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The aim of this research study is to develop a queue assessment model to evaluate the inflow of walk-in outpatients in a busy public hospital of an emerging economy, in the absence of appointment systems, and construct a dynamic framework dedicated towards the practical implementation of the proposed model, for continuous monitoring of the queue system.

The current study utilizes data envelopment analysis (DEA) to develop a combined queuing–DEA model as applied to evaluate the wait times of patients, within different stages of the outpatients' department at the Combined Military Hospital (CMH) in Lahore, Pakistan, over a period of seven weeks (23rd April to 28th May 2014). The number of doctors/personnel and consultation time were considered as outputs, where consultation time was the non-discretionary output. The two inputs were wait time and length of queue. Additionally, VBA programming in Excel has been utilized to develop the dynamic framework for continuous queue monitoring.

The inadequate availability of personnel was observed as the critical issue for long wait times, along with overcrowding and variable arrival pattern of walk-in patients. The DEA model displayed the “required” number of personnel, corresponding to different wait times, indicating queue build-up.

The current study develops a queue evaluation model for a busy outpatients' department in a public hospital, where “all” patients are walk-in and no appointment systems. This model provides vital information in the form of “required” number of personnel which allows the administrators to control the queue pre-emptively minimizing wait times, with optimal yet dynamic staff allocation. Additionally, the dynamic framework specifically targets practical implementation in resource-poor public hospitals of emerging economies for continuous queue monitoring.

This research had two main objectives: To measure the productivity of companies in the Colombian dairy industry and to identify efficient decision-making units (DMUs) that can be used as work sample in future case studies.

In the measurement of productivity, financial variables were considered for a sample of 19 DMUs. Efficient companies were identified through the data envelopment analysis (DEAs) methodology with the VRS model oriented to inputs and outputs. The input variables analyzed were “current asset,” “property, plant and equipment,” “non-current liability” and “equity,” while the output variables were “revenue” and “profit.”

Findings revealed that seven DMUs are efficient in the input and output orientation and that companies of different sizes and with or without quality certifications are efficient in the sample analyzed. Additionally, the benchmark efficient DMUs were identified for each of the non-efficient DMUs.

The implications for the research include the contribution to the theory, on the one hand, with the analysis of the current state of the literature on the use of DEA in the food sector, and on the other, with the use of DEA to measure the productivity of Colombian dairy industry companies and with the identification of a sample of efficient units that can be analyzed in future case studies.

This article is novel and pioneering because it measures for the first time the productivity of DMUs of the Colombian dairy industry, in addition to including the current state of the literature on the application of the DEA methodology in the food sector. These findings contribute to the consolidation of the theory and also provide inputs for researchers, practitioners, managers, and policy makers.

Issues of performance measurement are ubiquitous in modern organizations and are often concerned with evaluations of outputs or efficiency (which encompasses both inputs and outputs) of an entity or process. Examples of output measures include revenue generated, defective units produced, on-time shipments, etc. Efficiency examples include standard cost variances, machine up-time rate, and efficiency scores from input–output models such as Data Envelopment Analysis (DEA).

Difficult-to-measure outputs are often included even though they cannot be measured with precision. When outputs of a production process are not easy to measure, serious dysfunctional decision-making can be expected and these problems may be particularly acute when efficiency measurements from input–output models are directly tied to rewards and incentives. Both for-profit firms and public sector organizations may share output measurability problems.

In this paper, we examine the possible problems of using input–output models (such as DEA) when outputs are difficult to quantify within an agency theory perspective and illustrate the potential problems using recent proposals in the UK for evaluating and rewarding police unit performance. We conclude that although input–output models, particularly those such as DEA may be useful as a diagnostic tool to assist decision-makers in altering future operating strategies and policies, it has serious limitations when rewards and incentives are attached to the DEA performance evaluations. In our view, overreliance on mechanical, formula-based approaches is potentially a serious threat to improving performance in these situations.

The purpose of this paper is to measure the technical and scale efficiency of health centres; to evaluate changes in productivity; and to highlight possible policy implications of the results for policy makers.

Data envelopment analysis (DEA) is employed to assess the technical and scale efficiency, and productivity change over a four‐year period among 17 public health centres.

During the period of study, the results suggest that the public health centres in Seychelles have exhibited mean overall or technical efficiency of above 93 per cent. It was also found that the overall productivity increased by 2.4 per cent over 2001‐2004.

Further research can be undertaken to gather data on the prices of the various inputs to facilitate an estimation of the allocative efficiency of clinics. If such an exercise were to be undertaken, researchers may also consider collecting data on quantities and prices of paramedical, administrative and support staff to ensure that the analysis is more comprehensive than the study reported in this paper. Institutionalization of efficiency monitoring would help to enhance further the already good health sector stewardship and governance.

This paper provides new empirical evidence on a four‐year trend in the efficiency and productivity of health centres in Seychelles.

A binary integer programming model for the simple assembly line balancing problem (SALBP), which is well known as SALBP‐1, was formulated more than 30 years ago. Since then, a number of researchers have extended the model for the variants of assembly line balancing problem. The model is still prevalent nowadays mainly because of the lower and upper bounds on task assignment. These properties avoid significant increase of decision variables. The purpose of this paper is to use an example to show that the model may lead to a confusing solution.

The paper provides a remedial constraint set for the model to rectify the disordered sequence problem.

The paper presents proof that the assembly line balancing model formulated by Patterson and Albracht may lead to a confusing solution.

No one previously has found that the commonly used model is incorrect.

The purpose of this paper is to assess the performance of Greek fossil fuel‐fired power stations employing a data envelopment analysis (DEA) model combined with bootstrapping.

DEA is used to derive aggregate performance indicators using data on inputs and desirable and undesirable outputs for a sample of fossil fuel‐fired power stations. The statistical significance of the derived aggregate performance indicators is assessed via the bootstrapping approach.

The results suggest that the power stations in the sample are considerably more inefficient than revealed by the initial point estimates of inefficiency. Moreover, the non‐lignite‐fired stations of the sample are on an average more efficient than the lignite‐fired stations.

DEA represents a useful framework for exploring the current state to derive aggregate performance indicators of power stations, and moreover, the statistical properties of these metrics can be assessed via the bootstrapping approach.

The bootstrapping approach in DEA shows its superiority over DEA models that do not address the uncertainty surrounding point estimates. The DEA bootstrapping model used in this study to model environmental performance in the power station electricity production setting provides bias correction and confidence intervals for the point estimates and it is therefore more preferable.

The derivation of aggregate performance indicators of Greek fossil fuel‐fired power stations is an important addition to the existing literature on energy economics. The paper is also innovated in providing the statistical properties of the derived performance metrics.

The paper seeks to evaluate the changes in efficiency and productivity of coal‐fired electricity generation of 30 Chinese administrative regions from 1999 to 2007.

The paper incorporates data envelopment analysis with the Malmquist index to study the progress made in this sector. The model considers both economic and environmental factors by including the variables fuel consumption, labor, capital, sulfur dioxide emissions and electricity generated. A second model is constructed without the variable sulfur dioxide emissions to evaluate economic performances without taking environmental measures into consideration.

By comparing the two models, the paper identified provinces that favored economic performance over environmental performance, or vice versa. Also, it showed that the more efficient provinces tend to manage both economic and environmental efficiencies equally well, while the reverse is true for the least efficient provinces. The average total factor productivity growth in coal‐fired electricity generation of all provinces was 3.96 per cent for 1999‐2007, and this growth is mainly attributed to technological change. In addition, it found that the Eastern provinces are the most efficient and productive of the group.

In the absence of provincial coal quality data, a key efficiency factor is missing from the analysis.

Efficiency improvement efforts in the Chinese generation sector should target the least efficient provinces identified in this paper. Practices in the most efficient provinces should be further investigated to be replicated when possible.

The paper provides a contemporary overview of Chinese provincial efficiency and productivity measures for policy makers and investors to improve China's coal‐fired electricity generation sector.

The purpose of this paper is to present an integrated algorithm composed of data envelopment analysis (DEA), corrected ordinary least squares (COLS) and principal component analysis (PCA) to estimate efficiency scores of electricity distribution units.

Several DEA and COLS models are prescribed and their results are verified and validated by the algorithm. To calculate efficiency scores, three standard internal consistency conditions between DEA and COLS results are checked by the algorithm. If these conditions are satisfied, DEA is chosen as the superior model because it could be used for optimization as well. Otherwise, the geometric mean of DEA and COLS model is used as the final efficiency scores.

The algorithm of this paper may be easily applied to decision‐making units because of its robustness (combined DEA‐COLS input and output) and validity gained through PCA.

The integrated approach has several unique features which are: verification and validation mechanism by PCA, consideration of internal consistency conditions between DEA and COLS and consolidation of DEA and COLS for improved ranking given consistency conditions are violated.

Since the early 1990s, the US natural gas sector is characterized by substantial industry restructuring as a result of regulatory requirements following a liberalization objective. Accordingly, firms have chosen different strategies as a reaction of regulatory constraints, such as acquisitions, forming holding companies, or joint venture investment. In order to contribute to the success of business strategies driven by the regulatory framework, the purpose of this paper is to provide first evidence for the impacts of business strategies on a firm's efficiency within the gas transmission business.

The approach taken is a stochastic frontier analysis.

The results highlighted significant effects of acquisitions, holding structures, and joint ventures within the industry on the firms' technical efficiency estimates. The paper finds evidence for efficiency improvements caused by the new shareholder. The results suggested those holdings to be successful which have great experience in the construction and operation of oil pipelines. In the case that firms formed a joint venture to construct and operate a gas transportation pipeline, the empirical analysis showed a negative impact of such a joint venture on technical efficiency of the pipeline company.

The paper provides evidence for the missing link between operating performance and the evaluation of different business strategies in terms of efficiency within the natural gas interstate pipeline industry.