Journal article
Evaluate the locations for smart waste bins using BWM and WASPAS methods under a probabilistic linguistic environment
Journal of intelligent & fuzzy systems, v 41(6), pp 7199-7218
01 Jan 2021
Abstract
The decreasing resources of the earth and the deterioration of the environment are offering new challenges for handling waste management practices. The establishment of the smart waste bins plays an important role in promoting the development of waste classification and treatment fundamentally. We developed the evaluation system for the location selection problem of smart waste bins. Considering the uncertainty in the location selection of smart waste bins, the probabilistic linguistic term sets (PLTSs) are selected to express the evaluation information. Because of the excellent performance in weight-determing, the best worst method (BWM) is chosen to get the weight of criteria. While the weighted aggregated sum product assessment (WASPAS) method could handle both the qualitative and quantitative information, which are considered to derive the final ranking of the alternatives. This paper proposed a new group multi-criteria decision making approach integrating the BWM and the WASPAS with probabilistic linguistic information. Finally, in the empirical example, a sensitivity analysis shows that the proposed method is stable, a comparison analysis with PL-TOPSIS, PL-VIKOR, and PL-TODIM reflects its effectiveness and rationality, and the managerial implication verifies its usefulness and practicability, which also give guide to the company, government and resident.
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Details
- Title
- Evaluate the locations for smart waste bins using BWM and WASPAS methods under a probabilistic linguistic environment
- Creators
- Yanfang Ma - Hebei University of TechnologyWeifeng Xu - Hebei University of TechnologyXiaoyu Wang - Southeast UniversityZongmin Li - Sichuan UniversityBenjamin Lev - Drexel University
- Publication Details
- Journal of intelligent & fuzzy systems, v 41(6), pp 7199-7218
- Publisher
- Ios Press
- Number of pages
- 20
- Grant note
- 19YJC630117 / Humanity and Social Science Youth Foundation of Ministry of Education of China G2020202008 / Hebei Natural Science Foundation; Natural Science Foundation of Hebei Province 721704 0976 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000731754900096
- Scopus ID
- 2-s2.0-85122022450
- Other Identifier
- 991019169678504721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence