Journal of Applied Mathematics

Journal of Applied Mathematics / 2014 / Article
Special Issue

Operational Research 2014

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Research Article | Open Access

Volume 2014 |Article ID 703969 |

F. Hosseinzadeh Lotfi, Z. Taeb, S. Abbasbandy, "Progress and Regress of Time Dependent Data and Application in Bank Branch", Journal of Applied Mathematics, vol. 2014, Article ID 703969, 9 pages, 2014.

Progress and Regress of Time Dependent Data and Application in Bank Branch

Academic Editor: Mohammad Khodabakhshi
Received23 Jan 2014
Accepted01 Feb 2014
Published23 Apr 2014


To evaluate each decision making unit having time dependent inputs and outputs data, a new method has been developed and reported here. This method uses the Malmquist productivity index, and is a very simple function based on Cubic Spline function to determine the progress and regress of that unit. To show the capability of this developed method, the data of 9 branches of a commercial bank has been used, evaluated, and reported.

1. Introduction

In the modern managements, several applied scientific methods have been developed to improve the quality of systems. Data Envelopment Analysis (DEA) is one of the most suitable and applicable methods to evaluate and compare several units or a unit in various times [13]. By using this scientific technique, the system manager is in the position to find and apply the best decision for improving that system [4, 5].

At the beginning, the efficiency change was the only possible criteria to find the progress and regress of a unit. Via research developments, it has been shown that the technical change has effect in productivity, too, and the Malmquist Productivity Index (MPI) was introduced by Malmquist in 1953 [6]. Later the researchers recommended productivity evaluation and defined MPI for each unit based on inputs disposal and outputs products [710]. Hereafter several researches focused on calculating this index and several applications were procured [1113].

Those evaluations used fixed and certain values of the units and excluded some application cases, which have used the time dependent available or foresight data [1416]. To determine the progress and regress of a Decision Making Unit (DMU) is necessary to use models with time dependent data analysis capability.

In this paper, the definition of MPI will be illustrated [1719], then the efficiency of time dependent data will be defined, and a new method to determine progress and regress of units will be presented and applied for a commercial bank and the results will be discussed.

2. Malmquist Productivity Index

Farell (1957) determined a suitable method to evaluate experimental production function for several inputs and outputs with using linear programming technique and Data Envelopment Analyses (DEA) [20]. By applying DEA, the best efficiency frontier will be calculated with a set of DMUs and omitting of any priority for inputs and outputs. The DMUs of efficiency frontier are the units with the maximum output and/or the minimum input levels. Using the efficient units and efficiency frontier, the analysis of other inefficiency units is possible.

MPI is defined with assimilation efficiency changes of each unit and technology changes. MPI can be calculated via several functions, such as distance function This equation shows in special conditions only the efficiency frontier change at time related to that could not be a suitable criterion to calculate the technology change. If , then th unit is hypothesized as efficient. This distance function does not define the inefficiency values. Fare decomposed MPI into two components, using linear inefficiency of technology frontier. The efficiency frontier will be specified for each DMU with DEA. Production function is hypothesized instant and . Calculation of the MPI requires four linear programming problems as follows: is the th input and is the th output of at time . The efficiency shoes the highest possible decrease of input for related output.

Instead , CCR problem (2), is calculated at time and is equal to and is the technical efficiency for at time . The value of for is the distance of at with the frontier of time , calculated by the following problem: The same model is calculated: Fare hypotheses , must be equal to 1 to be efficient. Therefore he defined relative efficiency change as He described one geometric compotation to determine technology change between and : MPI will be calculated from multiplication efficiency change and technology change for each input oriented at time and : The simple form of relation (9) is This value defines geometric convex compotation, because it specified the smallest decrease of efficiencies and any small change in each efficiency effect in MPI. Three conditions are available.(1); increase productivity and observe progress.(2); decrease productivity and observe regress.(3); no change is seen in productivity at time in comparison to .

3. Efficiency of Time Dependent Units

Usually, study cases have time dependent inputs and outputs, and their efficiency is managers’ interest. In this relation a time dependent function is necessary to evaluate the efficiency of this kind of data in virtual intervals.

A system that includes DMUs which include inputs and outputs is supposed. Inputs and outputs may be a function dependent on time; then assume that and are th inputs and th output of . As this, each DMU is represented as These functions can contemplate linear or nonlinear and may be constant. To calculate the efficiency of , in case of known function, for some the inputs and outputs will be in hand. In some interval , this parameter may assume random numbers. By using uniform distribution it may be supposed that where is the number of numeral used in this interval and are random numbers (without losing the generality, we assume that are distinct). At first, the time points will be sorted in an increasing order and then named and indeed and . Because of this, in the fixed amount of , all inputs and outputs for any DMUs are fixed also. For each , all DMUs are fixed. Because of this, in the fixed amount of , all inputs and outputs for any DMUs are fixed, also. For each , all DMUs are fixed; and the efficiency of will be determined by using CCR model. To find the efficiency, it is necessary to solve linear programs (LPs). Therefore, always the number of LPs is more. In supposed model, only distinguished number models are solved and, by estimating the function of efficiency, efficiency of ideal is known.

The efficiency of time dependent data will be as follows according to the Cubic Spline function for th DMU: That is determined by By calculating the efficiency values in some times () of this interval Cubic Spline function can be predicted. This data is very close to efficiency function and has less calculating error.

The goal of this study was to determine the progress and regress of time dependent DMUs. Following a simple method will be illustrated in this relation.

4. The Progress and Regress of Time Dependent Units

Here the value of progress and regress of time dependent data will be introduced and the developed method will be schemed and reported.

4.1. Assumed

As the inputs and outputs of each DMU are functions dependent on time, the centers of gravity of each subinterval are estimated.

In supposed method, at first the interval will be divided into subintervals, showed by . Here we hypothesize that the DMUs have inputs and outputs that are functions of time. Then , are defined as follow: In each subinterval , an indicator can be set. To compute the mention coordinates, the centers of gravity of inputs and outputs are determined. These are contemplating as follows.: the center of gravity of for th input in .: the center of gravity of for th output in .

The center of gravity coordinate is showed as and are calculated by And the and are computed: Because of the above formula, the indicators of each subinterval are determined by These indicators are distinct; then the MPI should be exploited by using (8).

4.2. The Malmquist Productivity Index for Time Dependent Data

At first the centers of gravity of each subinterval are taken into consideration. As the functions are dependent on one variable, the first quantities are not used to calculate. Therefore the center of gravity is If inputs and outputs are available, then notes should be computed; they are remembered by (17).

The MPI index for is considered by in subinterval . All subintervals may be compared with each other. These values show the scope of progress and regress between two consecutive subintervals. If the calculative value is more than 1, this unit has progress, and for less than one the unit demonstrates regress. Otherwise it does not show any changes.

The managers understand DMU behavior in subintervals by using this comparison and have the possibility to apply this data for scientific evaluations.

The goal of this study is to define and set a time dependent function of progress and regress in . By applying this function, man can estimate the unit change in the particular important time.

In Section 2, the Cubic Spline function was offered as a suitable approximation. The one variable function is supposed for the evaluation of MPI of . To determine this function, the values calculated by center of gravity are used. These are the evaluation between and for . Therefore values of MPI are in hand. By using these values, the Malmquist function can be calculated. This function should be a piecewise linear function. This function should be a piecewise linear function. It will be computed in interval by Cubic Spline function as In the vole of hypotheses interval the MPI is computed with (20) for : Function 20 determines the value of MPI at the defined special time. The following cases are possible.(A)If the function value is more than one during consideration time, the DMU has progress.(B)If the function value is less than one during consideration time, the DMU has regress.(C)Otherwise, the intersection of this function and line one is necessary to calculate. The sums of function integral of regions above and under mentioned line 1 should be subtracted. If this difference is more than zero the DMU has progress, and if it is less than zero, the evaluated DMU has regress.

To explain case (C), suppose the above region functions are , , and and the below functions are , , , and ; then, And are the intersections point; according to this, is the criterion of progress and regress.(1)If then the DMU has progress in the evaluated interval.(2)If then the DMU has regress in the evaluated interval.(3)Otherwise the DMU does not have any changes in this evaluation interval.

The proposed method of this study should be assigned with simple calculation and solving linear programing. Therefore this method is very useful and when the Malmquist function is set, for each time, the system programmer can determine the progress and regress with replacing the considered time of this function.

5. Application

In this section, the application of the mentioned method will be reported for studying the progress and regress of nine commercial bank branches in Iran.

These branches have similar time dependent data. The input is the personal value and the relevant outputs are the sum of four deposits and interest, which are linear or nonlinear function of time. The units are as in Table 1.

DMUBranchInputOutput 1Output 2

1 Tabriz

The study interval was . By using (18), the center of the gravity for DMUs is calculated. Each month is supposed to be a subinterval. These centers are shown in Tables 2, 3, 4, 5, 6, 7, 8, 9 and 10.