction -- Decision analysis -- P-Graph; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2019.e03083
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: Identification of appropriate clean technologies for industrial implementation requires systematic evaluation based on a set of criteria that normally reflect economic, technical, environmental and other aspects. Such multiple attribute decision-making (MADM) problems involve rating a finite set of alternatives with respect to multiple potentially conflicting criteria. Conventional MADM approaches often involve explicit trade-offs in between criteria based on the expert's or decision maker's priorities. In practice, many experts arrive at decisions based on their tacit knowledge. This paper presents a new induction approach, wherein the implicit preference rules that estimate the expert's thinking pathways can be induced. P-graph framework is applied to the induction approach as it adds the advantage of being able to determine both optimal and near-optimal solutions that best approximate the decision structure of an expert. The method elicits the knowledge of experts from their ranking of a small set of sample alternatives. Then, the information is processed to induce implicit rules which are subsequently used to rank new alternatives. Hence, the expert's preferences are approximated by the new rankings. The proposed induction approach is demonstrated in the case study on the ranking of Negative Emission Technologies (NETs) viability for industry implementation. Abstract : Chemical engineering, Optimal selection; Simple additive weighting; Clean technologies; Induction; Decision analysis; P-graph.