Window Data Envelopment Analysis (WDEA) is a popular, effective, and applicable methods for dynamic performance assessment of peer decision making units (DMUs). WDEA is a non-parametric panel method that operates based on the principle of moving averages and establishes efficiency measures by treating each DMU in different periods as a separate DMU. By applying the WDEA approach, a decision-maker (DM) can measure the efficiency of different DMUs in different periods through a sequence of overlapping windows. Also, WDEA can increase the discrimination power by increasing the number of DMUs when a limited number of DMUs is available. Given the advantages of the WDEA approach and its applications in real-world problems, this paper surveys and analyses 387 WDEA papers published from 1985 to 2020. The paper also recommends some suggestions, guidelines, and opportunities for future research. Notably, the findings show the applicability and efficacy of WDEA in the literature.
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