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.

中文翻译：

---

窗口数据包络分析方法：综述和文献计量分析

窗口数据包络分析 (WDEA) 是一种流行、有效且适用的方法，用于对等决策单元 (DMU) 的动态性能评估。WDEA 是一种非参数面板方法，它基于移动平均线的原理进行操作，并通过将不同时期的每个 DMU 视为单独的 DMU 来建立效率度量。通过应用 WDEA 方法，决策者 (DM) 可以通过一系列重叠窗口来衡量不同时期不同 DMU 的效率。此外，当可用的 DMU 数量有限时，WDEA 可以通过增加 DMU 的数量来提高辨别能力。鉴于 WDEA 方法的优势及其在实际问题中的应用，本文对 1985 年至 2020 年发表的 387 篇 WDEA 论文进行了调查和分析。和未来研究的机会。值得注意的是，研究结果显示了 WDEA 在文献中的适用性和有效性。