نوع مقاله : مقاله پژوهشی

نویسندگان

• لیلا پرهیزکار میاندهی ¹
• علیرضا امیرتیموری ²
• سهراب کردرستمی ³
• منصور صوفی ⁴

¹ دانشجوی دکتری رشته ریاضی کاربردی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران

² استاد، گروه ریاضی کاربردی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران

³ استاد،گروه ریاضی، واحد لاهیجان، دانشگاه آزاد اسلامی، لاهیجان، ایران

⁴ استادیار، گروه مدیریت، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران

چکیده

اگر قیمت‌ های خروجی‌ های واحد های تحت ارزیابی مشخص باشد ، ارزیابی کارایی درآمد واحد ها یکی از مهم‌ ترین ارزیابی‌ هایی است که می‌ تواند اطلاعات ارزشمندی را در مورد واحدها ارائه دهد . در این مقاله، ابتدا تعریف جدیدی از اندازه مقیاس بهینه ، براساس بیشینه‌ سازی اندازه متوسط کارایی درآمد ارائه می‌ شود و سپس اندازه متوسط کارایی درآمد در دو فضای محدب و نامحدب تعریف می‌ شود که این اندازه، مستقل از بازده به مقیاس و فرض یکسان بودن بردار قیمت‌ های ورودی و خروجی واحد ها است . در ادامه ، اندازه متوسط کارایی درآمد برای ارزیابی واحد هایی با ورودی‌ ها و خروجی‌ های تصادفی به کار گرفته‌ شده و مدل‌ هایی جهت محاسبه آن در فضای تصادفی ارائه می‌ شود. در پایان نیز، روش پیشنهادی، در یک مثال تجربی برای محاسبه اندازه متوسط کارایی درآمد مجموعه‌ای از مناطق پستی ایران مو رد استفاده قرار می‌گیرد.

کلیدواژه‌ها

• اندازه مقیاس بهینه
• کارایی
• متوسط کارایی درآمد
• تحلیل پوششی داده‌های تصادفی

موضوعات

• مدیریت عملکرد

عنوان مقاله [English]

Average revenue efficiency and optimal scale sizes in stochastic data envelopment analysis: A case study of post offices

نویسندگان [English]

• Leila Parhizkar Miyandehi ¹
• Alireza Amirteimoori ²
• Sohrab Kordrostami ³
• Mansour Soufi ⁴

¹ PhD student in Applied Mathematics, Rasht Branch, Islamic Azad University, Rasht, Iran

² Professor, Department of Applied Mathematics, Rasht Branch, Islamic Azad University, Rasht,Iran

³ Professor, Department of Mathematics, Lahijan Branch, Islamic Azad University, Lahijan, Iran

⁴ Assistant Professor, Department of Management, Rasht Branch, Islamic Azad University, Rasht, Iran

چکیده [English]

Estimating the revenue efficiency of entities being evaluated is crucial as it provides valuable information about organizations, assuming that the output prices are known. This research introduces a new definition of optimal scale size (OSS) based on maximizing the average revenue efficiency (ARE). Additionally, the ARE is defined for both convex and non-convex sets, independent of returns to scale and the assumption that the vector of input-output prices of units is uniform. Moreover, to address the presence of uncertain data in real-world applications, the introduced ARE model is extended to evaluate systems with random inputs and outputs, along with approaches for its calculation. Finally, the proposed method is applied in an experimental example, calculating the ARE for a dataset of postal areas in Iran.
Introduction
The concept of optimal scale size has been extensively studied in the field of data envelopment analysis. Cesaroni and Giovannola's research on non-convex FDH technology reveals that the optimal scale size is a point in the production possibility set that minimizes average cost efficiency. Average cost efficiency, a new measure combining scale and allocation efficiencies, provides a more accurate performance assessment compared to cost and scale efficiencies. When evaluating units with known output prices instead of input prices, assessing revenue efficiency can offer more valuable insights. This paper extends the research on cost evaluation to revenue evaluation. It introduces the concepts of average revenue efficiency and optimal scale size based on revenue maximization. The optimal scale size based on revenue maximization is defined as the point in the production possibility set that maximizes the average radial income for the unit under investigation. Average revenue efficiency serves as an evaluation measure of unit revenue, surpassing revenue and scale efficiencies in accuracy. The paper examines methods for calculating average revenue efficiency in both convex and non-convex technologies. It demonstrates that the average revenue efficiency model in convex technology with variable returns to scale is equivalent to the revenue model with constant returns to scale. Furthermore, the relationship between optimal scale size points based on revenue maximization and the most productive scale size is determined. Next, the paper presents the average revenue efficiency model for stochastic sets with the presence of stochastic data. An experimental example is used to calculate the average revenue efficiency and obtain the optimal scale size for a set of postal areas in Iran.
Materials and Methods
The study builds upon Cesaroni and Giovannola's method for calculating average cost efficiency and optimal scale size to develop models for average revenue efficiency and optimal scale size based on revenue. It also utilizes chance-constrained probabilistic models with a deterministic objective function in DEA literature to present average revenue efficiency for stochastic sets. The model is transformed from stochastic to deterministic and then converted into a linear model using the error structure method.
Discussion and Results
This paper introduces average revenue efficiency and optimal revenue scale size, demonstrating the equivalence between the average revenue efficiency models in convex technology with variable returns to scale and those with constant returns to scale. It also presents the average revenue efficiency model for stochastic sets, enabling the calculation of average revenue efficiency and optimal revenue scale size for units with random inputs and outputs.
Conclusion
In many real-world scenarios, particularly when output prices are known, evaluating revenue efficiency holds greater significance than cost efficiency. This study develops the concepts of average cost efficiency and optimal scale size for revenue evaluation, expanding upon the existing literature on data envelopment analysis. The paper demonstrates how average revenue efficiency can be calculated as a valuable and accurate measure of efficiency in convex and non-convex technologies, without making assumptions about returns to scale. By assuming the randomness of input and output variables and employing chance-constrained models, a quadratic deterministic model is presented to calculate average revenue efficiency. It is then transformed into a linear model assuming uncorrelated variables, enabling the determination of average revenue efficiency and optimal scale size based on revenue maximization for random units. The proposed models are applied to a real-world sample, evaluating the average revenue efficiency of twelve postal units. The results highlight the models' ability to provide a more accurate evaluation of revenue efficiency and identify the best revenue scale size as the reference for inefficient units.

کلیدواژه‌ها [English]

• Optimal Scale Size
• Efficiency
• Average Revenue Efficiency
• Stochastic Data Envelopment Analysis