An AHP/DEA methodology for ranking decision making units

This paper presents a two-stage model for fully ranking organizational units where each unit has multiple inputs and outputs. In the first stage, the Data Envelopment Analysis (DEA) is run for each pair of units separately. In the second stage, the pairwise evaluation matrix generated in the first stage is utilized to rank scale the units via the Analytical Hierarchical Process (AHP). The consistency of this AHP/DEA evaluation can be tested statistically. Its goodness of fit with the DEA classification (to efficient/inefficient) can also be tested using non-parametric tests. Both DEA and AHP are commonly used in practice. Both have limitations. The hybrid model AHP/DEA takes the best of both models, by avoiding the pitfalls of each. The nonaxiomatic utility theory limitations of AHP are irrelevant here: since we are working with given inputs and outputs of units, no subjective assessment of a decision maker evaluation is involved. AHP/DEA ranking does not replace the DEA classification model, rather it furthers the analysis by providing full ranking in the DEA context for all units, efficient and inefficient.     

Selecting the best statistical distribution—a comment and a suggestion on multi-criterion evaluation

An interesting new approach for selecting a distribution to describe the observed data has been presented in a paper recently published in Computers and Industrial Engineering. While appreciating the new contribution of the paper, we highlight an important difficulty with their method and suggest that data envelopment analysis (DEA) could be advantageously used to improve the multi-criterion evaluation.     

Risk evaluation for industrial smart product-service systems: An integrated method considering failure mode correlations

Industrial smart product-service systems (ISPS²) is a complex and dynamic ICT-based ecosystem of value co-creation among stakeholders, making the ISPS² implementation a demanding and risky process. Previous studies often use Failure Modes and Effects Analysis (FMEA) approach to identify potential risks of failure modes to ensure successful implementation of ISPS². However, previous FMEA methods assumed independence between potential failure modes (FMs). Moreover, the previous FMEA methods omit the propagation of the impact of FMs with different initial importance in an ISPS², which will influence the result accuracy of risk evaluation. To solve these problems, a novel FMEA based on strength-influence analysis of FMs is provided. The proposed method not only considers the attenuation effect of failure mode impact but also the correlation between FMs. To verify the effectiveness of the proposed method, this research applies it to the case of the smart 3D printing service system.     

An integrated multi-objective Markowitz–DEA cross-efficiency model with fuzzy returns for portfolio selection problem

In this paper, a novel multi objective model is proposed for portfolio selection. The proposed model incorporates the DEA cross-efficiency into Markowitz mean–variance model and considers return, risk and efficiency of the portfolio. Also, in order to take uncertainty in proposed model, the asset returns are considered as trapezoidal fuzzy numbers. Due to the computational complication of the proposed model, the second version of non-dominated sorting genetic algorithm (NSGA-II) is applied. To illustrate the performance of our model, the model is implemented for 52 firms listed in stock exchange market of Iran and the results are analyzed. The results show that the proposed model is suitable in compared with Markowitz and DEA models due to considering return, risk and efficiency, simultaneously.     

An integrated decision-making method for product design scheme evaluation based on cloud model and EEG data

Selecting the optimal design scheme is a vital task in the product design area. It not only improves the performance of the product, but also leads to the greatest satisfaction of customers. However, existing methods express qualitative evaluation information roughly, and none of them has taken the implicit psychological states of customers into consideration. Therefore, an integrated decision-making method for product design scheme evaluation is proposed. This method applies the cloud model to facilitate the evaluation process of experts and uses the EEG data to reveal the psychological states of customers. Benefit from the probability theory and fuzzy set theory, the cloud model deals with the fuzziness and randomness simultaneously. It can decrease the cognitive discrepancy of experts and allow the information distortion to be neutralized to a great extent. Since the experts are not the final users of products, the evaluation results from experts cannot truly reflect the psychological states of customers when they use the product. An experiment is designed to collect the EEG data which can reveal the implicit psychological states of customers. The recorded data are segmented based on the operation process and tagged with the self-reported psychological states. Subsequently, the wavelet packet decomposition is applied and the sample entropy of each EEG frequency band is extracted as the feature. Taking advantage of the random forest classifier, the psychological states of customers can be classified with the average accuracy of 90.76%. This study can lead to a practical system for automatic assessment of psychological states in future applications. The evaluation process of elevator design schemes is conducted as a case study to illustrate the feasibility of the proposed method.     

An improved TOPSIS approach for the competitiveness analysis of provincial information resource industries in China

The development of information resources industries is becoming increasingly important for improving the competitiveness of individual economies. As a result, effectively evaluating the performance of individual information resources industries becomes a critical problem that needs to be adequately addressed. This paper presents an improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach for evaluating the performance of provincial information resources industries in an objective manner in China. The concept of information entropy is appropriately used for determining the objective weighting of the evaluation criteria in the evaluation process. The Mahalanobis distance is adequately adopted for better reflecting the relative distance between individual alternatives and the idea solution in determining the overall performance of individual alternatives across all the evaluation criteria. This leads to the determination of consistent performance evaluation outcomes, which are better accepted by all the stakeholders in the evaluation process. An example of evaluating the competitiveness of provincial information resources industries in China is presented. The result shows that the improved TOPSIS approach is effective for evaluating the competitiveness of information resources industries in the real‐world setting.     

Weight determination in the cross-efficiency evaluation

A neutral data envelopment analysis (DEA) model for cross-efficiency evaluation was recently proposed by Wang and Chin [Wang and Chin (2010b). A neutral DEA model for cross-efficiency evaluation and its extension. Expert Systems with Applications, 37(5), 3666–3675], which maximinimizes the relative efficiency of each output and effectively reduces the number of zero weights of outputs. Since a large number of zero weights may still exist among inputs, this paper proposes a simultaneously input- and output-oriented weight determination DEA model for the cross-efficiency evaluation. The new DEA model proves to reduce the number of zero weights for both inputs and outputs very significantly, as illustrated by numerical examples. The weights determined by the new DEA model are neutral, neither aggressive nor benevolent.     

Comments on finding the most efficient DMUs in DEA: An improved integrated model

This paper improves the integrated DEA model proposed for finding the most efficient DMUs introduced by Amin and Toloo, [Amin, Gholam R., Toloo, M. (2007). Finding the most efficient DMUs in DEA: An improved integrated model. Computers & Industrial Engineering, 52(2), 71–77]. The paper shows the problem of using the integrated DEA model and presents an improved integrated DEA model for determining a single efficient unit. Also the paper indicates the property of the improvements mathematically and a numerical example shows the usefulness and intelligibility of the study.     

An integrated approach for rating engineering characteristics’ final importance in product-service system development

Product-service system (PSS) approach has emerged as a competitive strategy to impel manufacturers to offer a set of product and services as a whole. A three-domain PSS conceptual design framework based on quality function deployment (QFD) is proposed in this research. QFD is a widely used design tool considering customer requirements (CRs). Since both product and services influence satisfaction of customer, they should be designed simultaneously. Identification of the critical parameters in these domains plays an important role. Engineering characteristics (ECs) in the functional domain include product-related ECs (P-ECs) and service-related ECs (S-ECs). ECs are identified by translating customer requirements (CRs) in the customer domain. Rating ECs’ importance has a great impact on achieving an optimal PSS planning. The rating problem should consider not only the requirements of customer, but also the requirements of manufacturer. From the requirements of customer, the analytic network process (ANP) approach is integrated in QFD to determine the initial importance weights of ECs considering the complex dependency relationships between and within CRs, P-ECs and S-ECs. In order to deal with the vagueness, uncertainty and diversity in decision-making, the fuzzy set theory and group decision-making technique are used in the super-matrix approach of ANP. From the requirements of manufacturer, the data envelopment analysis (DEA) approach is applied to adjust the initial weights of ECs taking into account business competition and implementation difficulty. A case study is carried out to demonstrate the effectiveness of the developed integrated approach for prioritizing ECs in PSS conceptual design.     

An AHP/DEA method for measurement of the efficiency of R&D management activities in universities

In order to develop a better tool for the assessment of the management performance of research and development (R&D) activities in research‐oriented universities, a combination of analysis hierarchical process (AHP) and data envelopment analysis (DEA) is proposed for the assessment of the efficiency of R&D management activities in universities. The measure consists of the measurement of a university's previous and present R&D strength by AHP and the assessment of the relative efficiency of its growth in R&D strength against those of other universities by DEA, in which the management basis of the measured universities is taken into consideration. The application of the measure to assess the R&D management efficiency of 29 universities in China indicates the universities which have improved their management work achieved a high efficiency value regardless of whether their original R&D strengths were strong or weak. Such a measure is proved to be helpful for motivating the universities to keep on improving their R&D management.     

An integrated method for supplier selection from the perspective of risk aversion

A lot of investigations have been published in the supplier selection area. However, few papers consider the problem from the perspective of risk aversion. In this paper, generalized intuitionistic fuzzy soft set (GIFSS) combined with extending gray relational analysis (GRA) method is proposed to select an appropriate supplier from the perspective of risk aversion in group decision-making environment. The proposed approach consists of two phases. In the first phase, the weights of decision makers are determined by using an extended GRA method with intuitionistic fuzzy soft set (IFSS). In traditional GRA method, the ideal reference is expressed in a vector, while, in this paper, the ideal reference for all individual decisions is expressed in a matrix, which is suitable for risk aversion. The weight of decision makers is utilized to aggregate individual opinions of decision makers. In the next phase, to eliminate the bias of decision makers in the choice of supplier and rule out the possibility of errors occurring in the evaluation of alternatives, the general manager will further validate it by utilized the GIFSS. Finally, a numerical example for supplier selection is given to illustrate application of the method, and the comparisons with other methods are also made.     

A technical note on ‘erratum to “Finding the most efficient DMUs in DEA: An improved integrated model” [Comput. Indus. Eng. 52 (2007) 71–77]’

You et al. (2013) indicated two errors in Amin and Toloo (2007). The first error was the infeasibility of Amin and Toloo’s (2007) model and the second drawback was the lack of a suitable value for the non-Archimedean epsilon in the proposed approach of Amin and Toloo (2007). This paper deals with the raised issues and proves that the model of Amin and Toloo (2007) is always feasible. In addition, we also formulate a new model for finding a suitable value for the epsilon.     

Improvement of risk assessment in the FMEA using nonlinear model,revised fuzzy TOPSIS,and support vector machine

In every organization, performing accurate risk assessment along with consideration of increasing accidents is a necessary tool to prevent and reduce the fatal and non-fatal consequences of their occurrence. One of the most popular methods of risk assessment is Failure Mode and Effects Analysis, which evaluate failure modes in a system by using risk priority numbers (RPNs). These methods have been criticized for including several deficiencies such as the effect of personal ’opinions, the same importance of the factors and risk rating. The present work utilizes a hybrid approach based on support vector machine and fuzzy inference system to decrease the effect of personal's opinions in determining the factors of the severity and occurrence. Also, Logarithmic Fuzzy Preference Programming is used to determine the crisp weight of dependent factor of FMEA and revised fuzzy TOPSIS used for more accurate ranking of risks. One main feature of the proposed model is that it can be used to evaluate safety risks in all organizations. To investigate the suitability of this approach, the proposed model was presented in the Copper leaching factory, Kerman, Iran. The results showed that this model has the ability to predict severity and occurrence refers to occupational accidents which occurred in a 5-year period (2012–2017) with accuracy of 87% and 95%, respectively. Also, based on the results, it was found that the weights of severity, occurrence, and detection were 0.479, 0.335, and 0.186, respectively. Results of the ranking process showed that the risk of fall from height and stucking between the objects had the highest and the lowest priority, respectively.     

Human factors risk assessment: An integrated method for improving safety in clinical use of medical devices

Medical devices play a critical role in care and treatment. The human-related failures can significantly affect the safety of patients in clinical use of medical devices. This study develops a comprehensive risk assessment model for identification and evaluation of failures which may occur in the clinical use of medical devices. First, the “Swiss cheese” model and SHEL model (the acronym of software, hardware, environment, and liveware) are integrated to comprehensively identify the potential human errors. Then, a new failure mode and effects analysis (FMEA) approach improved by rough set theory and grey relational analysis is developed to assess the risk of the identified failures. The proposed method integrates the strengths of the “Swiss cheese” and SHEL model in identifying human failures from both the vertical and horizontal perspectives of the system, and the advantages of the improved FMEA approach in flexibly manipulating vague information in risk evaluation without much priori information. Finally, the proposed method is applied in clinical use of respirator to verify its efficiency and effectiveness.     

A new mixed integer linear model for selecting the best decision making units in data envelopment analysis

In a recent paper by Amin (Amin, Gholam R. (2009). Comment on finding the most efficient DMUs in DEA: An improved integrated model. Computers & Industrial Engineering, 56, 1701–1702), he proposed an improved approach to determine a single efficient DMU as the most (or the best) efficient DMU. It will be shown that this nonlinear mixed integer model may fail to produce a solution since it can be infeasible in some cases. In this paper, a linear mixed integer model is proposed which is feasible and can produce a single efficient DMU as well. The model can also be extended to rank all extreme efficient DMUs. Some properties and advantages of the model will be explained. The contents of the paper will be illustrated by some numerical examples including a real data set of nineteen facility layout alternatives.     

Response time assessment in forest fire spread simulation: An integrated methodology for efficient exploitation of available prediction time

This work details a framework developed to shorten the time needed to perform fire spread predictions. The methodology presented relies on a two-stage prediction strategy which introduces a calibration stage in order to relieve the effects of uncertainty on simulator input parameters. Early assessment of the response time and quality of the results obtained constitute a key component in this method. This automatic and intelligent process of identification of lengthy simulations that slow down the course of the predictions presents a very high hit ratio. However, discarding certain simulations from the adjustment process (based on evolutionary algorithms) could lead to loss of accuracy in our predictions. A strong statistical study to analyze the impact of this action on our final predictions is reported. This study is based on a real fire which burnt 13,000 ha in the region of Catalonia (north-east of Spain) in the summer of 2012.     

A data envelopment analysis method for optimizing multi-response problem with censored data in the Taguchi method

Taguchi method is an efficient method used in off-line quality control in that the experimental design is combined with the quality loss. This method including three stages of systems design, parameter design, and tolerance design has been deeply discussed in Phadke [Quality engineering using robust design (1989)]. It is observable that most industrial applications solved by Taguchi method belong to single-response problems. However, in the real world more than one quality characteristic should be considered for most industrial products, i.e. most problems customers concern about are multi-response problems. As a result, Taguchi method is not appropriate to optimize a multi-response problem. At present, it is still necessary to rely on the engineering judgment to optimize the multi-response problem; therefore uncertainty will be increased during the decision-making process. On the other hand, due to some uncontrollable causes occurring, only a portion of experiment can be completed so that the censored data will be produced. Traditional approaches for analysis of censored data are computationally complicated. In order to overcome above two shortages, this article proposes an effective procedure on the basis of the neural network (NN) and the data envelopment analysis (DEA) to optimize the multi-response problems. A case study of improving the quality of hard disk driver in Su and Tong [ Total Quality Management 8 (1997) 409] is resolved by the proposed procedure. The result indicates that it yields a satisfactory solution.     

An analysis of the loop optimization scores in knuth's ‘empirical study of FORTRAN programs’

The optimization scores for Knuth's random sample of inner loops are analysed to provide a unified comparison of the five optimization levels. The techniques used are those of exploratory data analysis, and their role in the analysis is discussed. As a consequence of the analysis, five rough groups of programs with different optimization behaviour are identified and tentatively characterized.     

Multi-dimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation

There has been little research on assessment of learning management systems (LMS) within educational organizations as both a web-based learning system for e-learning and as a supportive tool for blended learning environments. This study proposes a conceptual e-learning assessment model, hexagonal e-learning assessment model (HELAM) suggesting a multi-dimensional approach for LMS evaluation via six dimensions: (1) system quality, (2) service quality, (3) content quality, (4) learner perspective, (5) instructor attitudes, and (6) supportive issues. A survey instrument based on HELAM has been developed and applied to 84 learners. This sample consists of students at both undergraduate and graduate levels who are users of a web-based learning management system, U-Link, at Brunel University, UK. The survey instrument has been tested for content validity, reliability, and criterion-based predictive validity. The analytical results strongly support the appropriateness of the proposed model in evaluating LMSs through learners’ satisfaction. The explanatory factor analysis showed that each of the six dimensions of the proposed model had a significant effect on the learners’ perceived satisfaction. Findings of this research will be valuable for both academics and practitioners of e-learning systems.