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• DEA provides solutions for an inefficient bus line to make it efficient. • DEA solutions may negatively affect exogenous social and environmental variables. • The magnitude of these negative effects can be estimated in advance using AIMSUN. • The magnitude of these negative effects may cause DEA solutions to be ignored. • AIMSUN pre-evaluates the side effects of DEA solution on exogenous variables. The bus network, including several bus lines (BLs), is the most important public transportation system in many cities. A bus line (BL) may be inefficient compared to the other BLs. Therefore, solutions are needed to make it efficient. Data envelopment analysis (DEA) is a mathematical programming model that can measure the performance of BLs and provide solutions to improve the performance of inefficient BLs. However, DEA can consider only a few attributes when evaluating BLs, while this evaluation requires big data to be considered. Moreover, DEA solutions may negatively affect exogenous variables, in particular social and environmental variables. Although these variables are not directly related to measuring BLs' efficiency, they may affect the implementation of DEA solutions. For example, reducing the number of buses on an inefficient line can increase its efficiency. However, it may indirectly increase the use of personal vehicles, resulting in more pollution and traffic. If the magnitude of this pollution or traffic is too high, decision-makers (DMs) may ignore improving the efficiency of that BL. Thus, the magnitude of possible negative consequences of DEA solutions, especially on critical social and environmental variables, should be estimated before their implementation. This study uses AIMSUN simulation software to pre-evaluate the negative effects of DEA solutions on critical exogenous social and environmental variables. We use a real example to illustrate that DEA solutions sometimes may not be implemented due to their harmful negative consequences. [ABSTRACT FROM AUTHOR]

In this paper, we introduce data envelopment analysis (DEA) model into rough sets, and propose a novel three-way decision model to deal with the decision problems with multiple decision attributes. On the integration of the "trisecting-acting-outcome (TAO)" framework of three-way decision (3WD) and DEA model, three ordered regions (i.e. efficient region, weakly efficient region, and inefficient region) are generated in the trisecting part. The corresponding strategies are subsequently adopted to process these three regions in the acting part. Specifically, the decision making units (DMUs) in the efficient region are considered as benchmarks for the other two regions; three projection strategies are designed to improve the DMUs in the weakly efficient and inefficient regions. Besides, the effectiveness of the results of trisecting and acting is measured in the outcome evaluation part. Finally, two numerical examples are provided to validate the proposed model. [ABSTRACT FROM AUTHOR]

Balancing the accuracy rates of the majority and minority classes is challenging in imbalanced classification. Furthermore, data characteristics have a significant impact on the performance of imbalanced classifiers, which are generally neglected by existing evaluation methods. The objective of this study is to introduce a new criterion to comprehensively evaluate imbalanced classifiers. Specifically, we introduce an efficiency curve that is established using data envelopment analysis without explicit inputs (DEA-WEI), to determine the trade-off between the benefits of improved minority class accuracy and the cost of reduced majority class accuracy. In sequence, we analyze the impact of the imbalanced ratio and typical imbalanced data characteristics on the efficiency of the classifiers. Empirical analyses using 68 imbalanced data reveal that traditional classifiers such as C4.5 and the k-nearest neighbor are more effective on disjunct data, whereas ensemble and undersampling techniques are more effective for overlapping and noisy data. The efficiency of cost-sensitive classifiers decreases dramatically when the imbalanced ratio increases. Finally, we investigate the reasons for the different efficiencies of classifiers on imbalanced data and recommend steps to select appropriate classifiers for imbalanced data based on data characteristics. [ABSTRACT FROM AUTHOR]

The ultimate goal of the optimal alternative selection decision-making is to achieve higher returns with as few risks as possible. However, the existing multi-attribute decision-making (MADM) methods rarely measure their returns and risks simultaneously. In addition, these methods have no clear explanation for how to improve the non-optimal alternatives. Considering this, this paper constructs the returns and risks of alternatives in an identical measurement system by extending DEA model, thereby selecting the optimal alternative more comprehensively and improving the non-optimal alternatives. First, considering the bounded rationality of experts, the prospect theory is introduced into the information evaluation using hesitant fuzzy linguistic term sets to fully characterize the experts' psychological behavior under uncertainty. Then, by constructing the return-risk ratio as the efficiency evaluation index, two prospect theory-based hesitant fuzzy linguistic superefficiency models, namely the risk-oriented (RIHFLS) and return-oriented (REHFLS) models, are proposed to fully rank the alternatives. Furthermore, these two models are extended to RIHFLPS and REHFLPS models considering different importance of attributes. By adjusting the returns and risks to the target values, the non-optimal alternatives can be enhanced to efficient state. Finally, the feasibility and superiority of the proposed methods are verified by an application example. [ABSTRACT FROM AUTHOR]

Unlike most existing clustering methods, data envelopment analysis (DEA) clusters decision-making units (DMUs) based on production characteristics rather than distance. The clustering results obtained using the DEA clustering approach reflect the production relationship between the inputs and outputs of the DMUs to better identify the inherent production correlation between them. However, existing DEA-based clustering approaches struggle to rationally assign unique clusters to DMUs that exhibit multiple production characteristics and lack the further processing of clustering results. Therefore, this study proposes a new DEA clustering approach based on the individual perspective of DMUs that incorporates prospect theory to reflect the individual preferences of DMUs to assign each DMU to a relatively unique cluster. Furthermore, a clustering adjustment method and a clustering reduction method are proposed to further improve the clustering quality. The former can handle some special clusters according to the decision-maker's preference, and the latter permits the realization of an arbitrary number of clusters. The new DEA clustering approach is more reliable and flexible, and more valuable information can be provided for decision-makers. Finally, the validity of the new approach is verified through a comparison with existing approaches in two numerical cases, and an empirical example is used to illustrate its practicability. [ABSTRACT FROM AUTHOR]

• Context-Dependent DEASort is proposed based on BWM for multi-criteria sorting problem. • Categories can be simply obtained without predefined classification categorical rules. • A common set of weight model is introduced for evaluating attractiveness of each DMU. • Case study concerning risk assessment of Yangtze River Economic Zone is provided. • Sensitivity analysis is conducted to illustrate the stability of the proposed method. With the increasing urban economic development and awareness of environment protection, ecological risk assessment (ERA), as a management mode combined with ecological research and risk assessment, has become a highly relevant topic in environment sustainable development. In this paper, we propose a novel sorting model based on Data Envelopment Analysis (DEA) and Best Worst method (BWM) to solve multi-criteria sorting problems and apply it to ERA. The proposed Context-Dependent DEASort method based on the idea of Context-Dependent DEA to position decision-making units (DMUs) into diverse categories in uncertain circumstance. It also takes experts' preference into full account as well, which makes the sorting solution more flexible and reasonable. Besides, a common set of weight-based model is introduced to deal with the situation when evaluating attractiveness and progress of each DMU. The common weight set model can ensure to provide an overall evaluation context compared to the original Context-Dependent DEA model, which only distinct the DMU from a single virtual DMU. Furthermore, a case study concerning ecological risk assessment of Yangtze River Economic Zone in China is provided to illustrate the applicability of the developed environment. Finally, comparative analysis and sensitivity analysis are carried out to demonstrate the practicality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In the crowdsourcing task allocation scheme, there is an emerging and realistic co-opetition phenomenon. To availably solve the crowdsourcing task allocation problem with co-opetition, this paper designs a two-stage co-opetition model by constructing novel three-way decision (TWD), including a competition-optimization model and a negotiation-cooperation model. Unlike the other studies, the two-stage co-opetition model with TWD can not only protect the profits of the task candidates, but also optimize the overall benefits. Specifically speaking, in the competition-optimization model, we construct an optimization model based on the data envelopment analysis (DEA) method in advance, which maximizes the personal benefit. By integrating information system and the loss function matrix, we consider the linkage of evaluation information and risk information and then improve the original TWD to make an initial allocation. In the negotiation-cooperation model, considering that the relationship among the candidates may influence the task performance, the fuzzy measure is introduced to describe a broader partnership. Meanwhile, we also design two different schemes to coordinate and optimize the best task allocations based on the initial allocation. In order to choose the best scheme, the selection strategy between schemes is further investigated under the guidance of the utility and the loss. Finally, we give an example of a medical supply chain crowdsourcing problem to illustrate and verify our proposed approach. [ABSTRACT FROM AUTHOR]

• An uncertain dynamic goal setting process is introduced to provide implementable goal sequences. • Variables of economic-social-environmental sustainability are considered in goal setting. • Detailed information classification is conducted to make reasonable analysis. • More sensible goal sequence can be given by layering process and the measure of attractiveness and progress. • Numerical example is conducted to show the feasibility and efficacy of the proposed model. Because of the growing awareness of balancing the economy, the society and the environment, there has been an increased focus on goal setting under sustainable context. However, underperforming suppliers may set impractical goals that are difficult to achieve in the short term due to the poor information analysis, which could cause benchmarking difficulties. Therefore, rather than focusing on a single ultimate goal, this paper developed an implementable goal sequence for which a dynamic Data Envelopment Analysis based benchmarking model was developed as a multi-period process. The indicators that could not be directly measured using numerical values were incorporated into the model as fuzzy numbers, and based on efficiencies, the suppliers were divided into several layers. A method that identified supplier attractiveness and progress was then introduced to select the most appropriate goal in each layer for the goal sequence. A numerical example is provided that illustrated the feasibility and efficacy of the proposed method to successfully identify the inefficient suppliers and determine a goal sequence that had three attainable goals. The sensitivity analysis and comparisons revealed that the proposed model avoided selecting period-inefficient suppliers and provided a better benchmarking process. [ABSTRACT FROM AUTHOR]

• The novel transformation and comparison methods for MGPLZNs are developed. • DEA is employed to solve the normalized priority weight vectors for MGPLZNPRs. • Expert credibility that reflects the certainty, ambivalence, and hesitancy of experts is defined. • The subjectively determined expert weights are adjusted according to the expert credibility. • Two case examples are used to illustrate the validity of the proposed method. Multi-granularity probabilistic linguistic Z-number (PLZN) is an effective and flexible tool for expressing expert opinions. For group decision-making (GDM) problems with multi-granularity PLZN preference relations (PLZNPRs), this paper proposes a GDM method based on data envelopment analysis cross-evaluation (DEA-CE) model and expert credibility. First, as the foundation for solving GDM problems with multi-granularity PLZNPRs, this paper reveals the inadequacies of existing methods on transformation and comparison between multi-granularity PLZNs through arithmetic examples. After analyzing these inadequacies, this paper proposes the novel transformation and comparison methods with better performances. Second, to obtain the priorities of multi-granularity PLZNPRs more efficiently, DEA-CE model is introduced and the effectiveness of introducing DEA-CE model is demonstrated. The superiority of introducing DEA-CE model is that it requires no further adjustment of preference relations at any consistency level, which helps to maximize the retention of original information. Third, the subjective expert weights determined based on realistic situations are objectively adjusted using the expert credibility calculated according to the information quality, making the results more reasonable. Finally, this paper illustrates by two case examples that the proposed model can be well adapted to GDM problems with all sizes, regardless of the numbers of decision variables and experts. [ABSTRACT FROM AUTHOR]

To deal with group decision making (GDM) with interval multiplicative preference relations (IMPRs), this paper proposes a novel method based on multiplicative data envelopment analysis (DEA) cross-efficiency and stochastic weight space acceptability analysis. We first develop a multiplicative DEA model to evaluate the relative efficiency of all alternatives of a given multiplicative preference relation (MPR). Then, we present a method, free from consistency adjustment, to derive a priority vector using the multiplicative DEA cross-efficiency with respect to the given MPR. For GDM with IMPRs, we consider the decision makers' weights as a uniform distribution for acceptability analysis. A modified unacceptability index is further defined to measure the unlikeliness for a particular alternative in a particular rank. Finally, we develop an assignment problem model to achieve an optimal ranking by minimizing the total rank unacceptability, and to compute the expected priority vector of all alternatives. Numerical examples are provided to show the applicability and justifications of the proposed GDM method. [ABSTRACT FROM AUTHOR]

Accompanying with strengthening consciousness of environmental protection and social security, developing sustainable supply chain (SSC) has been becoming more and more important. Under this background, it is essential to deeply understand and evaluate SSCs from comprehensive economy-environment-society aspects. In order to do this, this paper considers the SSC as a dynamic three-stage network system consisting of supplier, manufacturer and distributor across multiple periods, and establishes a novel dynamic network DEA model with desirable and undesirable indicators to compute the detailed efficiencies based on both effective and invalid production frontiers. In the model, the degrees of environmental pollution and customer satisfaction are described as interval type-2 fuzzy sets. Then a method considering the optimistic-pessimistic attitude of decision makers is introduced to get solutions. For case study, the proposed DEA model is used to evaluate 20 SSCs, and analysis from time and stage perspectives are conducted to show the detailed performance. According to the results, the evaluated SSCs can be divided into four groups with different patterns, the weakness stage and period for each SSC can also be identified. The comparison and sensitivity analysis demonstrates our proposed model has more discriminating power and the ability of providing more managerial insights. Not only the sensitivity degree of a SSC to the attitude change of decision makers can be determined, but also the important indicator in different aspects for each SSC can be pointed out. [ABSTRACT FROM AUTHOR]

Abstract This paper deals with nonlinear observer design for a class of nonlinear systems with nonlinear output measurements. The proposed methodology is based on the use of Linear Matrix Inequalities (LMIs) to handle a problem of W 1 , 2 -convergence criterion. Some new assumptions and convenient Young's formulation are used to get less conservative LMI conditions compared to the literature. Indeed, the obtained LMIs contain additional decision variables, which render the conditions more general. Furthermore, due to the presence of nonlinearities in both the process dynamics and the output measurements, the class of systems studied in this paper is more general than those available in the literature in the same LMI context. Two numerical examples are presented to show the effectiveness and superiority of the new design procedure compared to the H ∞ method. [ABSTRACT FROM AUTHOR]

We study the portfolio selection problem from the perspective of incorporating more information about the non-normality of asset returns by considering the mean-variance-skewness-kurtosis framework. Using additional criteria (namely asset turnover, earnings per share, earnings per share growth rate, leverage ratio, price/earnings ratio, and beta ( β ) value), we employ data envelopment analysis technique to construct a benefit criterion from the efficiency scores of the assets. To incorporate the uncertainty and vagueness of real financial markets, the input data (with respect of all criteria) are assumed as ( λ, ρ ) interval-valued fuzzy numbers constructed using the historical data. Marginal impacts of the assets on higher moments of the portfolio are used to formulate a fuzzy multi-objective linear programming model. The constraints of the proposed model include the bounds on investment in individual assets, full utilization of the investment capital, and no short selling of assets. The signed distance ranking method is used to obtain a numeric optimization model, which is solved through a weighted max-min approach (in order to incorporate the investor’s preferences regarding investment criteria). A case of real financial market portfolio selection is presented to demonstrate the efficiency of both the proposed model and the solution method. [ABSTRACT FROM AUTHOR]

Problems with inconsistency and incompleteness are widely found in rule-based decision support systems. These problems often impact the accuracy and usability of rule-based decision support systems. The present work focuses on an advanced rule-based decision support system, namely the extended belief-rule-based (EBRB) system, and proposes the consistency analysis-based rule activation (CABRA) method to overcome the above two problems simultaneously. However, two challenges must be discussed and addressed for the EBRB system. First, rather than using activated weights, suitable activated rules must be redefined to better analyze the problems of inconsistency and incompleteness. Second, suitable activated rules must be selected without having to depend on subjective information. Therefore, the proposed CABRA method uses the set of consistent rules as an activation framework to define suitable activated rules before calculating their activated weights, and utilizes the CCR model as a selection model to select suitable activated rules from the set of consistent rules. As such, by embedding the CABRA method, the EBRB system can overcome the problems of inconsistency and incompleteness. Three case studies demonstrate how the use of the CABRA method improves the accuracy and rule activation rate of the EBRB system, which is further confirmed by comparisons with the results of other existing studies. In addition to the work performed in the EBRB system, the CABRA method is treated as a generic rule activation method that can be available for other rule-based decision support systems. [ABSTRACT FROM AUTHOR]

We propose a risk-based stochastic VIKOR (RB-VIKOR) model that accounts for differences in the risk attitudes of the decision makers (DMs) when ranking stochastic alternatives. Our proposed RB-VIKOR model is designed to solve multi-criteria problems characterized by stochastic data and DMs categorized by their risk averse or risk seeking behavior. These differences in risk attitudes determine the subjective beliefs of the DMs regarding the evaluation of each alternative per decision criterion and the resulting rankings. We present a case study in the banking industry to illustrate how differences in the risk attitudes of the DMs condition the rankings obtained. Moreover, we compare our results with those derived from a stochastic super-efficiency data envelopment analysis (DEA) model to demonstrate the applicability and efficacy of RB-VIKOR. The proposed method has a considerable amount of potential applications to diverse research areas ranging from economics to knowledge based and decision support systems. [ABSTRACT FROM AUTHOR]

Green low-carbon transformation is necessary to achieve sustainable development in “the eighteenth”. Based on the literature review of regional green low-carbon transformation, regional green low-carbon transformation and evaluation framework are defined as the process of building regional green low-carbon transformation. We think that the system innovation is a complex system. By establishing collaborative model of the green transformation of the system innovation, we evaluate dynamically the regional green transformation capacity with data envelopment analysis from changing and developing of the two systems. Which react the efficiency of resource allocation of two systems in-depth analysis of the interaction between the two development of the internal mechanism, and clearing the mechanism between system innovation and transformation capacity. Be corroborated in the case of the practice green transformation of the Jiaodong Peninsula (Qingdao, Yantai and Weihai), strengthening the quantitative study of the depth of the theory and practice in transition. We concluded that: the overall coordination between system innovation and green transformation in Jiaodong Peninsula is good during 2010–2013. System innovation capacity cannot be fully translated into green transformation capacity, and lag weakens the supporting role of other developments in the field innovation for the transition. The situation has become better since 2010. Finally, it focus that the value of research applications and future research direction. [ABSTRACT FROM AUTHOR]

Group consensus ranking is an important topic in performance evaluation and selection research. Data envelopment analysis (DEA) has been used for obtaining an efficiency score (preference score) for each candidate. We propose an integrated DEA and simulation method for group consensus ranking. The ranking method proposed in this study has several unique features. In contrast to most voting methods that assume equal voting power to voters, the proposed method classifies voters into different groups and allows for assigning a different voting power to each group. In spite of its effectiveness, though similarly to the competing methods in the literature, the proposed method may lead to more than one efficient candidate. Several ranking models are extended and used to discriminate among the efficient candidates. Despite the wealth of information provided to the decision maker(s), different extended ranking models may produce different rankings. Simulation is used to analyze these rankings and synthesize them into one overall group ranking. A case study is used to demonstrate the applicability and exhibit the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

In the condition based maintenance, quantitative evaluation of the system in gradual failure is important foundation for timely and reasonable decision making. However, there are several types of components in fluid power system, and they have complex mutual constraints between different component’s performance parameters, this make it difficult to evaluate the condition of fluid power system in gradual failure. To solve this problem, the paper proposes a model based on data envelopment analysis (DEA) to analyze the efficiency change degree of fluid power system. Leakage of fluid power system is selected as an application example and the sample data are collected through a leakage experiment system, and the experiments were carried out in single component failure mode and multi-components failure mode respectively. The main contents of this research include determining the input and output index of fluid power system for condition evaluation, modeling based on DEA and calculating the relative efficiency of fluid power system in gradual failure. Experimental results show that condition evaluation model based on DEA can reveal the leakage degree in fluid power system, and provide a feasible solution for accurate assessment of fluid power system in gradual failure. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the upper and lower bounds of a class of uncertainty measures endowed with particular characteristics (decomposability, monotonicity, partial additivity and so on). We consider an initial partial assessment consistent with either probability or possibility or necessity, then we study the upper and lower envelopes of all possible extensions. By resorting to a notion of weak logical independence we get as lower or upper envelope a possibility or a necessity, respectively, starting either from a probability or from a possibility or from a necessity. [Copyright &y& Elsevier]

Motivated by the COVID-19 pandemic and ensuing challenges to human and economic welfare, this research seeks to evaluate innovation efficiency, its micro-level drivers, and its impact on stock returns. This study considers innovation-related activities during two growth phases experienced by 138 listed pharmaceutical manufacturing companies in China: research and development (R&D) and marketing. A dynamic two-phase network data envelopment analysis (DEA) model measured R&D efficiency, marketing efficiency, and dynamic integrated innovation efficiency. A projection difference analysis was presented to offer a feasible solution to address inefficient companies. Then, panel regression methods were adopted to examine micro-level drivers of innovation efficiency. Additionally, a portfolio formation test was used to investigate innovation efficiency as a characteristic impacting stock returns. The results indicate that the listed companies are generally innovation inefficient. Only two are DEA innovation efficient. Low marketing and R&D efficiencies are impeding innovation efficiency amelioration, with the most losses attributed to the marketing phase. Most companies lack good conditions for R&D and the commercialization of scientific and technological breakthroughs. Institutional investors and financial analysts' coverage have a positive and significant impact on innovation efficiency. Foreign institutional ownership and stock market overvaluation impact listed companies with high innovation efficiency positively. There is a negative and significant effect of size on innovation efficiency. However, growth in pharmaceutical manufacturing company size positively influences innovation efficiency at higher levels. The portfolio formation test results reveal that investors consider companies with high innovation efficiency as high-profile targets that provide high stock returns. The findings of this study offer stakeholders avenues to shape the initiation, process, and outcome of innovation. [ABSTRACT FROM AUTHOR]

Abstract: Project selection is a complex decision making process that is influenced by multiple and often conflicting objectives. The complexity of the project selection problem is due to the high number of projects from which a subset (portfolio) has to be chosen. We present a hybrid fuzzy rule-based multi-objective framework for sustainable project portfolio selection. The multiple and conflicting objectives are considered as the input variables in a Fuzzy Rule-Based (FRB) system developed to estimate the overall fitness (suitability) of the potential project portfolios. A hybrid multi-objective framework integrates and synthesizes the results from a data mining model with the results from a Data Envelope Analysis (DEA) model and an Evolutionary Algorithm (EA) to design the structure of the proposed FRB system. The proposed framework simultaneously considers the accuracy maximization and the complexity minimization objectives. A Genetic Based Machine Learning (GBML) method is utilized to design an alternative FRB system for comparison purposes. The proposed framework and the GBML method are used to assess the alternative project portfolios in a real-world financial services institution. The statistical analysis shows the performance dominance of the proposed hybrid framework over the GBML method based on selected accuracy and interoperability measures. [Copyright &y& Elsevier]

Abstract: This paper establishes the equivalent relationship between the data classification machine and the data envelopment analysis (DEA) model, and thus build up a DEA based classification machine. A data is characterized by a set of values. Without loss of the generality, it is assumed that the data with a set of smaller values is preferred. The classification is to label if a particular data belongs to a specified group according to a set of predetermined characteristic or attribute values. We treat such a data as a decision making unit (DMU) with these given attribute values as input and an artificial output of identical value 1. Then classifying a data is equivalent to testing if the DMU is in the production possibility set, called acceptance domain, constructed by a sample training data set. The proposed DEA classification machine consists of an acceptance domain and a classification function. The acceptance domain is given by an explicit system of linear inequalities. This makes the classification process computationally convenient. We then discuss the preference cone restricted classification process. The method can be applied to classifying large amount of data. Furthermore, the research finds that DEA classification machines based on different DEA models have the same format. Input-oriented and output-oriented DEA classification machines have similar properties. The method developed has great potential in practice with its computational efficiency. [Copyright &y& Elsevier]

Abstract: In this paper, a Russell non-radial measure of eco-efficiency is proposed. Data Envelopment Analysis (DEA) is used for the computations. The inputs considered are the damage indicators provided by the Ecoindicator 99 methodology for Life Cycle Impact Assessment (LCIA). The single output is consumer price, used as a surrogate of economic value. After projecting onto the efficient frontier using input orientation and obtaining efficient target values for the damage indicators, additional DEA models are solved in order to compute corresponding scale elasticities bounds. The proposed approach is illustrated on a small sample of household electric and electronic products. The results confirm that mobile phones, desktop computers and vacuum cleaners are eco-efficient and, of these, only mobile phones are scale efficient. Bounds on the scale elasticity of all products are reported. Most products show decreasing eco-efficiency returns to scale. [Copyright &y& Elsevier]

Abstract: Supply chain management (SCM) is one of the most important competitive strategies used by modern enterprises. The main aim of supply chain management is to integrate various suppliers to satisfy market demand. Meanwhile, supplier selection and evaluation plays an important role in establishing an effective supply chain. Traditional supplier selection and evaluation methods focus on the requirements of single enterprises, and fail to consider the entire supply chain. Therefore, this study proposes a structured methodology for supplier selection and evaluation based on the supply chain integration architecture. In developing the methodology for supplier selection and evaluation in a supply chain, enterprise competitive strategy is first identified using strengths weaknesses opportunities threats (SWOT) analysis. Based on the competitive strategy, the criteria and indicators of supplier selection are chosen to establish the supplier selection framework. Subsequently, potential suppliers are screened through data envelopment analysis (DEA). Technique for order preference by similarity to ideal solution (TOPSIS), a multi-attribute decision-making (MADA) method is adapted to rank potential suppliers. Finally, the Taiwanese textile industry is used to illustrate the application and feasibility of the proposed methodology. This study facilitates the improvement of collaborator relationships and the management of potential suppliers to help increase product development capability and quality, reduce product lifecycle time and cost, and thus increase product marketability. [Copyright &y& Elsevier]

Abstract: New Zealand''s success in raising agricultural productivity has been accompanied by higher input use, leading to adverse effects on the environment. Until recently, analysis of farm performance has tended to ignore such negative externalities. The current emphasis on environmental issues has led dairy farmers to target improvements in both environmental performance and productivity. Therefore, measuring the environmental performance of farms and integrating this information into farm productivity calculations should assist in making informed policy decisions which promote sustainable development. However, this is a challenging process since conventional environmental efficiency measures are usually based on simple input and output flows but nitrogen discharge is a complex process affected by climate, pasture composition, cow physiology and geophysical variability. Furthermore, the outdoor pastoral nature of New Zealand farming means that it is difficult to control input and output flows, particularly of nitrogen. We present a novel approach to measure the environmental and economic efficiency of farms, using the Overseer nutrient budget model and spatially micro-simulated virtual population data. The empirical analysis is based on dairy farms in the Karapiro catchment, where nitrogen discharge from dairy farming is a major source of nonpoint pollution. [Copyright &y& Elsevier]

Abstract: This article first presents several formulas of chance distributions for trapezoidal fuzzy random variables and their functions, then develops a new class of chance model (C-model for short) about data envelopment analysis (DEA) in fuzzy random environments, in which the inputs and outputs are assumed to be characterized by fuzzy random variables with known possibility and probability distributions. Since the objective and constraint functions contain the chance of fuzzy random events, for general fuzzy random inputs and outputs, we suggest an approximation method to compute the chance. When the inputs and outputs are mutually independent trapezoidal fuzzy random variables, we can turn the chance constraints and the chance objective into their equivalent stochastic ones by applying the established formulas for the chance distributions. In the case when the inputs and the outputs are mutually independent trapezoidal fuzzy random vectors, the proposed C-model can be transformed to its equivalent stochastic programming one, in which the objective and the constraint functions include a number of standard normal distribution functions. To solve such an equivalent stochastic programming, we design a hybrid algorithm by integrating Monte Carlo (MC) simulation and genetic algorithm (GA), in which MC simulation is used to calculate standard normal distribution functions, and GA is used to solve the optimization problems. Finally, one numerical example is presented to demonstrate the proposed modeling idea and the efficiency in the proposed model. [Copyright &y& Elsevier]

Abstract: The cost efficiency model (CE) has been considered by researchers as a Data Envelopment Analysis (DEA) model for evaluating the efficiency of DMUs. In this model, the possibility of producing the outputs of a target DMU is evaluated by the input prices of the DMU. This provides a criterion for evaluating the CE of DMUs. The main contribution of this paper is to provide an approach for generalizing the CE of DMUs when their input prices are triangular fuzzy numbers, where preliminary concepts of fuzzy theory and CE, are directly used. [Copyright &y& Elsevier]

Abstract: In a recent paper [Soleimani-damaneh M. Fuzzy upper bounds and their applications. Chaos, Solitons & Fractals 2008;36:217–25.], I established the existence of a distance-based fuzzy upper bound for the objective function of a fuzzy DEA model, using the properties of a discussed signed distance, and provided an effective approach to solve that model. In this paper a new dual-based proof for the existence of the above-mentioned upper bound is provided which gives a useful insight into the theory of fuzzy DEA. [Copyright &y& Elsevier]

Abstract: The relative efficiency of a DMU is the result of comparing the inputs and outputs of the DMU and those of other DMUs in the PPS (production possibility set). If the inputs and outputs are fuzzy, the DMUs cannot be easily evaluated and ranked using the obtained efficiency scores. In this paper, presenting a new idea for ranking of DMUs with fuzzy data. And finally, we introduce a numerical example. [Copyright &y& Elsevier]

Abstract: In this paper, fuzzy DEA (data envelopment analysis) models are proposed for evaluating the efficiencies of objects with fuzzy input and output data. The obtained efficiencies are also fuzzy numbers that reflect the inherent ambiguity in evaluation problems under uncertainty. An aggregation model for integrating fuzzy attribute values is provided in order to rank objects objectively. Using the proposed method, a case study involving a restaurant location problem is analyzed in detail. Rent of establishment, traffic amount, level of security, consumer consumption level and competition level are identified as the primary factors in determining an ideal location for a Japanese-style rotisserie restaurant. Based on field investigation, the uncertain information on primary factors is represented by fuzzy numbers. Using the fuzzy aggregation model, the best location of restaurant is determined. The case study shows that fuzzy DEA models can be quite useful for solving business problems under uncertainty. [Copyright &y& Elsevier]

Abstract: Facility layout design (FLD) has a very important effect on the performance of a manufacturing system. The concept of FLD is usually considered as a multiobjective problem. For this reason, a layout generation and its evaluation are often challenging and time consuming due to their inherent multiple objectives in nature and their data collection process. In addition, an effective facility layout evaluation procedure necessitates the consideration of qualitative criteria, e.g., flexibility in volume and variety and quality related to the product and production, as well as quantitative criteria such as material handling cost, adjacency score, shape ratio, and material handling vehicle utilization in the decision process. This paper presents a decision-making methodology based on data envelopment analysis (DEA), which uses both quantitative and qualitative criteria, for evaluating FLD. The criteria that are to be minimized are viewed as inputs whereas the criteria to be maximized are considered as outputs. A computer-aided layout-planning tool, VisFactory, is adopted to facilitate the layout alternative design process as well as to collect quantitative data by using exact and vague data by means of fuzzy set theory. Analytic hierarchy process (AHP) is then applied to collect qualitative data related to quality and flexibility. The DEA methodology is used to solve the layout design problem by simultaneously considering both the quantitative and qualitative data. The purposed integrated procedure is applied to a real data set of a case study, which consists of 19 FLDs provided of the plastic profile production system. [Copyright &y& Elsevier]

Park et al. (Econometric Theory 16 (2000) 855) and Gijbels et al. (J. Amer. Statist. Assoc. 94 (1999) 220) derived the limit distributions of the two most popular nonparametric estimators, FDH and DEA, of a boundary in a setting where the density or intensity of the data has a sharp or fault-type boundary. In this paper, we extend their results to the general case where the density or intensity may decrease to zero or infinity at a speed of power α−1 (α>0) of the distance from the boundary. [Copyright &y& Elsevier]