A Value Efficiency Approach to Incorporating Preference Information in Data Envelopment Analysis

Abstract

We develop a procedure and the requisite theory for incorporating preference information in a novel way in the efficiency analysis of Decision Making Units. The efficiency of Decision Making Units is defined in the spirit of Data Envelopment Analysis (DEA), complemented with Decision Maker's preference information concerning the desirable structure of inputs and outputs. Our procedure begins by aiding the Decision Maker in searching for the most preferred combination of inputs and outputs of Decision Making Units (for short, Most Preferred Solution) which are efficient in DEA. Then, assuming that the Decision Maker's Most Preferred Solution maximizes his/her underlying (unknown) value function, we approximate the indifference contour of the value function at this point with its possible tangent hyperplanes. Value Efficiency scores are then calculated for each Decision Making Unit comparing the inefficient units to units having the same value as the Most Preferred Solution. The resulting Value Efficiency scores are optimistic approximations of the true scores. The procedure and the resulting efficiency scores are immediately applicable to solving practical problems.efficiency analysis, data envelopment analysis, multiple criteria decision making, value function

Assessing the Queuing Process Using Data Envelopment Analysis: an Application in Health Centres

“…shows that several attempts have been made to incorporate preference information, mainly with the objective of target setting (Halme et al, 1999). Golany (1988) proposed the idea that the preference information should be considered in order to allow for selecting the most appropriate efficient output target among different alternatives.…”

“…However, in few cases, some variables might be preferred over others (Halme et al, 1999). Thanassoulis and Dunstan (1994) applied the target setting models with different preferences in the education sector where the mean GCSE score output had a higher priority than the output of the percentage of pupils who got a placement on leaving school.…”

Optimal Path for Controlling Sectoral CO2 Emissions Among China’s Regions: A Centralized DEA Approach

“…Decision maker preference information can also be incorporated into DEA by employing the multiple criteria decision-making concept [43,44]. A well-known drawback of weight restrictions is the fact that their use in the multiplier model implicitly changes the model of production technology in the envelopment form [45], and that they inappropriately represent the demands of real-world productive activities [46]; in other words, their use makes the traditional meanings of "technical efficiency" and "technologically feasible improvement" become unsubstantiated [47,48].…”

A Target-Oriented Data Envelopment Analysis for Energy-Environment Efficiency Improvement in Japan

“…In addition, the Reference Point Model provides suggestions, which make it possible to search freely on the efficient frontier for good solutions, or for the most-preferred solution (MPS) based on the decision-maker's preference structure. Furthermore, Halme et al (1999) developed a Value Efficiency Analysis (VEA), which included the decision-maker's preference information in a DEA model. The foundation of VEA originates from the Reference Point Model in a MOLP context.…”