@article{lertworasirikul_charnsethikul_fang_2011, title={Inverse data envelopment analysis model to preserve relative efficiency values: The case of variable returns to scale}, volume={61}, ISSN={0360-8352}, url={http://dx.doi.org/10.1016/j.cie.2011.06.014}, DOI={10.1016/j.cie.2011.06.014}, abstractNote={This paper studies the inverse Data Envelopment Analysis (inverse DEA) for the case of variable returns to scale (inverse BCC). The developed inverse BCC model can preserve relative efficiency values of all decision making units (DMUs) in a new production possibility set composing of all current DMUs and a perturbed DMU with new input and output values. We consider the inverse BCC model for a resource allocation problem, where increases of some outputs and decreases of the other outputs of the considered DMU can be taken into account simultaneously. The inverse BCC problem is in the form of a multi-objective nonlinear programming model (MONLP), which is not easy to solve. We propose a linear programming model, which gives a Pareto-efficient solution to the inverse BCC problem. However, there exists at least an optimal solution to the proposed model if and only if the new output vector is in the set of current production possibility set. The proposed approach is illustrated via a case study of a motorcycle-part company.}, number={4}, journal={Computers & Industrial Engineering}, publisher={Elsevier BV}, author={Lertworasirikul, Saowanee and Charnsethikul, Peerayuth and Fang, Shu-Cherng}, year={2011}, month={Nov}, pages={1017–1023} }
 @article{lertworasirikul_fang_joines_nuttle_2003, title={Fuzzy data envelopment analysis (DEA): a possibility approach}, volume={139}, ISSN={["1872-6801"]}, DOI={10.1016/S0165-0114(02)00484-0}, abstractNote={Evaluating the performance of activities or organizations by traditional data envelopment analysis (DEA) models requires crisp input/output data. However, in real-world problems inputs and outputs are often imprecise. This paper develops DEA models using imprecise data represented by fuzzy sets (i.e., “fuzzy DEA” models). It is shown that fuzzy DEA models take the form of fuzzy linear programming which typically are solved with the aid of some methods to rank fuzzy sets. As an alternative, a possibility approach is introduced in which constraints are treated as fuzzy events. The approach transforms fuzzy DEA models into possibility DEA models by using possibility measures of fuzzy events (fuzzy constraints). We show that for the special case, in which fuzzy membership functions of fuzzy data are of trapezoidal types, possibility DEA models become linear programming models. A numerical experiment is used to illustrate the approach and compare the results with those obtained with alternative approaches.}, number={2}, journal={Fuzzy Sets and Systems}, publisher={Elsevier BV}, author={Lertworasirikul, S. and Fang, S. C. and Joines, J. A. and Nuttle, H. L. W.}, year={2003}, pages={379–394} }