ارزیابی عملکرد سیستم‌های تولید با در نظر گرفتن داده‌های مربوط به دوره‌های زمانی مختلف یکی از مهمترین مسائل نظریه تولید است. در این مقاله یک روش جدید برای اندازه گیری کارایی تجمیعی سیستم‌های تولید چند دوره‌ای با استفاده از روش تحلیل پوششی داده‌ها ارائه می‌شود. رویکرد ارائه شده را می‌توان به عنوان توسعه‌ای از روش‌های شعاعی در ادبیات در نظر گرفت. یک مدل مبتنی بر راسل توسعه یافته برای اولین بار برای اندازه گیری کارایی تجمیعی با در نظر گرفتن بازه‌های زمانی مراحل تولید ارائه می‌شود. یکی از ویژگی‌های مفید مدل پیشنهادی این است که ناکارایی رویکرد تجمیعی موجود در یک مرحله بدون نیاز به در نظر گرفتن مرحله دوم برای بهینه سازی متغیرهای کمکی، قابل تشخیص است. ویژگی‌ها و مزایای مدل جدید بحث می‌شوند. در پایان، برای نشان دادن قابلیت اجرای رویکرد جدید، دو مثال کاربردی بررسی و تحلیل می‌شوند. نتایج، عملکرد خوب روش پیشنهادی را نشان می دهند. Manuscript profile

With a brief review of the studies on the industry in Data Envelopment Analysis (DEA) framework, the present paper proposes inner and outer technologies when only some basic information is available about the technology. Furthermore, applying Linear Programming techniques, it also determines lower and upper bounds for directional distance function (DDF) measure, overall and allocative efficiency in industry level. Finally, the results are illustrated using a Cobb-Douglas function. Manuscript profile

The current study investigates performance in two-stage production systems through the measurement of capacity utilization (CU). In short-run, the degree of capacity utilization depends on the ability of decision making units to utilize fixed production factors. To develop this indicator in two-stage production processes, a modified SBM network DEA model is proposed. A numerical example is also presented to point out the applicability and effectiveness of current model. Manuscript profile

This paper proposes a centralized network data envelopment analysis model that combines the centralized data envelopment analysis model with possibility of downsizing and two-stage network data envelopment analysis. In the proposed model, this paper also considers the situation in which shared inputs are jointly consumed in each stage. We also assume some outputs can be produced by the first and second stages by using separate inputs. The proposed model is illustrated in an empirical example of twenty sale representatives in two provinces of Golestan and Mazandaran. The results provide valuable information for the centralized decision-maker on how to reallocate resources among the units. Manuscript profile

Data Envelopment Analysis (DEA) is a method for assessing the efficiency of Decision Making Units (DMUs). Traditional DEA models do not examine the potential differences between two stages caused by intermediate operations. As a result, DEA has been extended to evaluate the efficiency of two-stage processes. In these processes, all outputs of the first stage are intermediate operations that comprise the inputs of the second stage. The input data in real-world applications may have negative values. In this study, considering the importance of network production processes, we deal with the efficiency evaluation of two-stage production units with negative data. Also, we extend CRS (constant returns to scale) bounded additive model for the efficiency evaluation of the two-stage units in the presence of negative data. For illustration, we evaluate the efficiency and ranking of 36 airlines by applying the new model. Manuscript profile

Data Envelopment Analysis (DEA) is a mathematical technique to assess the performance of Decision Making Units (DMUs) with similar inputs and outputs. The traditional DEA models disregard the internal structure of units and have a “black box” view. Thus, to evaluate the structures with more than one stage, the network DEA (NDEA) models expanded. On the other hand, the dynamic optimization models have been presented to eliminate the limitations of static models in optimization. In the article, for the first time, a systematic approach is used to present a dynamic NDEA with constant inputs and undesirable outputs. First, we used an axiomatic approach in DEA with undesirable output and presented an NDEA model with undesirable output. Then, we extended the proposed approach and presented a dynamic NDEA with undesirable output and a constant input. Afterward, we applied this model to evaluate hospitals’ performance in an experimental study to estimate the efficiency of their dynamic network. Manuscript profile