تزریق بیش از اندازه وجه به خودپردازها موجب تحمیل هزینه اضافی به بانک و کمبود وجه در دستگاه‌ها موجب نارضایتی مشتریان و به خطر افتادن برند بانک خواهد شد. برای این منظور باید در دستگاه‌های خودپرداز وجه نقد قابل ملاحظه‌ای تزریق شود تا پاسخگوی نیاز مشتریان باشد؛ اما تأکید بر این رویه ممکن است سبب رسوب پول در دستگاه‌ها شده و زیان‌های اقتصادی برای بانک به ‌همراه داشته باشد. بنابراین، بانک‌ها همان‌طور که به مدیریت نقدینگی در شعب می‌پردازند، باید با توجه به شرایط زمانی، مکانی و اقتصادی به مدیریت نقدینگی دستگاه‌های خودپرداز نیز بپردازند. مهمترین گام در این راستا تشخیص میزان تقاضای وجه نقد مشتریان است. بدین منظور میانگین تراکنشهای 9 ماه سال 95 برای 1377 دستگاه خودپرداز مورد سنجش قرار داده شده و در این مقاله سعی شده است تا با پیدا کردن یک الگوی رفتاری از مشتریان با استفاده از شبکه عصبی سری زمانی پویا (NARX) روند نقدینگی دستگاه‌های خودپرداز پیش بینی شود. نتایج بدست آمده نشان می دهد که مدل طراحی شده با شبکه عصبی پویا نسبت به مدلهای کلاسیک از کارایی بهتری برخوردار بوده است. Manuscript profile

In the Inverse Data Envelopment Analysis (InvDEA) models, inputs and outputs of Decision Making Units (DMUs) are estimated while their relative efficiency scores remain unchanged. But, in some cases, the inputs cost information is available. This paper uses the inputs cost information and generalized the InvDEA concept in two-stage network structures with undesirable output in the second stage. To this end, it proposes a four-stage method to deal with the InvDEA concept for estimating the inputs of the DMUs with a two-stage network structure method, while the allocative efficiency scores of all units remain stable. Eventually, an empirical example is presented to illustrate the capability of the presented method. Manuscript profile

In the present world, calculating the efficiency of systems with an internal structure, suchas two-stage systems, is principally imperative. Conventional Data envelopment analysis (DEA) is a non-parametric approach that measures the efficiency of comparable black box systems. There is a weakness in traditional DEA that does not survey the internal structure of systems. Hence, for evaluating the efficiency of the systems with internal structure, Network DEA (NDEA) was presented. Two-stage systems are a special case of network systems. Many procedures were suggested to measure the efficiency of these systems such as slacks-based measure (SBM) approach. In this paper, we will focus on two-stage systems with shared inputs between stages and final output in stage 1 and we shall propose non-cooperative models to calculate the efficiency of these systems based on SBM procedure. Also, we shall indicate the overall efficiency of these systems as the product of the efficiency scores of stages. In the end, to explain the proposed approach, a numerical example will be presented. Manuscript profile

Nowadays, the rapid growth of data in organizations has caused managers to look for a way to analyze them. Extracting useful knowledge from aggregation data can lead to appropriate strategic decision-making for the organization. This paper suggests an application of hybrid network based on amount month demand in every ATM device based on transaction mean of 9 months for 1377 devices to obtain customer behavior patterns, to do so, first designed a basic model based on an auto-regressive with exogenous input network (NARX) then, the optimization of the weight and bias of the designed network is made by the genetic algorithm (GA). As a result, finding the weights of the network represents a nonlinear optimization problem that is solved by the genetic algorithm. Paper results show that the NARX-ANFIS Hybrid network using GA for the learning of rules and to optimize the network weights and weights of the network and the fixed threshold can improve the accuracy of the prediction model. Also, classic models are more efficient and increased benefits and lower financing costs and more rational inventory cash control. As well, the designed model can lead to increase benefits and decrease costs in the bank so that, exact forecast and optimal cash upload in ATMs will lead to increase funds on the bank and rise customers and popularity the brand of the bank. Manuscript profile

One of the major concerns in healthcare management is the optimal allocation of staffing and resources. The global crisis created by the COVID-19 Pandemic has created extreme strains both on the staffing and the resources available to the healthcare systems. It has geometrically added to the number of patients seeking health services. In addition, the Pandemic has dramatically increased the rate of mortality in the hospitals in an unprecedented way. Therefore, in this research, in order not to sacrifice the quality of the services provided, we have used Data Envelopment Analysis (DEA) model to determine the efficiency of the emergency departments in the hospitals and the possible improvements that could be made to them. As traditional DEA models do not seek to reduce the undesirable outputs and increase the undesirable inputs, in addition to determining the efficiency of decision making units (DMU) despite some undesirable input components, the effect of these units on performance is investigated. To this end, considering the problem assumptions, first, a set of proper production possibilities is defined. Finally, a new method is introduced to determine the system’s performance in the presence of some undesirable input components. The impact of undesirable components on determining efficiency is specified, and a real example is provided consisting of emergency rooms in 30 hospitals, in which five desirable inputs and four desirable outputs along with one undesirable input are considered. The example is solved using the presented model, and the efficiency scores are determined. Manuscript profile

In the real world, many systems are a combination of two stages that are connected with intermediate measures. In fact, intermediate measures are considered as the outputs of the first stage and the inputs of the second stage. To evaluate the efficiency of these system, network DEA (NDEA) models are presented. In some cases, these systems may have shared inputs, and also part of the intermediate measures will be allocated as inputs of second-stage. We also frequently deal uncertain information such as stochastic data, fuzzy data and so on. Therefore, in practice, it is not easy to obtain the exact values of these inputs and assign them to each of the stages and determine the use of the second stage of intermediate measures. Therefore, in this paper, we shall combine fuzzy DEA and NDEA models which introduce a model based on the multiplicative approach, the non-compensatory property of the multiplication operator and the cut procedure. These models calculate the cut interval of overall efficiency and efficiency of stages in the presence of triangular fuzzy numbers (TFNs) and specify the optimal portion of stages in the use of shared inputs and the portion of the second stage of intermediate measures. Furthermore, the product of the upper (the lower) bound of the cut of the stages efficiency is considered as the upper (the lower) bound overall efficiency. Finally, we will illustrate the proposed models by using a numerical example extracted from the extant literature. Manuscript profile

Data envelopment analysis is always considered as a non-parametric method for measuring the efficiency of a set of decision units. The efficiency number obtained from standard models is a criterion for comparing the performance of each decision unit with other units. Despite the many strengths of these models, one of their weaknesses is the lack of distinction between efficient units. Also, these models do not pay attention to the internal structure of the units and have a black box view. To solve these problems, relational data envelopment analysis models are used, which are much more cost-effective in terms of time and cost; But these models are static and do not take time into evaluation. In this paper, a method has been proposed for grading of the decision making units with multi- period two stages network structure using relative data envelopment analysis. Three different perspective are introduced for assessment of efficiency in time periods via relative data envelopment analysis. Proportionate with each perspective, an efficiency number is obtained for any decision making unit. Then three efficiency numbers obtained in the mentioned method is combined with Shannon entropy method and a total efficiency criterion is defined for each unit. Finally, this measure is considered as the main indicator for the units grading. The results of implementation of the mentioned algorithm on the real example and comparison with the similar methods clarify the strength of this algorithm. Manuscript profile

This paper investigates the centralized resource allocation with centralized structures by using the data envelopment analysis-ratio (DEA-R) models. To this end, it proposes a method to determine the resource allocation of centralized structures such that the ratio of inputs to outputs are minimized. Manuscript profile