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1 – 10 of over 1000The traditional data envelopment analysis (DEA) model as a non-parametric technique can measure the relative efficiencies of a decision-making units (DMUs) set with exact values…
Abstract
Purpose
The traditional data envelopment analysis (DEA) model as a non-parametric technique can measure the relative efficiencies of a decision-making units (DMUs) set with exact values of inputs and outputs, but it cannot handle the imprecise data. The purpose of this paper is to establish a super efficiency interval data envelopment analysis (IDEA) model, an IDEA model based on cross-evaluation and a cross evaluation-based measure of super efficiency IDEA model. And the authors apply the proposed approach to data on the 29 public secondary schools in Greece, and further demonstrate the feasibility of the proposed approach.
Design/methodology/approach
In this paper, based on the IDEA model, the authors propose an improved version of establishing a super efficiency IDEA model, an IDEA model based on cross-evaluation, and then present a cross evaluation-based measure of super efficiency IDEA model by combining the super efficiency method with cross-evaluation. The proposed model cannot only discriminate the performance of efficient DMUs from inefficient ones, but also can distinguish between the efficient DMUs. By using the proposed approach, the overall performance of all DMUs with interval data can be fully ranked.
Findings
A numerical example is presented to illustrate the application of the proposed methodology. The result shows that the proposed approach is an effective and practical method to measure the efficiency of the DMUs with imprecise data.
Practical implications
The proposed model can avoid the fact that the original DEA model can only distinguish the performance of efficient DMUs from inefficient ones, but cannot discriminate between the efficient DMUs.
Originality/value
This paper introduces the effective method to obtain the complete rank of all DMUs with interval data.
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Zoubida Chorfi, Abdelaziz Berrado and Loubna Benabbou
Evaluating the performance of supply chains is a convoluted task because of the complexity that is inextricably linked to the structure of the aforesaid chains. Therefore, the…
Abstract
Purpose
Evaluating the performance of supply chains is a convoluted task because of the complexity that is inextricably linked to the structure of the aforesaid chains. Therefore, the purpose of this paper is to present an integrated approach for evaluating and sizing real-life health-care supply chains in the presence of interval data.
Design/methodology/approach
To achieve the objective, this paper illustrates an approach called Latin hypercube sampling by replacement (LHSR) to identify a set of precise data from the interval data; then the standard data envelopment analysis (DEA) models can be used to assess the relative efficiencies of the supply chains under evaluation. A certain level of data aggregation is suggested to improve the discriminatory power of the DEA models and an experimental design is conducted to size the supply chains under assessment.
Findings
The newly developed integrated methodology assists the decision-makers (DMs) in comparing their real-life supply chains against peers and sizing their resources to achieve a certain level of production.
Practical implications
The proposed integrated DEA-based approach has been successfully implemented to suggest an appropriate structure to the actual public pharmaceutical supply chain in Morocco.
Originality/value
The originality of the proposed approach comes from the development of an integrated methodology to evaluate and size real-life health-care supply chains while taking into account interval data. This developed integrated technique certainly adds value to the health-care DMs for modelling their supply chains in today's world.
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Yao Wen, Qingxian An, Xuanhua Xu and Ya Chen
This paper aims to prioritize the most efficient Six Sigma project that can generate the greatest benefit to the organization, according to the relative performance among a set of…
Abstract
Purpose
This paper aims to prioritize the most efficient Six Sigma project that can generate the greatest benefit to the organization, according to the relative performance among a set of homogenous projects (in here, DMUs). The selection of a Six Sigma project is a multiple-criteria decision-making problem, which is difficult in practice because the projects are not yet complete and the values of evaluation indicators are often interval or imprecise data. Managers stress the need for developing an effective performance evaluation methodology for selecting a Six Sigma project.
Design/methodology/approach
This study proposes a modified model considering interval or imprecise data based on common weight data envelopment analysis (DEA) approach to solve problems on project selection.
Findings
By comparing its findings with an example from a previous study, the new model obtained realistic and fair evaluation results and significantly reduced the difficulties and the time spent during calculation. Moreover, not only the best project is identified, but also the exact indicator information is obtained.
Originality/value
This study solves the problem of selecting the most efficient Six Sigma project in the preference of interval or imprecise data. Many studies have shown how a Six Sigma project is chosen, but only a few have integrated interval data into the selection process.
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Yasaman Zibaei Vishghaei, Sohrab Kordrostami, Alireza Amirteimoori and Soheil Shokri
Assessing inputs and outputs is a significant aspect of taking decisions while there are complex and multistage processes in many examinations. Due to the presence of interval…
Abstract
Purpose
Assessing inputs and outputs is a significant aspect of taking decisions while there are complex and multistage processes in many examinations. Due to the presence of interval performance measures in various real-world studies, the purpose of this study is to address the changes of interval inputs of two-stage processes for the perturbations of interval outputs of two-stage systems, given that the overall efficiency scores are maintained.
Design/methodology/approach
Actually, an interval inverse two-stage data envelopment analysis (DEA) model is proposed to plan resources. To illustrate, an interval two-stage network DEA model with external interval inputs and outputs and also its inverse problem are suggested to estimate the upper and lower bounds of the entire efficiency and the stages efficiency along with the variations of interval inputs.
Findings
An example from the literature and a real case study of the banking industry are applied to demonstrate the introduced approach. The results show the proposed approach is suitable to estimate the resources of two-stage systems when interval measures are presented.
Originality/value
To the best of the authors’ knowledge, there is no study to estimate the fluctuation of imprecise inputs related to network structures for the changes of imprecise outputs while the interval efficiency of network processes is maintained. Accordingly, this paper considers the resource planning problem when there are imprecise and interval measures in two-stage networks.
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Mehdi Abbasi and Mohamad Amin Kaviani
Competition in dynamic markets requires operational excellence and effective operations strategy. Hence, organizations have to evaluate their operational performance in comparison…
Abstract
Purpose
Competition in dynamic markets requires operational excellence and effective operations strategy. Hence, organizations have to evaluate their operational performance in comparison to their rivals. The purpose of this paper is to present an operational performance evaluation framework for analyzing and ranking the organizations based on the effectiveness of their operations strategies.
Design/methodology/approach
In this study three uncertain data envelopment analysis (DEA) models consist of fuzzy DEA (FDEA), imprecise DEA (IDEA) and Grey DEA are applied to assess the performance of organizations in terms of the operations. The applicability of the proposed framework is demonstrated through a case study at Fars province cement manufacturers of Iran.
Findings
The ranking results assist the mangers of service and manufacturing enterprises in finding out the current positions of their companies and the effectiveness of their operations strategies in comparison to their rivals. Accordingly they can make proper strategic and operational decisions to improve the performance of the operations.
Originality/value
Based on the previous literature, this is the first study which uses FDEA and IDEA methods in an operational performance evaluation problem. Furthermore this study proposes a novel framework for evaluating and ranking the operational performance of firms from the perspective of operations strategy.
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The purpose of this paper is to propose a straightforward model for selecting slightly non‐homogeneous vendors.
Abstract
Purpose
The purpose of this paper is to propose a straightforward model for selecting slightly non‐homogeneous vendors.
Design/methodology/approach
In this paper the use of the interval data envelopment analysis (DEA) is suggested. The bounds of intervals are constant and can be obtained by various estimation techniques. The interval DEA model provides for the decision making units (DMUs) with missing values a lower and an upper bound of their efficiency score corresponding to their most favorable and unfavorable option.
Findings
Employing the proposed method for selecting slightly non‐homogeneous vendors largely reduced practical difficulties for vendor selection. This method does not exclude any vendor from the selection problem. For all the vendors it provides bounds of the efficiency scores depended on the particular data values that the vendors with missing data assign within the intervals so to maximize their efficiency score.
Practical implications
The proposed model considers a slightly non‐homogeneous situation for vendor selection. The proposed approach is driven by multiple criteria. The joint consideration of multiple criteria in a slightly non‐homogeneous environment helps managers select vendors using a comprehensive approach that goes beyond just purchase costs.
Originality/value
This paper is believed to be the first to discuss the problem of slightly non‐homogeneous vendor selection with respect to interval mathematics.
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Rokhsaneh Yousef Zehi and Noor Saifurina Nana Khurizan
Uncertainty in data, whether in real-valued or integer-valued data, may result in infeasible optimal solutions or unreliable efficiency scores and ranking of decision-making…
Abstract
Purpose
Uncertainty in data, whether in real-valued or integer-valued data, may result in infeasible optimal solutions or unreliable efficiency scores and ranking of decision-making units. To handle the uncertainty in integer-valued factors in data envelopment analysis (DEA) models, this study aims to propose a robust DEA model which is applicable in the presence of such factors.
Design/methodology/approach
This research focuses on the application of fuzzy interpretation of efficiency to a mixed-integer DEA (MIDEA) model. The robust optimization approach is used to address the uncertain integer-valued parameters in the proposed MIDEA model.
Findings
In this study, the authors proposed an MIDEA model without any equality constraint to avoid the arise problem by such constraints in the construction of the robust counterpart of the conventional MIDEA models. We have studied the characteristics and conditions for constructing the uncertainty set with uncertain integer-valued parameters and a robust MIDEA model is proposed under a combined box-polyhedral uncertainty set. The applicability of the developed models is shown in a case study of Malaysian public universities.
Originality/value
This study develops an MIDEA model equivalent to the conventional MIDEA model excluding any equality constraint which is crucial in robust approach to avoid restricted feasible region or infeasible solutions. This study proposes a robust DEA approach which is applicable in cases with uncertain integer-valued parameters, unlike previous studies in robust DEA field where uncertain parameters are generally assumed to be only real-valued.
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The purpose of this paper is to estimate technical efficiency obtained from both data envelopment analysis (DEA) and stochastic frontier approaches using household survey data for…
Abstract
Purpose
The purpose of this paper is to estimate technical efficiency obtained from both data envelopment analysis (DEA) and stochastic frontier approaches using household survey data for rice farming households in Vietnam.
Design/methodology/approach
A bootstrap method is used to provide statistical precision of the DEA estimator. Technical efficiency is modeled as a function of household and production factors.
Findings
The results from the deterministic, semi‐parametric and parametric approaches indicate that among other things, technical efficiency is significantly influenced by primary education and regional factors. In addition, scale efficiency analysis shows that many farms in Vietnam are operating with less than optimal scale of operation.
Originality/value
The study is among the first that employ a bootstrap method and compare estimates from both Data Envelopment Analysis (DEA) and stochastic frontier approaches.
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Sifeng Liu, Yingjie Yang, Naiming Xie and Jeffrey Forrest
The purpose of this paper is to summarize the progress in grey system research during 2000-2015, so as to present some important new concepts, models, methods and a new framework…
Abstract
Purpose
The purpose of this paper is to summarize the progress in grey system research during 2000-2015, so as to present some important new concepts, models, methods and a new framework of grey system theory.
Design/methodology/approach
The new thinking, new models and new methods of grey system theory and their applications are presented in this paper. It includes algorithm rules of grey numbers based on the “kernel” and the degree of greyness of grey numbers, the concept of general grey numbers, the synthesis axiom of degree of greyness of grey numbers and their operations; the general form of buffer operators of grey sequence operators; the four basic models of grey model GM(1,1), such as even GM, original difference GM, even difference GM, discrete GM and the suitable sequence type of each basic model, and suitable range of most used grey forecasting models; the similarity degree of grey incidences, the closeness degree of grey incidences and the three-dimensional absolute degree of grey incidence of grey incidence analysis models; the grey cluster model based on center-point and end-point mixed triangular whitenization functions; the multi-attribute intelligent grey target decision model, the two stages decision model with grey synthetic measure of grey decision models; grey game models, grey input-output models of grey combined models; and the problems of robust stability for grey stochastic time-delay systems of neutral type, distributed-delay type and neutral distributed-delay type of grey control, etc. And the new framework of grey system theory is given as well.
Findings
The problems which remain for further studying are discussed at the end of each section. The reader could know the general picture of research and developing trend of grey system theory from this paper.
Practical implications
A lot of successful practical applications of the new models to solve various problems have been found in many different areas of natural science, social science and engineering, including spaceflight, civil aviation, information, metallurgy, machinery, petroleum, chemical industry, electrical power, electronics, light industries, energy resources, transportation, medicine, health, agriculture, forestry, geography, hydrology, seismology, meteorology, environment protection, architecture, behavioral science, management science, law, education, military science, etc. These practical applications have brought forward definite and noticeable social and economic benefits. It demonstrates a wide range of applicability of grey system theory, especially in the situation where the available information is incomplete and the collected data are inaccurate.
Originality/value
The reader is given a general picture of grey systems theory as a new model system and a new framework for studying problems where partial information is known; especially for uncertain systems with few data points and poor information. The problems remaining for further studying are identified at the end of each section.
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The purpose of this paper is to solve the DEA model with grey interval data while the inputs/outputs have large interval length.
Abstract
Purpose
The purpose of this paper is to solve the DEA model with grey interval data while the inputs/outputs have large interval length.
Design/methodology/approach
Some methods have been developed to calculate the interval efficiencies of the decision‐making unit (DMU) in DEA model with interval data, in which there are two shortcomings. One is that the evaluated DMU and referenced DMUs are not be dealt with fairly, as they are not counterparts in locations of inputs and outputs within possible ranges. Another is that efficiency intervals may be too wide to provide valuable information. This paper proposes the hypotheses of data consistency in DEA model. Under the hypotheses, linear programming (LP) models to solve the upper and lower bounds of interval efficiencies are established.
Findings
It is found that lengths of efficiency intervals under the hypotheses are shorter, which produces more reliable and informative evaluation results and DMUs are dealt with more fairly.
Practical implications
The method proposed in the paper could be used in efficiencies evaluation of enterprises, governments, etc. when the classic methods are invalid for the high uncertainty evaluation results.
Originality/value
The paper succeeds in proposing the hypotheses of data consistency and solving the DEA model with interval grey data under that.
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