Rough DT2R2ML for renewable energy supplier selection in the presence of big data

Abstract

SummarySupplier selection is a substantial problem in supply chain management due to concurrent decision on key performance indicators on multi‐dimension data. Recently, more studies investigated the supplier section problem with respect to variety of criteria within the contexts of applied cases. The problem is more significant when industries with high amount of investment explore appropriate suppliers such as renewable energy. This article concerns with developing a new method encompass all indices effective on supplier selection. The proposed algorithm first group by decision tree (DT) indices to criteria and sub‐criteria; then, to include large amount of uncertain data, rough comparisons and weighing are fulfilled using a machine learning (RML). Further, transformation (T) to crisp value and ranking (R) of suppliers are delivered. The new method is implemented for a renewable energy supplier selection problem as a case study. The outputs show the effectiveness of the proposed method in practice since it can handle big data through a machine learning technique. Managerial implications as decision supports are discussed.