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Integrating the Stochastic Multi-criteria Acceptability Analysis with the MULTIMOORA Method for Multiple Criteria Decision Making

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Proceedings of the Thirteenth International Conference on Management Science and Engineering Management (ICMSEM 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1001))

Abstract

The stochastic multi-criteria acceptability analysis (SMAA-2) is a useful approach for multiple criteria decision making with inaccurate or uncertain information. The MULTIMOORA (MULTIplicative Multi-Objective Optimization by Ratio Analysis) is a robust method to aggregate the utility values of alternatives in decision making. However, in some decision-making problems solved by the MULTIMOORA method, the stochastic information is available. To tackle such problems appropriately, this study investigates the SMAA-MULTIMOORA method by combining the SMAA-2 with MULTIMOORA. The proposed method enables the MULTIMOORA method to handle inaccurate, imprecise or uncertain information as input by stochastic variables. The procedure of how to use the SMAA-MULTIMOORA method is provided for the facility of applications. Finally, an illustrative example is given to verify the validity of the proposed method.

J. Xu et al. (eds.), International Conference on Management Science and Engineering Management 2019

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Acknowledgement

The work was supported by the National Natural Science Foundation of China (71771156), the 2019 Sichuan Planning Project of Social Science (No. SC18A007), the 2019 Soft Science Project of Sichuan Science and Technology Department (No. 2019JDR0141), the Project of Innovation at Sichuan University (No. 2018hhs-43), and the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah (No. RG-10-611-39).

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Correspondence to Huchang Liao .

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Mi, X., Liao, H., Al-Barakati, A. (2020). Integrating the Stochastic Multi-criteria Acceptability Analysis with the MULTIMOORA Method for Multiple Criteria Decision Making. In: Xu, J., Ahmed, S., Cooke, F., Duca, G. (eds) Proceedings of the Thirteenth International Conference on Management Science and Engineering Management. ICMSEM 2019. Advances in Intelligent Systems and Computing, vol 1001. Springer, Cham. https://doi.org/10.1007/978-3-030-21248-3_40

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