Chance-constrained data envelopment analysis modeling with random-rough data

Abstract

Data envelopment analysis (DEA) is a useful management tool for measuring the relative efficiency of decision making units (DMUs) which consumes multiple inputs to produce multiple outputs. Although precise input and output data are fundamentally indispensable in classical DEA models, real-world problems often involve random and/or rough input and output data. We present a chance-constrained DEA model with random and rough (random-rough) input and output data and propose a deterministic equivalent model with quadratic constraints to solve the model. The main contributions of this paper are fourfold: (3.1) we propose a DEA model for problems characterized by random-rough variables; (3.2) we transform the proposed chance-constrained model with random-rough variables into a deterministic equivalent non-linear form that could be simplified as a deterministic model with quadratic constraints; (3.3) we perform sensitivity analysis to investigate the stability and robustness of the proposed model; and (3.4) we use a numerical example to demonstrate the feasibility and richness of the obtained solutions.

Solving geometric programming problems with triangular and trapezoidal uncertainty distributions

“…They displayed comparative research work between the FDEA model and the Robust Fuzzy Data Envelopment Analysis (RFDEA) model. Shiraz, Tavana, and Di Caprio [26] studied a chance-constrained Data Envelopment Analysis (DEA) model with random-rough data inputs and outputs and investigated the stability and robustness through sensitivity analysis. The authors considered a chance-constrained DEA model with random and rough input and output data.…”