الفهرس | Only 14 pages are availabe for public view |
Abstract The case in many real-life applications deals with complex, vague, imprecise, and uncertain data. Traditional Data Envelopment Analysis (DEA) requires accurate and exact inputs and outputs; hence it cannot deal with many practical problems. As well, traditional DEA assumes the dependent relations among decision-making units (DMUs) are not exist. In the traditional DEA DMUs are treated as black boxes where the initial inputs enter the first stage and the final outputs from the second stage are only considered. The intermediate stage is neglected to measure their efficiency. Therefore, the “black box” theory reflects inaccurate efficiency indicators about systems consisting of complex structures. This research integrates classical DEA, a Two-stage network, and rough theory to obtain a comprehensive evaluation. The proposed Two-stage RDEA model is a constant return to scale (CRS) that measures the relative efficiencies of DMUs containing two subprocesses while handling some uncertain rough variables in inputs and outputs. The model is applied in the supply chain (Sc) to measure the efficiency of two stages of the supply chain, including suppliers and manufacturers. The experimental results showed the applicability of the proposed model to deal with the multistage system containing uncertainty data. Moreover, we compare the traditional DEA with the twostage rough RDEA model and explain how to improve the relative efficiency. As well, we enhanced our model to evaluate the multistage system, and apply it in a three-level supply chain, including suppliers, manufacturers, and distributors. We also introduce some recommendations for helping the decision-maker improve comprehensive efficiency by changing trust-level (α). |