Inverse Data Envelopment Analysis in the Presence of Random and Undesirable Outputs

Conventional inverse data envelopment analysis (IDEA) models assess some certain and desirable performance measures for the changes of others while the efficiency scores are fixed or improved. However, there are random and undesirable outputs in many investigations. Accordingly, an inverse DEA model with random and undesirable outputs is proposed in this research to estimate natural disposable random inputs for the alterations of random desirable outputs. Actually, natural disposable random inputs are assessed using the proposed chance-constrained inverse DEA model under managerial and natural disposability. The multi-objective chance-constrained inverse DEA model is answered by solving a linear problem. A numerical example is used to illustrate the introduced technique. The findings show the proposed approach can be applied to analyze the efficiency and performance measures where undesirable and random outputs are presented.

Keywords:

Inverse DEA , Chance-Constrained DEA , Undesirable Outputs , Managerial Disposability