ارزیابی عملکرد واحدهای تصمیم­گیرنده در حالتی که خروجی­هایی با مجموع ثابت در فرآیند تولید حضور داشته باشند از موضوعات قابل توجه در بحث مدیریت عملکرد می­باشد. تعریف­های متفاوتی از اندازه­گیری کارایی واحدهای تصمیم­گیرنده، زمانی که هدف ثابت نگه داشتن مجموع خروجی­ها باشد، ارایه شده است که در تمام آنها واحدها به یک مرز تعادل مشترک واحد با کارایی یک می­رسند و این امر چه بسا مستلزم یک تبادل چشمگیر بین ورودی­ها و خروجی­های واحدهای کارا و ناکارا باشد که ممکن است در کاربردهای عملی میسر نباشد. در این مقاله هدف ارایه یک مدل تحلیل پوششی داده­هاست تا در آن با استفاده از مفهوم dependent -context، تبادل داده بین واحدها عادلانه­تر باشد و به جای رسیدن به یک مرز تعادل واحد، این اجازه به واحدها داده می­شودکه با توجه به اندازه توانایی واحد، برای بهبود کارایی فعلی خود تلاش کنند. برای تحلیل روش ارایه شده در این مقاله 21 کشور عضو  OECDانتخاب شده و مورد مطالعه قرار گرفت. Manuscript profile

In this article, we investigate the measurement of performance in DMUs in which input and/or output values are given as imprecise data. By imprecise data, we mean that in some cases, we only know that the actual values are inside certain intervals, and in other cases, data are specified only as ordinal preference information. In this article, we present two distinct perspectives for determining the upper and lower bounds of the efficiency the DMU under evaluation can have with imprecise data: (1) The optimistic perspective, which uses DEA-efficient production frontier, and seeks the best score among various values of the efficiency score; the measured efficiency in this perspective is called the best relative efficiency or the optimistic efficiency. (2) The pessimistic perspective, which uses inefficiency frontier, also called input frontier, and seeks the lowest score among various values of the efficiency score; the measured efficiency in this perspective is called the worst relative efficiency or the pessimistic efficiency. For this reason and contrary to some DEA-related studies, we do not restrict our attention only to precise data. We will investigate a more general case of dealing with imprecise data, providing a method for obtaining the upper and lower bounds of efficiency. Two numerical examples will be presented to illustrate the application of the proposed DEA approach.‎‎ Manuscript profile

In this article, we investigate a new continuous linear model with constraints for the direct selection of the most efficient unit in the analysis of data coverage presented by Akhlaghi et al. (2021) on uncertainty robust optimization. Considering the importance of incorporating uncertainty into performance evaluation models in the real world and its increasing application in various problems, we propose a robust optimization approach. Given the discrete and non-convex nature of the introduced models for selecting the most efficient decision-making unit, examining the dual and finding an optimistic scenario is practically impossible. Therefore, by utilizing the linear model presented by Akhlaghi et al. (2021) with constraints for identifying the most efficient unit, we can investigate the robustness of the desired model using(BS )Bertsimas and Sim's (2004) robust estimation method while also considering uncertainty. We aim to demonstrate that employing a robust formulation leads to reliable performance in uncertain conditions Manuscript profile