The Efficiency Distribution Approach in Data Envelopment Analysis: An Application

A method is developed here for characterizing the empirical distribution of the efficient units in data envelopment analysis. Two empirical applications illustrate the various uses of the distribution approach. One involves the cost frontier which exhibits increasing returns to scale and the other involves a dynamic production frontier, where technological change causes a shift of the production frontier over time.     

An MOLP based procedure for finding efficient units in DEA models

In this paper a multiple objective linear programming (MOLP) problem whose feasible region is the production possibility set with variable returns to scale is proposed. By solving this MOLP problem by multicriterion simplex method, the extreme efficient Pareto points can be obtained. Then the extreme efficient units in data envelopment analysis (DEA) with variable returns to scale, considering the specified theorems and conditions, can be obtained. Therefore, by solving the proposed MOLP problem, the non-dominant units in DEA can be found. Finally, a numerical example is provided.     

Estimating most productive scale size using data envelopment analysis

The relation between the most productive scale size (mpss) for paparticular input and output mixes and returns to scale for multiple-inputs multiple-outputs situations is explicitly developed. This relation is then employed to extend the applications of Data Envelopment Analysis (DEA) introduced by Charnes, Cooper and Rhodes (CCR) to the estimation of most productive scale sizes for convex production possibility sets. It is then shown that in addition to productive inefficiencies at the actual scale size, the CCR efficiency measure also reflects any inefficiencies due to divergence from the most productive scale size. Two illustrations of the practical applications of these results to the estimation of most productive scale sizes and returns to scale for hospitals and stem-electric generation plants are also provided emphasize the advantage of this method in examining specific segments of the efficient production surface.     

A full-scale investigation of the directional returns to scale in data envelopment analysis

Returns to scale is considered as one of the important concepts in data envelopment analysis (DEA) which can be useful for deciding to increase or decrease the size of a particular decision making unit. Traditional returns to scale on the efficient surface of the production possibility set with variable returns to scale (VRS) technology is introduced as a ratio of proportional changes of output components to proportional changes of input components. However, a problem which may arise in the real world is the impossibility or undesirability of proportional change in the input or output components. One of the attempts which is made to solve the aforementioned problem is the work of Yang et al., 2014. They have introduced the “directional returns to scale” in the DEA framework and have proposed some procedures to estimate and measure it. In this paper, the introduced directional returns to scale is investigated from a new perspective based on the defining hyperplanes of the production possibility set with VRS technology. We propose some algebraic equations and linear programming models which in addition to measuring the directional returns to scale, they enable us to analyse it. Moreover, we introduce the concepts of the best input and output direction vectors for expansion of input components or compression of output components, respectively, and propose two linear programming models in order to obtain these directions. The presented equations and models are demonstrated using a case study and numerical examples.     

Characterizing a subset of the PPS maintaining the reference hyperplane of the radial projection point

In this paper, we characterize a subset of the production possibility set consisting of production points whose radial projection points lie on the same supporting hyperplane of the production possibility set (PPS). To this end, we consider the CCR and BCC models and establish some theoretical results by utilizing linear programming-based techniques. Determining such a subset of the PPS provides a means to perform sensitivity analysis of inefficient units. This allows us to categorize DMUs into classes with the same returns to scale. Both these issues are addressed as applications.     

Evaluating returns to scale and congestion by production possibility set in intersection form

This research proposes a new method to estimate returns to scale(RTS) of decision making units(DM Us) with multiple inputs and outputs.The state of return to scale includes increasing RTS,constant RTS,decreasing RTS and evidence of congestion.The method is based on the production possibility set in the intersection form given by a set of linear inequalities.We propose and prove the necessary and sufficient conditions for the RTS estimation.With the new procedure,to estimate the RTS of a DM U is simply to ch...     

An interval portfolio selection problem based on regret function

Recent mergers in the banking industry have often generated disappointing shareholder returns. Delays in implementing potential operating savings and realizing benefits of scale economies may be one reason these mergers have disappointing returns. Using data envelopment analysis (DEA), we analyze a 200-branch network formed in a merger of four banks. The operating efficiency of each branch is benchmarked against “best-practice” branches in the combined merged bank as well as “best practice” branches within each pre-merger bank. This analysis identified opportunities to reduce branch operating costs by 22 percent for the entire merged bank. In contrast, the cost savings opportunity is under seven percent when analyzed within each pre-merger bank.These findings suggest benchmarking across the entire merged bank to identify the best practices bank-wide can generate added savings. However, in this bank merger, these merger benefits were not realized until four years after the merger. Interviews with key players in the merged bank indicate that the bank deferred realizing these benefits because of political pressures, personnel integration issues, system integration issues, and financial components of the merger such as restructuring reserves and the purchase price. These causes suggest areas where shareholders can and should demand more rapid improvement in performance of bank mergers and areas for future corporate merger research.     

Finding the efficiency score and RTS characteristic of DMUs by means of identifying the efficient frontier in DEA

Data envelopment analysis (DEA) is basically a linear programming based technique used for measuring the relative performance of organizational units, referred to as decision-making units (DMUs), where the presence of multiple inputs and outputs makes comparisons difficult. The ability of identifying frontier DMUs prior to the DEA calculation is of extreme importance to an effective and efficient DEA computation. In this paper, a method for identifying the efficient frontier is introduced. Then, the efficiency score and returns to scale (RTS) characteristic of DMUs will be produced by means of the equation of efficient frontier.     

Efficiency decomposition for parallel production systems

For production systems composed of parallel processes, the system efficiency will more properly represent the aggregate performance of the component processes if the operation of each process is taken into consideration. Several approaches have been proposed for measuring the efficiency of parallel production systems. By requiring the same factor to have the same virtual multiplier, the proposed method is able to calculate the system and process efficiencies at the same time. Moreover, the former can be decomposed into a weighted average of the latter. A system is efficient only if all of its component processes are efficient. Chemistry departments in the UK are used as an example, where teaching and research are two major functions of the department. The relationship between the system and process efficiencies which holds for the case of constant returns to scale can be extended to the case of variable returns to scale.

     

Parametric multiple sequence alignment and phylogeny construction

Bounds are given on the size of the parameter-space decomposition induced by multiple sequence alignment problems where phylogenetic information may be given or inferred. It is shown that many of the usual formulations of these problems fall within the same integer parametric framework, implying that the number of distinct optima obtained as the parameters are varied across their ranges is polynomially bounded in the length and number of sequences.     

Incentive and production decisions for remanufacturing operations

We consider a manufacturer producing original products using virgin materials and remanufactured products using returns from the market where the amount of returns depend on the incentive offered by the manufacturer. We determine the optimal value of this incentive and the optimal production quantities in a stochastic demand setting with partial substitution. We analyze 3 different models in centralized and decentralized settings where the collection process of the returns is managed by a collection agency in the decentralized setting. We also analyze contracts to coordinate the decentralized systems and determine the optimal contract parameters. Finally, we present our computational study to observe the effect of different parameters on the system performance.     

Measuring returns to scale in DEA models when the firm is regulated

In the standard framework of data envelopment analysis (DEA) models, the returns to scale are fully characterized using the multiplier on the convexity constraint of inefficient decision making units (DMU) using the projection of the input–output vector on the frontier. In this note, we investigate how the returns to scale measurements in DEA models are affected by the presence of regulatory constraints. These additional constraints change the role played by the convexity constraint. In order to avoid biased estimation of the returns to scale, we show that the interaction between the regulatory and the convexity constraints has to be taken into account.     

How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement

Monte Carlo experimentation is a well-known approach used to test the performance of alternative methodologies under different hypotheses. In the frontier analysis framework, whatever the parametric or non-parametric methods tested, experiments to date have been developed assuming single output multi-input production functions. The data generated have mostly assumed a Cobb–Douglas technology. Among other drawbacks, this simple framework does not allow the evaluation of DEA performance on scale efficiency measurement. The aim of this paper is twofold. On the one hand, we show how reliable two-output two-input production data can be generated using a parametric output distance function approach. A variable returns to scale translog technology satisfying regularity conditions is used for this purpose. On the other hand, we evaluate the accuracy of DEA technical and scale efficiency measurement when sample size and output ratios vary. Our Monte Carlo experiment shows that the correlation between true and estimated scale efficiency is dramatically low when DEA analysis is performed with small samples and wide output ratio variations.     

Generating strong defining hyperplanes of the production possibility set in data envelopment analysis

In data envelopment analysis (DEA), identification of the strong defining hyperplanes of the empirical production possibility set (PPS) is important, because they can be used for determining rates of change of outputs with change in inputs. Also, efficient hyperplanes determine the nature of returns to scale. The present work proposes a method for generating all linearly independent strong defining hyperplanes (LISDHs) of the PPS passing through a specific decision making unit (DMU). To this end, corresponding to each efficient unit, a perturbed inefficient unit will be defined and, using at most m+s linear programs, all LISDHs passing through the DMU will be determined, where m and s are the numbers of inputs and outputs, respectively.     

On alternative optimal solutions in the estimation of returns to scale in DEA

Zhu and Shen [European Journal of Operational Research 81 (1995) 590] show that alternative optimal solutions in the estimation of returns to scale (RTS) are caused by a particular linear dependency among a set of extreme efficient DMUs when one employs the concept of most productive scale size [European Journal of Operational Research 17 (1984) 35] in data envelopment analysis (DEA). This paper demonstrates that the presence of weakly efficient DMUs may also lead to alternative optima and extends the results of Zhu and Shen to the entire frontier. Necessary and sufficient conditions for the presence of multiple optimal solutions for constant returns to scale (CRS) DMUs are established.     

Returns to scale and the random production of innovations

I define constant, increasing and decreasing returns to scale in the production of innovations that occur randomly with a probability that depends upon resources spent in research. I analyse the mathematical representations of random processes of innovation that exhibit constant, increasing or decreasing returns to scale in that sense and determine their respective functional forms. I also give two complementary conditions, which are respectively sufficient for increasing returns to scale, and decreasing returns. Finally, as a particular case, I show processes that use only one factor of innovation and satisfy constant returns form a one—parameter family.     

Decomposing technical efficiency and scale elasticity in two-stage network DEA

The constant returns to scale assumption maintained by neoclassical theorists for justifying the black-box structure of production technology in long run does not necessarily allow one to infer that there are no scale benefits available in its sub-technologies. Most of real-life production technologies are multi-stage in nature, and the sources of increasing returns lie in the sub-technologies. It is, therefore, imperative to estimate the scale economies of a firm not only for the network technology but also for the sub-technologies. To accomplish this, two approaches are suggested in this contribution, based on the premise concerning whether a network technology construct considers allocative inefficiency. The first approach, which is ours, makes use of a single network technology for two interdependent sub-technologies. The second approach, which is due to Kao and Hwang (2011), however, assumes complete allocative efficiency by considering two independent sub-technology frontiers, one for each sub-technology. The distinction between these two approaches is important from a policy point of view since the network efficiencies revealed from these two approaches have distinctive causative factors that do not permit them to be used interchangeably.     

The utility of returns to scale in DEA programming: An analysis of Michigan rural hospitals

In 1984, Banker, Charnes, and Cooper introduced the capability of using data envelopment analysis to assess increasing, decreasing, or constant returns to scale. This analysis would appear to make an important contribution to the health care field because of the regulatory environment within which the industry exists and the competition among hospitals for additional services and capacity. In many states, hospitals must submit a “certificate of need” to prove eligibility to add capacity or services. Agency administrators at the state level should analyze each hospital's production performance to determine the effectiveness of resource utilization. Residents of a state where hospitals are regulated need to know the effectiveness of agencies in allowing resources to be properly allocated to hospitals. Returns to scale analysis can help provide answers to these concerns. We examine Michigan rural hospitals and propose a simple, yet logical procedure for evaluating returns to scale for technically inefficient hospitals.     

Calculating scale elasticity in DEA models

In the data envelopment analysis (DEA) efficiency literature, qualitative characterizations of returns to scale (increasing, constant, or decreasing) are most common. In economics it is standard to use the scale elasticity as a quantification of scale properties for a production function representing efficient operations. Our contributions are to review DEA practices, apply the concept of scale elasticity from economic multi-output production theory to DEA piecewise linear frontier production functions, and develop formulas for scale elasticity for radial projections of inefficient observations in the relative interior of fully dimensional facets. The formulas are applied to both constructed and real data and show the differences between scale elasticities for the two valid projections (input and output orientations). Instead of getting qualitative measures of returns to scale only as was done earlier in the DEA literature, we now get a quantitative range of scale elasticity values providing more information to policy-makers.     

规模收益状况的动态因素分析

基于DEA理论的"交形式"生产可能集,从投入增大和缩小两种角度,对多投入多产出生产系统的各种规模收益状态进行分析,研究部分投入要素变化对部分产出要素的作用效果,即对规模收益状况给予"动态"因素分析,得到判定部分投入与部分产出之间规模收益各种状态的充要条件,从而为研究规模收益各状态的产生原因提供理论依据.