Multi-criteria group decision making under uncertainty: Application in reservoir flood control operation. (February 2018)

• Record Type: Journal Article
• Authors: Zhu, Feilin; Zhong, Ping-an; Sun, Yimeng
• Is Part Of: Environmental modelling & software. Volume 100(2018)
• Page Start: 236
• Page End: 251
• Publication Date: 2018-02
• Subjects: Multi-criteria decision making -- Flood control operation -- Stochastic multicriteria acceptability analysis -- Monte carlo simulation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.11.032
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper proposes an innovative framework for solving stochastic multi-criteria decision making (MCDM) problems when uncertainties exist in criteria performance values (PVs) and criteria weights (CWs) simultaneously. Methods for quantifying uncertainties in criteria PVs and CWs are presented. We establish the SMAA-TOPSIS model by combining stochastic multicriteria acceptability analysis (SMAA) and technique for order preference by similarity to ideal solution (TOPSIS). The risk of decision making errors is proposed to assess the impact of uncertainties on MCDM. We develop the LHS-based Monte Carlo simulation algorithm and corresponding computer program for solving the SMAA-TOPSIS model. We also suggest a three-stage MCDM procedure for stochastic MCDM problems. We apply the proposed methodology to a flood control operation case study to demonstrate its applicability. Our results indicate that the proposed methods can provide valuable risk information and enable risk-informed decisions to be made with higher reliabilities. Highlights: A novel SMAA-TOPSIS model for stochastic MCDM problems is proposed. The algorithm and program are designed for solving the SMAA-TOPSIS model. Methods for quantifying multiple uncertainties in MCDM are proposed. The risk of decision making errors is proposed to assess the effect of uncertainty. Helps decision makers make risk-informed decisions with higher reliabilities.