Data Envelopment Analysis (DEA) is an effective method for evaluating and improving the performance of Decision-Making Units (DMUs). It utilizes mathematical programming models to compare homogeneous units based on their inputs and outputs. One of the major challenges in this field is assessing the efficiency of DMUs over different time periods, where heterogeneity, arises due to various factors, such as, scientific and technological advancements, political and economic changes, system management updates, etc. These changes may lead to the addition or elimination of outputs, making unit comparisons more complex and creating significant differences in efficiency. Traditional DEA methods often fail to account for these changes across time periods simultaneously. Therefore, there is a need for new approaches, as to assess the efficiency of DMUs under such conditions. This paper addresses these challenges and presents a novel approach for analyzing the efficiency of DMUs undergoing substantial changes over time. As a practical application, the results of an empirical study evaluating conferences throughout three time periods, namely, (before the COVID-19 outbreak, during the pandemic, and after the pandemic) have been presented. These findings demonstrate the efficiency of the proposed approach effectively and can significantly assist organizations in more accurate evaluations and the enhancement of performance, relative to the evolving units.