Affiliations: [a] School of Economics and Management, Hebei University of Technology, Tianjin, P. R. China | [b] School of Economics and Management, Southeast University, Nanjing, Jiangsu, P. R. China | [c] Business School, Sichuan University, Chengdu, P. R. China | [d] Drexel University, LeBow College of Business, Philadelphia, PA, USA
Correspondence:
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Corresponding author. Xiaoyu Wang, School of Economics and Management, Southeast University, Nanjing, Jiangsu 211189, P. R. China. E-mail: [email protected].
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.
Keywords: Multiple attributes decision making, BWM, WASPAS, probabilistic linguistic term set, smart waste bins
