Multicriteria decision making under uncertainty: An advanced ordered weighted averaging operator for planning electric power systems

Authors:
M. Q. Suo;Y. P. Li;G. H. Huang
Affiliations:
MOE Key Laboratory of Regional Energy Systems Optimization, S-C Academy of Energy and Environmental Research, North China Electric Power University, Beijing 102206, China;MOE Key Laboratory of Regional Energy Systems Optimization, S-C Academy of Energy and Environmental Research, North China Electric Power University, Beijing 102206, China;MOE Key Laboratory of Regional Energy Systems Optimization, S-C Academy of Energy and Environmental Research, North China Electric Power University, Beijing 102206, China
Venue:
Engineering Applications of Artificial Intelligence
Year:
2012

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Abstract

In this study, an advanced ordered weighted averaging (AOWA) operator is proposed for tackling multicriteria decision making (MCDM) problems under uncertainties. The AOWA incorporates techniques of interval theory and a center of gravity (COG) method within a traditional ordered weighted averaging (OWA) operator. It can deal with the uncertain inputs under optimistic and pessimistic conditions without knowing their distribution information and linguistic important degrees of all inputs in MCDM systems. The results obtained help decision makers select the optimal alternative according to their optimism degrees. A case study of planning electric power problems is provided for demonstrating the applicability of the proposed method. The results indicate that reasonable solutions have been generated for both discrete intervals and linguistic inputs. For all criteria under consideration, corrective alternatives can be undertaken sensitively under various optimism degrees and thus can help resolve the conflicts in electric power systems under uncertainties.