Pythagorean fuzzy weighted discrimination‐based approximation approach to the assessment of sustainable bioenergy technologies for agricultural residues. Issue 6 (9th March 2021)

• Record Type: Journal Article
• Authors: Rani, Pratibha; Mishra, Arunodaya R.; Saha, Abhijit; Pamucar, Dragan
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 6(2021)
• Page Start: 2964
• Page End: 2990
• Publication Date: 2021-03-09
• Subjects: bioenergy technology -- discrimination measure -- Pythagorean fuzzy sets -- score function -- weighted discrimination‐based approximation; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22408
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: The inappropriate dumping of agricultural residues (ARs) can result in environmental pollution and the waste of valuable energy resources. The process of converting ARs to energy has been considered an important step for regional energy, agricultural development, and environmental sustainability and recently, many sustainable bioenergy technologies (BETs) have been developed for ARs. Since the assessment of ARs‐to‐energy conversion technologies contains several alternatives concerning multiple criteria with imprecise information, it is deliberated as an uncertain multicriteria decision‐making (MCDM) problem. The Pythagorean fuzzy set (PFS) is an important and effective way to tackle the uncertainty present in real‐life decision‐making problems. To select a suitable conversion technology from a set of options and upgrade the ARs‐to‐energy industries, the present study develops a combined approach to PFSs based on weighted discrimination‐based approximation (WDBA). This method extends the classical WDBA approach using an improved score function and discrimination measure within the PFS context, to evaluate MCDM problems with partial information on the criteria weights. To estimate the weights of the unknown attributes, a score function‐based linear programming model is developed. A new ranking method is extended to grade the options using the proposed discrimination measure within the PFS environment. Further, a case study assessing ARs‐to‐energy conversion technologies is conducted to illustrate the practicality and feasibility of this method. A comparative analysis shows that the approach developed is more effective and proficient in facilitating decision experts' selection of desirable BETs for ARs.