Performance prediction of pharmaceutical suppliers: comparative study between DEA-ANFIS-PSO and DEA-ANFIS-GA
by Rohaifa Khaldi; Abdellatif El Afia; Raddouane Chiheb
International Journal of Computer Applications in Technology (IJCAT), Vol. 60, No. 4, 2019

Abstract: The selection of a pharmaceutical supplier is a critical task within a hospital. Dealing with the wrong supplier may plague the overall healthcare supply chain, especially patient's life. Thereby, this study investigates the feasibility of using DEA in conjunction with ANFIS-PSO and ANFIS-GA, to evaluate and predict supplier performance. This investigation is a comparative study between ANFIS-PSO and ANFIS-GA. To our best knowledge, it fills a gap in the literature by assessing the benchmarking capabilities of the two proposed models. DEA-BCC was applied to evaluate the efficiency scores of the selected suppliers. ANFIS-PSO and ANFIS-GA were applied to learn DEA patterns and to predict the performance of unseen suppliers. Further, to determine the accuracy of those models, a statistical analysis was performed, and their results were compared with ANFIS-Hybrid model. The results revealed that ANFIS-PSO model yields the best trade-off approximation-generalisation. Thus, this model can be considered as a promising decision support system at the operational and strategic level.

Online publication date: Fri, 26-Jul-2019

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