GRA Method for Probabilistic Linguistic Multiple Attribute Group Decision Making with Incomplete Weight Information and Its Application to Waste Incineration Plants Location Problem
- DOI
- 10.2991/ijcis.d.191203.002How to use a DOI?
- Keywords
- Multiple attribute group decision making (MAGDM); Probabilistic linguistic term sets (PLTSs); GRA method; Incomplete weight information; Waste incineration plants
- Abstract
In this essay, we investigate the probabilistic linguistic multiple attribute group decision making (PL-MAGDM) with incomplete weight information. In this method, the linguistic representation developed recently is converted into probabilistic linguistic information. For deriving the weight information of the attribute, an optimization model is built on the basis of the fundamental idea of grey relational analysis (GRA), by which the attribute weights can be decided. Then, the optimal alternative is chosen through calculating largest relative relational degree from the probabilistic linguistic positive ideal solution (PLPIS) which considers both the largest grey relational coefficient (GRC) from the PLPIS and the smallest GRC form probabilistic linguistic negative ideal solution (PLNIS). In the end, a case study concerning waste incineration plants location problem is given to demonstrate the merits of the developed methods.
- Copyright
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Fan Lei AU - Guiwu Wei AU - Jianping Lu AU - Jiang Wu AU - Cun Wei PY - 2019 DA - 2019/12/09 TI - GRA Method for Probabilistic Linguistic Multiple Attribute Group Decision Making with Incomplete Weight Information and Its Application to Waste Incineration Plants Location Problem JO - International Journal of Computational Intelligence Systems SP - 1547 EP - 1556 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.191203.002 DO - 10.2991/ijcis.d.191203.002 ID - Lei2019 ER -