We transformed a data envelopment analysis (DEA) optimization model into a robust second-order cone equivalent to immunize against output perturbation in an uncertainty set. The robust DEA framework was then used to assess the effect of a wood hardening treatment using methyl methacrylate (MMA) on selected hybrid poplar clones. Because the performance of MMA-hardened hybrid poplar clones varies across clones, ranking hardened clones is crucial for developing hardening treatments for specific industrial applications. The numerical results demonstrate that the hardening treatment can be optimized by applying the proposed DEA framework to select the best hybrid poplar clone types and the optimal amount of impregnated chemicals.
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We greatly appreciate the financial support provided by the Canada Research Chair Program and the Ministere du developpement economique, de l’innovation et de l’exportation du Quebec (MDEIE), and technical support from the Service de Recherche et d’expertise en Transformation des produits forestiers (SEREX).
This project is funded by the National Science Foundation of China (Grant# 71471055 and 91546102), and Marianne and Marcus Wallenberg Foundation (Grant# MMW 2015.0007).
Dexiang Wu and WeiDan Ding are contributed equally to this work.
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Wu, D., Ding, W., Koubaa, A. et al. Robust DEA to assess the reliability of methyl methacrylate-hardened hybrid poplar wood. Ann Oper Res 248, 515–529 (2017). https://doi.org/10.1007/s10479-016-2201-9
- Hybrid poplar
- Methyl methacrylate (MMA)
- Data envelopment analysis (DEA)
- Robust optimization