  Multi-criteria decision-making (MCDM) problems involve the evaluation of
alternatives based on various minimization and maximization criteria.
Similarly, efficiency evaluation (EA) methods assess decision-making units
(DMUs) by analyzing their input consumption and output production. MCDM and EA
methods face challenges in managing alternatives and DMUs with varying
capacities across different criteria (inputs and outputs). That leads to
performance assessments often skewed by subjective biases in criteria
weighting. We introduce two innovative scenarios utilizing linear
programming-based Virtual Gap Analysis (VGA) models to address these
limitations. This dual-scenario approach aims to mitigate traditional biases,
offering robust solutions for comprehensively assessing alternatives and DMUs.
Our methodology allows for the influential ranking of alternatives in MCDM
problems and enables each DMU to adjust its input and output ratios to achieve
efficiency.
