Suppliers’ selection problem has always been daunting challenges in the Newsvendor problem. Furthermore, since the failures in the supplier’s products cause irreparable damage to the retailer, it is necessary to consider the reliability of products in ordering suppliers’ products. This paper develops the Newsvendor model by considering the impactful criteria in supplier selection and product reliability so that the total cost of the chain is minimized in a multi-product and multi-period model with multiple suppliers. While multiple criteria decision making (MCDM) accounts for multiple criteria and their tradeoffs, its application in Newsvendor model is not considered. This paper applies the Bayesian best worst method (BWM), as one of the MCDM methods, for ranking criteria and the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) for prioritizing the suppliers. Then, the obtained weights are plugged into the model as the inputs of the designed model. A case study with real data in the electronic supply chain is considered. To validate the results obtained by the proposed method, genetic algorithm (GA) and particle swarm optimization (PSO) are leveraged to solve the proposed model. Finally, the efficiency of the designed model is verified through a case study.

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