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.     

Nonparametric estimation of returns to scale in the public sector with an application to the provision of educational services

Nonparametric programming models have been developed to measure technical efficiency and scale economies. The programming models used for public sector applications, however, are based on standard private sector production theory. In the public sector environmental variables have a significant impact on the provision of public services. Without controlling for these environmental factors point estimates of efficiency and returns to scale will be biased. This paper extends nonparametric methods to allow measurement of returns to scale in the provision of public services. The method is applied to the provision of educational services in New York State school districts for illustrative purposes.     

The relationship between the efficiency of orthopedic wards and the socio-economic status of their patients

The purpose of this paper is to study the effect of the socio-economic status of patients on the efficiency of orthopedic wards in acute hospitals in Israel (20 hospitals), from the viewpoint of the regulator—Israel Ministry of Health. At the first stage, data envelopment analysis is used with two inputs, and three outputs, where one output is undesirable—“number of deaths”—which also reflects the quality of the health services. At the second stage, various nonparametric tests are utilized to test the relationship between the socio-economic status of patients and the efficiency. As by-product DEA provides benchmark analysis, which indicates the peers of each inefficient ward, and the I/O improvements are needed for achieving efficiency. Two versions of DEA were used: the output oriented version (variable returns to scale), and the non-oriented version (Additive). Further analysis provides comparison of the results with other simple efficiency measures. We also compare between the efficiency from the regulator viewpoint and the hospitals’ viewpoint.     

Evaluation of a new maintenance concept for the preservation of highways

Asphalt roads gradually deteriorate over time and need mendingto remain in a good state. Inspections are performed to obtaininformation about the current state of the road; when the levelof damage reaches the established standards, the road is repairedthrough a maintenance action that is considered most cost-effective.Because of economies of scale, it is to a certain extent economicalto limit the number of maintenance services by integrating neighbouringsegments into a homogeneous section which is completely repaired.In this report we consider the question of whether it is economicallyjustified to adopt a maintenance concept in which the road isoccasionally regenerated from junction to junction with a distanceof several kilometers. The big advantage of junction-to-junctionmaintenance is that it can be done within special road barrierconstructions which reduce the road capacity only slightly,so that the road can be maintained 24 hours a day.     

Time substitution with application to data envelopment analysis

In this paper we analyze resource allocation distinguishing between the decision of when to begin allocation and over how many periods to apply the resources. We present analytical results for specific production technologies under different returns to scale assumptions, under capacity constraints and for production with technical change. Using a dynamic activity analysis framework we show how to compute in general optimal solutions for resource intensity use.     

Performance and congestion analysis of the portuguese hospital services

The health care services have been characterized by a growing demand by the citizens leading to the need of more and more resources. Population aging, new pathologies, drugs, as well as new treatments are some of the major factors for this. However, in hospitals, for example, consumption of a large number of inputs has not frequently corresponded to the production of the same or more proportion of outputs. Sometimes, the outputs even decline with the increase of inputs due to the influence of the congestion effect on efficiency. The heavy burden of the health sector on the state budget brings about the interest of research over its efficiency. This paper aims to assess the performance of the Portuguese hospitals and particularly the contribution of the congestion effect. We use the non-parametric technique of data envelopment analysis for this purpose and a double-bootstrap procedure to take into account the influence of operational environment on efficiency. Afterwards, by comparing three different approaches, we determine the importance of congestion in efficiency measurement and discuss its computation methodologically. The results suggest significant levels of inefficiency in 68 major Portuguese hospitals for the year 2005 and more than half of them were found to be congested.     

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.     

基于三阶段SBM模型的中国节能服务上市企业效率研究

节能服务企业效率对于推动节能服务产业发展和确保国家“双碳”目标实现具有重要意义。运用三阶段SBM模型,对2020年60家中国节能服务上市企业的综合效率进行实证研究,并分析效率的影响因素。研究结果表明,经济发展水平对综合效率具有双重影响,合适的产业结构和较高的投入资本回报率有利于综合效率提升,而政府补贴与企业存续时间制约了综合效率改进;剔除环境因素与随机误差影响后,多数企业综合效率下降,整体效率偏低;提高规模效率是提升综合效率的关键,且绝大部分企业处于规模收益递增状态。研究最后提出创新补贴方式、建立企业认证制度、开展综合能源服务和推动企业间兼并重组等可行建议,以提升节能服务企业的综合效率。     

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.     

Improving the identification of returns to scale in data envelopment analysis

In order to assess the efficiencies of a set of production units, it is necessary to identify the nature of returns to scale which characterise efficient production. Some methods have been developed to test the nature of the scale elasticity across the full range of scale sizes. However these tests are heavily weighted by the majority of the units and may not identify small ranges of scale size where different returns to scale hold. This paper develops a procedure based on a combination of Data Envelopment Analysis and regression analysis to identify the ranges of scale size where the returns to scale may differ from those in other ranges for the single-output, multi-input case. We also develop a measure of scale size across different input mixes.     

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.     

Measurement of returns to scale in radial DEA models

A general approach is proposed in order to measure returns to scale and scale elasticity at projections points in the radial data envelopment analysis (DEA) models. In the first stage, a relative interior point belonging to the optimal face is found using a special, elaborated method. In previous work it was proved that any relative interior point of a face has the same returns to scale as any other interior point of this face. In the second stage, we propose to determine the returns to scale at the relative interior point found in the first stage.     

考虑服务和退货的双渠道供应链定价问题研究

在考虑零售服务和消费者退货情况下,运用两阶段优化方法分别探究了集中式和分散式两种双渠道供应链的最优定价策略。研究发现零售服务有助于提高零售商的渠道权力,并始终使制造商获益,但只有当服务水平小于某一临界值时,才使制造商和零售商同时得到帕累托改进。零售价格和直销价格与退货率正相关,而与服务水平的关系受渠道结构的影响。服务水平的提高加剧了供应链的“双重边际化”效应,制造商通过生产与消费者需求相匹配的产品来降低退货风险可以减弱这种效应,从而提高供应链效率。     

Home health care routing and scheduling problems: a literature review

4OR - Home health services arise from the need for hospitals to care for patients and/or dependent persons who, due to special conditions, require hospitalisation and/or care at home. The...     

Measurement of returns to scale using non-radial DEA models

There are some specific features of the non-radial data envelopment analysis (DEA) models which cause some problems for the returns to scale measurement. In the scientific literature on DEA, some methods were suggested to deal with the returns to scale measurement in the non-radial DEA models. These methods are based on using Strong Complementary Slackness Conditions from optimization theory. However, our investigation and computational experiments show that such methods increase computational complexity significantly and may generate as optimal, solutions contradicting optimization theory. In this paper, we propose and substantiate a direct method for the returns to scale measurement in the non-radial DEA models. Our computational experiments documented that the proposed method works reliably and efficiently on the real-life data sets.     

A new non-oriented model for classifying flexible measures in DEA

In conventional data envelopment analysis (DEA), measures are classified as either input or output. However, in some real cases there are variables which act as both input and output and are known as flexible measures. Most of the previous suggested models for determining the status of flexible measures are oriented. One important issue of these models is that unlike standard DEA, even under constant returns to scale the input- and output-oriented model may produce different efficiency scores. Also, can be expected a flexible measure is selected as an input variable in one model but an output variable in the other model. In addition, in all of the previous studies did not point to variable returns to scale (VRS), but the VRS assumption is prevailed on many real applications. To deal with these issues, this study proposes a new non-oriented model that not only selects the status of each flexible measure as an input or output but also determines returns to scale status. Then, the aggregate model and an extension with the negative data related to the proposed approach are presented.     

Irreversible capital accumulation under interest rate uncertainty

We consider the optimal sequential irreversible investment policy of a value maximizing firm facing decreasing returns to scale and interest rate uncertainty. We characterize the optimal accumulation policy and its value for a broad class of diffusion models of the short interest rate by focusing on the marginal investment decision and deriving the marginal value of capital explicitly. We also state a set of conditions under which there is a maximal capital stock above which the option to expand productive capacity further in the future becomes valueless. Hence, our results indicate that interest rate uncertainty may limit the size of an optimally investing firm.     

DEA model with shared resources and efficiency decomposition

Data envelopment analysis (DEA) has proved to be an excellent approach for measuring performance of decision making units (DMUs) that use multiple inputs to generate multiple outputs. In many real world scenarios, DMUs have a two-stage network process with shared input resources used in both stages of operations. For example, in hospital operations, some of the input resources such as equipment, personnel, and information technology are used in the first stage to generate medical record to track treatments, tests, drug dosages, and costs. The same set of resources used by first stage activities are used to generate the second-stage patient services. Patient services also use the services generated by the first stage operations of housekeeping, medical records, and laundry. These DMUs have not only inputs and outputs, but also intermediate measures that exist in-between the two-stage operations. The distinguishing characteristic is that some of the inputs to the first stage are shared by both the first and second stage, but some of the shared inputs cannot be conveniently split up and allocated to the operations of the two stages. Recognizing this distinction is critical for these types of DEA applications because measuring the efficiency of the production for first-stage outputs can be misleading and can understate the efficiency if DEA fails to consider that some of the inputs generate other second-stage outputs. The current paper develops a set of DEA models for measuring the performance of two-stage network processes with non splittable shared inputs. An additive efficiency decomposition for the two-stage network process is presented. The models are developed under the assumption of variable returns to scale (VRS), but can be readily applied under the assumption of constant returns to scale (CRS). An application is provided.     

Proactive data envelopment analysis: Effective production and capacity expansion in stochastic environments

Demand fluctuations that cause variations in output levels will affect a firm’s technical inefficiency. To assess this demand effect, a demand-truncated production function is developed and an “effectiveness” measure is proposed. Often a firm can adjust some input resources influencing the output level in an attempt to match demand. We propose a short-run capacity planning method, termed proactive data envelopment analysis, which quantifies the effectiveness of a firm’s production system under demand uncertainty. Using a stochastic programming DEA approach, we improve upon short-run capacity expansion planning models by accounting for the decreasing marginal benefit of inputs and estimating the expected value of effectiveness, given demand. The law of diminishing marginal returns is an important property of production function; however, constant marginal productivity is usually assumed for capacity expansion problems resulting in biased capacity estimates. Applying the proposed model in an empirical study of convenience stores in Japan demonstrates the actionable advice the model provides about the levels of variable inputs in uncertain demand environments. We conclude that the method is most suitable for characterizing production systems with perishable goods or service systems that cannot store inventories.     

Measuring super-efficiency in DEA in the presence of infeasibility

Super-efficiency data envelopment analysis (DEA) model is obtained when a decision making unit (DMU) under evaluation is excluded from the reference set. Because of the possible infeasibility of super-efficiency DEA model, the use of super-efficiency DEA model has been restricted to the situations where constant returns to scale (CRS) are assumed. It is shown that one of the input-oriented and output-oriented super-efficiency DEA models must be feasible for a any efficient DMU under evaluation if the variable returns to scale (VRS) frontier consists of increasing, constant, and decreasing returns to scale DMUs. We use both input- and output-oriented super-efficiency models to fully characterize the super-efficiency. When super-efficiency is used as an efficiency stability measure, infeasibility means the highest super-efficiency (stability). If super-efficiency is interpreted as input saving or output surplus achieved by a specific efficient DMU, infeasibility does not necessary mean the highest super-efficiency.