Integrating Fuzzy C-Means and TOPSIS for performance evaluation: An application and comparative analysis

In this paper we introduce a multi-method multiple criteria approach for evaluating the performance of organizations. Performance analysis may include both strategic and operational performance, as well as financial and other less tangible factors. This paper introduces the use of Fuzzy C-Means and TOPSIS for organizational performance evaluation purposes. Using real company data and balanced scorecard accounting and performance dimensions the methodology is applied and evaluated. The predictive abilities of the technique from an organizational performance evaluation approach are evaluated using this data. One of the results from the illustrative application is that economic performance evaluation is not the best predictor of overall viability of some organizations, especially e-commerce based organizations.     

A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics

Due to green legislations, industries track the used products through reverse logistics contractors. A reverse logistics programme offers significant cost savings in procurement, transportation, disposal and inventory carrying. Since reverse logistics operations and the supply chains they support are considerably more complex than traditional manufacturing supply chains, it can be offered to third party contractors. But availability of more number of contractors make evaluating and selecting the most efficient Reverse Logistics Contractor (RLC) a challenging task and treated as a multi-criteria decision making problem. In this paper, a hybrid method using Analytical Hierarchy Process (AHP) and the Fuzzy Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) is proposed. AHP is used to obtain the initial weights and Fuzzy TOPSIS is used to get the final ranking. A case study demonstrates the application of the proposed method. Finally sensitivity analysis is carried out to confirm the robustness.     

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.     

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.     

GPROC—an integrated system for the processing of numerical scientific data

Work done on an interactive graphical processing system is described. An overview is given of the uses to which a system could be put, with especial reference to aspects of solid state physics. Algorithms which may be usefully included in such a system, when used in a technical scientific environment, are described. The problems of implementing such a system on small microcomputer systems are outlined, and some possible areas of future development are discussed.     

Using the BCD model for risk analysis: An influence diagram based approach

In this paper, we are interested in addressing risk analysis. We propose an influence diagram-based approach that focuses on a Benefit, Cost, Deficit (BCD) model. The BCD model is proposed for studying the intentional deviant behaviors of human operators in a system. In this model, the consequences of human actions are analyzed with respect to three parameters: benefit, cost and deficit. Our approach aims to expand the BCD model by integrating factors, such as those related to the organization of the system in question, that influence human operator actions. In addition, the approach considers multiple criteria that are related, for example, to safety and productivity. To build a model that evaluates the risk induced by human actions in a system and analyzes the impact of the different factors, we use influence diagrams. Influence diagrams are probabilistic graphical models that can deal with uncertainty and with incomplete and imprecise information. Influence diagrams also represent the interdependencies between the different variables of the studied problem. In addition, contrary to Bayesian networks, influence diagrams can rank a set of actions by providing information on which action carries the greatest risk or the most benefits. We applied this approach to a case study of an industrial rotary press, but it can also be used in other problems and sectors.     

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.     

Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process

Data envelopment analysis (DEA) is proposed in this paper to generate local weights of alternatives from pair-wise comparison judgment matrices used in the analytic hierarchy process (AHP). The underlying assumption behind the approach is explained, and some salient features are explored. It is proved that DEA correctly estimates the true weights when applied to a consistent matrix formed using a known set of weights. DEA is further proposed to aggregate the local weights of alternatives in terms of different criteria to compute final weights. It is proved further that the proposed approach, called DEAHP in this paper, does not suffer from rank reversal when an irrelevant alternative(s) is added or removed.     

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.     

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.     

Data envelopment analysis for evaluating the efficiency of genetic algorithms on solving the vehicle routing problem with soft time windows

This study proposes an alternative to the conventional empirical analysis approach for evaluating the relative efficiency of distinct combinations of algorithmic operators and/or parameter values of genetic algorithms (GAs) on solving the pickup and delivery vehicle routing problem with soft time windows (PDVRPSTW). Our approach considers each combination as a decision-making unit (DMU) and adopts data envelopment analysis (DEA) to determine the relative and cross efficiencies of each combination of GA operators and parameter values on solving the PDVRPSTW. To demonstrate the applicability and advantage of this approach, we implemented a number of combinations of GA’s three main algorithmic operators, namely selection, crossover and mutation, and employed DEA to evaluate and rank the relative efficiencies of these combinations. The numerical results show that DEA is well suited for determining the efficient combinations of GA operators. Among the combinations under consideration, the combinations using tournament selection and simple crossover are generally more efficient. The proposed approach can be adopted to evaluate the relative efficiency of other meta-heuristics, so it also contributes to the algorithm development and evaluation for solving combinatorial optimization problems from the operational research perspective.     

Alternative mixed integer linear programming models for identifying the most efficient decision making unit in data envelopment analysis

A mixed integer linear model for selecting the best decision making unit (DMU) in data envelopment analysis (DEA) has recently been proposed by Foroughi [Foroughi, A. A. (2011a). A new mixed integer linear model for selecting the best decision making units in data envelopment analysis. Computers and Industrial Engineering, 60(4), 550–554], which involves many unnecessary constraints and requires specifying an assurance region (AR) for input weights and output weights, respectively. Its selection of the best DMU is easy to be affected by outliers and may sometimes be incorrect. To avoid these drawbacks, this paper proposes three alternative mixed integer linear programming (MILP) models for identifying the most efficient DMU under different returns to scales, which contain only essential constraints and decision variables and are much simpler and more succinct than Foroughi’s. The proposed alternative MILP models can make full use of input and output information without the need of specifying any assurance regions for input and output weights to avoid zero weights, can make correct selections without being affected by outliers, and are of significant importance to the decision makers whose concerns are not DMU ranking, but the correct selection of the most efficient DMU. The potential applications of the proposed alternative MILP models and their effectiveness are illustrated with four numerical examples.     

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.     

On the Role of Metadata in Visual Language Reuse and Reverse Engineering – An Industrial Case

Collecting metadata on a family of programs is useful not only for generating statistical data on the programs but also for future re-engineering and reuse purposes. In this paper we discuss an industrial case where a project library is used to store visual programs and a database to store the metadata on these programs. The visual language in question is a domain-specific language, Function Block Language (FBL) that is used in Metso Automation for writing automation control programs. For reuse, program analysis and re-engineering activities and various data and program analysis methods are applied to study the FBL programs. Metadata stored in a database is used to provide advanced program analysis support; from the large amount of programs, the metadata allows focusing the analysis to certain kinds of programs. In this paper, we discuss the role and usage of the metadata in program analysis techniques applied to FBL programs.     

An evidential approach for detection of abnormal behaviour in the presence of unreliable sensors

We address the problem of abnormal behaviour recognition of the inhabitant of a smart home in the presence of unreliable sensors. The corner stone of this work is a two-level architecture sensor fusion based on the Transferable Belief Model (TBM). The novelty of our work lies in the way we detect both unreliable sensors and abnormal behaviour within our architecture by using a temporal analysis of conflict resulting from the fusion of sensors. Detection of abnormal behaviour is based on a prediction/observation process and the influence of the faulty sources is discarded by discounting coefficients. Our architecture is tested in a real-life setting using three heterogeneous sensors enabling the detection of impossible transitions between three possible postures: Sitting, Standing and Lying. The impact of having a faulty sensor management is also tested in the real-life experiment for posture detection.     

Modeling the risk for a new invasive forest disease in the United States: An evaluation of five environmental niche models

Efforts to model the potential habitat and risk for spread of invasive diseases such as Sudden Oak Death (SOD) are important for disease regulation and management. However, spatially referenced risk models using identical data can have differing results, making decision-making based on the mapped results problematic. We examined the results from five spatial risk models generated from common input parameters, and investigated model agreement for mapping risk for the causal pathogen for SOD, Phytophthora ramorum across the conterminous United States. We examined five models: Expert-driven Rule-based, Logistic Regression, Classification and Regression Trees, Genetic Algorithms, and Support Vector Machines. All models were consistent in their prediction of some SOD risk in coastal California, Oregon and Washington states, and in the northern foothills of the Sierra Nevada Mountains in California, and in an east–west oriented band including eastern Oklahoma, central Arkansas, Tennessee, Kentucky, northern Mississippi, Alabama, Georgia and South Carolina, parts of central North Carolina, and eastern Virginia, Delaware and Maryland states. The SVM model was the most accurate model, and had several advantages over the other models. Although theoretical in nature, this paper presents results that have practical, applied value for managers and regulators of this disease, and discusses common challenges in modeling invasive species niches over large scales.