Using Multi-Objective Linear Programming (MOLP) and Data Envelopment Analysis (DEA) models in Non-Discretionary Performance Measurement

Inverse DEA (InvDEA) models put one step forward, in contrast with the DEA models by estimating required input level for producing a perturbed output level with the current efficiency status. In many real world applications, decision-makers face non-discretionary factors which can be hardly controlled by the Decision Making Units (DMUs). However, these types of factor are not dealt in the inverse DEA problems. Thus, the current covers the research methodological gap of the literature by developing mathematical foundation of the InDEA models capable of dealing with non-discretionary factors. To do this end, an MOLP model along with its required constraints is developed to be linked with the developed models. A numerical example and a real-world case study are provided to illustrate the proposed models and demonstrate their applicability and validity for the real world problems.