Performance prediction of pharmaceutical suppliers: comparative study between DEA-ANFIS-PSO and DEA-ANFIS-GA. (22nd July 2019)

• Record Type: Journal Article
• Authors: Khaldi, Rohaifa; Afia, Abdellatif El; Chiheb, Raddouane
• Is Part Of: International journal of computer applications technology. Volume 60:Number 4(2019)
• Page Start: 317
• Page End: 325
• Publication Date: 2019-07-22
• Subjects: adaptive neuro-fuzzy inference system -- genetic algorithm -- particle swarm optimisation -- data envelopment analysis -- benchmarking -- prediction -- performance -- pharmaceutical suppliers -- healthcare supply chain; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

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.