An interactive MOLP method for analysis Optimization estimation by DEA models

Data envelopment analysis (DEA) and multiple objective linear programming (MOLP) are tools that can be used in management control and planning. Whilst these two types of model are similar in structure, DEA is directed to assessing past performances as part of management control function and MOLP to planning future performance targets. Several equivalence models between the output-oriented DEA and Multiple Objective Linear Programming (MOLP) models have been proposed in the literature to take the DMs’ preferences into consideration. However, these models are not able to identify target units when undesirable outputs are produced with desirable outputs in the production process. In this study we obtain a new link between a BCC model and the weighted minimax reference point of the MOLP formulation that simultaneously and interactively considers the increase in the total desirable outputs and total undesirable outputs. The interactive methods developed for solving nonlinear multiobjective optimization problems. In interactive methods, a decision maker plays an important part and the idea is to support her/him in the search for the most preferred solution. In interactive methods, steps of an iterative solution algorithm are repeated and the decision maker progressively provides preference information so that the most preferred solution can be found. In this paper our proposed model gives the most preferred solution for DM with trade off analysis on both undesirable outputs and desirable outputs values of DMUs. Our main aim is to decrease total input consumption and increase total output production which results in solving one mathematical programming model instead of n models. Key-Words: Data envelopment analysis; interactive multi objective linear programming; Gradient projection method