A new model for selecting third-party reverse logistics providers in the presence of multiple dual-role factors

In traditional models of data envelopment analysis it is assumed that decision making units do not have dual-role factors. This paper proposes a model for selecting third-party reverse logistics (3PL) provider in the presence of multiple dual-role factors. A numerical example demonstrates the application of the proposed method.     

Input and output scaling in advanced manufacturing technology: theory and application

This paper suggests new data envelopment analysis (DEA) models for input and output scaling in advanced manufacturing technology (AMT). For a given group of AMT observations using the traditional DEA models, it is not possible to evaluate the units when a specified input (or specified output) is required to be scaled for all units. The paper provides theoretical results for obtaining the relationship between the original AMT observations and the corresponding scaled data. Also, the paper uses numerical illustrations to show the usefulness of the suggested contribution.     

A note on data envelopment analysis with missing values: an interval DEA approach

Smirlis et al. (Appl Math Comput 177(1):1–10, 2006) have proposed a pair of interval data envelopment analysis (DEA) models for computation of the efficiency of decision-making units (DMUs) in the presence of missing data. In this paper, we show that the interval DEA models presented by Smirlis et al. have some drawbacks due to the use of variable production frontier for computation of the efficiency intervals of DMUs. To overcome these drawbacks, this paper presents new interval DEA models based on interval arithmetic. It is shown that the proposed interval DEA models do not need extra variable changes and use a fixed, unified production frontier for computation of the efficiency intervals of the DMUs with interval input and output data. A numerical example is presented to illustrate the potential applications of the new interval DEA models and their effectiveness for measuring the interval efficiencies of the DMUs.     

A new DEA model for technology selection in the presence of ordinal data

This paper suggests a data envelopment analysis (DEA) model for selecting the most efficient alternative in advanced manufacturing technology in the presence of both cardinal and ordinal data. The paper explains the problem of using an iterative method for finding the most efficient alternative and proposes a new DEA model without the need of solving a series of LPs. A numerical example illustrates the model, and an application in technology selection with multi-inputs/multi-outputs shows the usefulness of the proposed approach.     

Finding the best asset financing alternative: A DEA–WEO approach

Measurement of performance is an important activity in identifying weaknesses in managerial efficiency and devising goals for improvement. Data envelopment analysis (DEA) is a mathematical quantitative approach for measuring the performance of a set of similar units. Toloo (2013) extended a DEA approach for finding the most efficient unit considering a data set without explicit inputs. The aim of this paper is to develop DEA models without explicit outputs, henceforth called DEA–WEO, to find the most efficient unit when outputs are not directly considered. The suggested models directly utilize the data without the need of adding a virtual output, whose value is equal to for all units. A real data set involving 139 different alternatives for long-term asset financing provided by Czech banks and leasing companies is taken to illustrate the potential application of the proposed approach.     

An efficient DEA method for ranking woven fabric defects in textile manufacturing

This paper deals with the problem of ranking woven fabric defects (WFDs) observed in textile manufacturing using a data envelopment analysis (DEA) method. The paper shows that the optimal solutions of DEA models for decision-making units (DMUs) with multiple inputs can be found without the need of solving the corresponding models. The paper performs a mean–variance analysis for determining the most important statistical factors of WFDs in terms of multiple inputs. The paper also ranks the observed WFDs from the worst preferred using the suggested DEA formulation. The contribution of this study can be explained as follows. It introduces a new application for DEA method in textile manufacturing for ranking fabric defects. This is significant in defining rich project in reducing defects through prioritizing of quality specification of fabric defects by Six Sigma experts. Also, the result of this paper can be obtained using an efficient DEA method without the need of solving the corresponding DEA models for any sample size of fabric defects.     

An integrated design methodology based on the use of group AHP-DEA approach for measuring lean tools efficiency with undesirable output

The selection of lean tools is one of the crucial factors for decision makers and practitioners in a competitive environment. A few efforts have been made based on problem selection. Conversely, numerical studies have been done on analytical hierarchy process (AHP)–data envelopment analysis (DEA) as well as DEA-undesirable variables separately. Thus, there is a shortage of lean practitioners as well as the methods involved. The present research aims at integrating AHP and DEA with desirable and undesirable factors to evaluate the lean tools and techniques and to rank the aspect of efficacy. We suggest a logical procedure to measure the efficacy of lean tools on leanness and to prioritize them as decision makers. In this extensive research, we apply the integrated multicriteria decision-making approach, including the hybrid groups AHP and DEA models with desirable and undesirable variables, to assess the relative efficiency of lean manufacturing tools and techniques. Case studies are used to demonstrate the lean implementation in companies while being validated by a panel of experts. The integration of these approaches has created synergy and shown to be even more powerful. Thus, the proposed integrated AHP-DEA model can evaluate and rank different alternatives while considering desirable and undesirable variables in the production processes.     

Multi-objective data envelopment analysis model in fuzzy dynamic environment with missing values

Data envelopment analysis (DEA) is an important managerial tool for evaluating and improving the performance of decision making units. The existing DEA models are mostly limited to static environment using crisp data and are time-consuming and also have weak discriminating power. The aim of this work is to introduce a new fuzzy dynamic DEA model with missing values, which benefits from strengths of multi-objective modeling to overcome weakness and drawbacks of the classic DEA models. To check for quality and accuracy of the proposed model, this paper offers a comparative study to compare the discriminating power and computational efforts of the model with two problems in the literature taken as benchmarks. Also, this paper presents a real application of the fuzzy dynamic DEA model for assessing and ranking the level of performance for 56 railways around the globe using real data gathered from credible sources. The numerical case illustrates the model and the result may be used by railways to improve their performance efficiency compared to the best in the sample. Results for the comparative study and the real case reveal significant improvement in computational time and discriminating power.     

Evaluating and ranking sustainable suppliers by robust dynamic data envelopment analysis

One of the advantages of data envelopment analysis (DEA) is to determine benchmarks for inefficient decision making units (DMUs). However, determination of the benchmarks is the result of past performance of DMUs. In other words, the benchmarks do not provide any recommendation for improvement of future efficiency of DMUs. On the other hand, in dynamic DEA models often no DMU gets the efficiency score of unity. In this case, although we can rank the DMUs, we cannot introduce an efficient DMU and benchmarks. To overcome these shortcomings we propose a dynamic ideal DMU using dynamic DEA and scenario-based model of robust. A case study showing the model in use provided.     

Improved DEA models for measuring interval efficiencies of decision-making units

In a recent paper by Wang and Yang (2007) [6], a pair of bounded data envelopment analysis (DEA) models were proposed to measure the overall performances of a group of decision-making units (DMUs), which were characterized by interval efficiencies. In this paper, we show by a numerical example that the bounded DEA models are incapable of determining an efficiency interval for any DMU when there is a zero value for each output. A pair of improved bounded DEA models is thus proposed to overcome the drawback. Another example involving performance measurement of countries participating in the Athens 2004 Summer Olympic Games is presented to show that the proposed approach is an effective and practical method for performance analysis in real world situations.     

Using data envelopment analysis for evaluating flexible manufacturing systems in the presence of imprecise data

The aim of this paper is to present a comprehensive methodology for evaluation and selection of advanced manufacturing technologies, incorporating both the economic and strategic aspects and the related imprecise as well as exact data into the decision making process. Initially, a data envelopment analysis (DEA) model that can take into account crisp, ordinal, and fuzzy data is introduced. Then, the developed framework is used for flexible manufacturing system (FMS) selection. The DEA approach is performed by employing capital and operating cost, required floor space and work-in-process (WIP) as the input variables, and using product flexibility, quality improvement and lead time reduction as the output variables. The assessment of FMS alternatives versus product flexibility and quality improvement are represented via ordinal data, while WIP and lead time reduction are stated using triangular fuzzy numbers. The proposed framework is illustrated through an application and comparative results are presented.     

Developing distinctive two-stage data envelopment analysis models: An application in evaluating the sustainability of supply chain management

Sustainable supply chain management (SSCM) has received much attention from scholars and practitioners in the past years. It has become a method for simultaneous improvement of economic, social, and environmental performance. SSCM evaluation, therefore, is a significant duty for any types of organizations. Among evaluation methods, data envelopment analysis (DEA) seems to be an appropriate technique for assessment of the SSCM. One of the uses of DEA is to evaluate the efficiency of two-stage processes, where all the outputs from the first stage are intermediate measures that are considered as the inputs to the second stage. The resulting two-stage DEA models assess both the overall efficiency score of the whole process and each of the individual stages. Notwithstanding, there are major weaknesses in the previous extensions of two-stage DEA models. Firstly, a challenging issue is that suggestions for improvements are offered only for input and output measures, and intermediate measures are neglected. Although, some extensions for network structures take into account intermediate measures, they arbitrarily assign an input or output role for the measures, thus in optimal solution for inefficient DMUs, this measures are forced to respectively take a lower or upper amount. Secondly, the efficiency scores are calculated based on inputs and outputs. That is, while the models consider these measures by corresponding constraints, the intermediate measures are not included in the objective function, or incorrectly assign an input or output role. Thirdly, in some cases, the former developments specify points on the efficient frontier only for inefficient stages, while for a network which is entirely inefficient such points are also required. Moreover, the organization (which in DEA terminology is named decision making unit) is supposed to be divided into two autonomous departments. It means that the performance of one department is quite unrelated to another department, while from the organizational perspective this is called into the question. To overcome these shortcomings, in this paper, innovative models are proposed. The proposed ideas are used for evaluating the sustainability of supply chains in resin producing companies.     

An interactive MOLP method for identifying target units in output-oriented DEA models: The NATO enlargement problem

Data Envelopment Analysis (DEA) is a mathematical programming technique for identifying efficient Decision Making Units (DMUs) with multiple inputs and multiple outputs. DEA provides a technical efficiency score for each DMU, a technical efficiency reference set with peer DMUs, and a target for the inefficient DMU. The target unit informs the Decision Maker (DM) of the amount (%) by which an inefficient DMU should decrease its inputs and/or increase its outputs to become efficient. However, the conventional DEA models generally do not consider the DM’s preference structure in identifying the target units. Several equivalence models between the output-oriented DEA and Multiple Objective Linear Programming (MOLP) models have been proposed in the literature to take the DMs’ preferences into consideration. However, these models are not able to identify target units when undesirable outputs are produced with desirable outputs in the production process. In this study we obtain a new link between a BCC model and the weighted minimax reference point of the MOLP formulation that simultaneously and interactively considers the increase in the total desirable outputs and the decrease in the total undesirable outputs. We present a pilot study for the North Atlantic Treaty Organization (NATO) enlargement problem to demonstrate the applicability of the proposed method and exhibit the efficacy of the procedures and algorithms.     

Equivalent multiplier and envelopment DEA models for measuring efficiency under general network structures

Data envelopment analysis (DEA) has been shown to be a very useful mathematical programming tool to measure the relative efficiency of decision making units (DMUs), especially when the so-called internal network structure of the production process is taken into account. Under a network structure, however, two standard directions of modeling the production process may generally lead to a pair of multiplier and envelopment DEA models so that the outcomes are not necessarily equivalent, i.e. a network duality problem occurs. Although, the duality problem has recently been addressed for specific cases of network structures, for more complex structures, DEA models have only been able to be developed by following either the envelopment form or multiplier form. Investigating this duality problem, this paper also proposes DEA models for general network structures with two additional properties. Due to the first property, all factors in a general network structure including main inputs/outputs and/or intermediate inputs/outputs can be shared among the divisions while the second property assumes that a factor in a structure may be considered as both intermediate input/output and main input/output simultaneously. We will show that the proposed network DEA models cannot only deal with the already existing general network structures in the literature, but are also represented by dual multiplier and envelopment linear programming-based problems by which consistent outcomes can be obtained. A comprehensive numerical example will be presented to explain the properties and features of the suggested models.     

Supplier selection by the new AR-IDEA model

Traditionally, supplier-selection models have been based on cardinal data with less emphasis on ordinal data. However, with the widespread use of manufacturing philosophies such as just-in-time (JIT), emphasis has shifted to the simultaneous consideration of cardinal and ordinal data in the supplier-selection process. The application of data envelopment analysis (DEA) for supplier-selection problems is based on total flexibility of the weights. However, the problem of allowing total flexibility of the weights is that the values of the weights obtained by solving the unrestricted DEA program are often in contradiction to prior views or additional available information. The objective of this paper is to propose a new pair of assurance region-imprecise data envelopment analysis (AR-IDEA) model for selecting the best suppliers in the presence of both weight restrictions and imprecise data. A numerical example demonstrates the application of the proposed method.     

含AHP约束锥DEA模型在虚拟企业合作伙伴选择中的应用

以虚拟企业下合作伙伴的选择为背景,讨论现有的选择方法以及评价准则的特点。并提出了用含AHP约束锥的DEA 模型来解决虚拟企业下合作伙伴评价问题,这个模型不仅有一般DEA模型的特点,而且能够反映决策者的主观偏好。最后 通过分析算例的结果表明,此模型可为合作伙伴选择问题提供有效的分析方法。     

A ranking method based on DEA and PROMETHEE II (a rank based on DEA & PR.II)

In this paper we present an integrated Data Envelopment Analysis-Multiple Criteria Decision Aid (DEA-MCDA) model which can be applied to increase the discrimination power of DEA. The aim is to restrict weight values of a DEA model by using tools from MCDA. This model leads to more reasonable inputs/outputs weights while in classic DEA models, some inputs/outputs may be characterized by very low or high weight values. To achieve this goal we use the stability intervals based on PROMETHEE II (Preference Ranking Organization METHod for Enrichment of Evaluations) as weight constraints in DEA. Furthermore, the unicriterion net flow scores matrix is used instead of the initial evaluation matrix. By doing so, we already integrate preferential information in the DEA process. By construction, the best results are compatible with the PROMETHEE II ranking. Additional comparisons with the outputs of other decision making techniques are provided based on two examples.     

Performance measurement of decision-making units under uncertainty conditions: An approach based on double frontier analysis

Data envelopment analysis (DEA) is an approach to measure the relative efficiency of a set of decision-making units (DMUs) which uses multiple inputs to produce multiple outputs. In real world situations, due to uncertainty, DEA is sometimes faced with imprecise inputs and/or outputs. Therefore, performance measurement must often be performed under uncertainty conditions. Generally, the performance of DMUs can be evaluated from two perspectives—optimistic and pessimistic. As a result, two different evaluations are obtained for each DMU. In this article, we first obtain the efficiencies of the DMUs under evaluation from both optimistic and pessimistic views. The optimistic view evaluates each DMU with a set of the most desirable weights; the efficiencies measured by the optimistic approach are called optimistic efficiencies. The pessimistic view evaluates each DMU with a set of the most undesirable weights; the efficiencies measured by the pessimistic approach are called pessimistic efficiencies. Then it is shown that the outcomes of these two evaluations are conflicting with each other, being undoubtedly biased, unrealistic, and unconvincing. To overcome this problem, we propose a new measure of overall performance which is used for integrating the measures obtained from optimistic and pessimistic views and we will use it to identify the DMU with the best performance under uncertainty conditions. Also, we propose new fuzzy DEA models that evaluate a DMU from the pessimistic perspective in a fuzzy context. The proposed measure will be shown with two numerical examples, including the selection of a flexible manufacturing system.     

熵在供应链复杂性研究中的应用

供应链的网状结构决定了其在复杂多变的市场中将不可避免要面对诸多不确定因素。本文把熵论应用到供应链复杂性的研究中 ,提出了量化分析模型与方法。通过量化分析 ,可以清楚地看到供应链各节点之间复杂性产生的原因以及复杂性沿供应链在上下游合作伙伴之间的传递过程 ,并且可以将复杂性比较准确地加以量化 ,为进一步有效地控制降低整个供应链的复杂性奠定基础     

A DEA approach for comparing solution efficiency in U-line balancing problem using goal programming

Line balancing problem plays an important role in the decision making process to increase efficiency and productivity. Recently, U-shaped layout in many production lines has replaced the traditional straight line layout using just-in-time concept. Here, we propose a model, using multi-objective decision making approach to the U-shaped line balancing problem, to offer enhanced decision maker flexibility, by allowing for conflicting goals. The assembly line operation efficiency is the most significant aim in our study, and this efficiency relates to management of resources and the solution of line balancing problem. First, the U-shaped line balancing problem is solved considering the model's goals. Then, the index function of assembly line balancing is determined and the efficiencies of the optimal solution outputs are evaluated using data envelopment analysis (DEA). Finally, the discrimination weakness and distribution of illogical weight in simple DEA models are resolved using a mixed method.