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The purpose of this study is to use the individual blade pitch control (IBC), reduce actively both the rotor hub vibratory loads and airframe vibration responses for the lift-offset compound helicopter at a high-speed flight condition.

The Sikorsky X2 technology demonstrator (X2TD) is used as the lift-offset compound helicopter. The X2TD lift-offset rotor is modelled and its rotor hub vibratory loads at a flight speed of 250 knots are predicted using a rotorcraft comprehensive analysis code, CAMRAD II, and the airframe structural dynamics is represented with a finite element analysis code, MSC.NASTRAN. When the propulsive trim methodology is applied for rotor trim, the best input condition for IBC using multiple harmonic inputs is searched to reduce the rotor vibration, while the rotor aerodynamic performance (the rotor effective lift-to-drag ratio) is improved or maintained at least. Finally, the reduction in airframe vibration responses is investigated when the best input condition for IBC with multiple harmonics is applied to the lift-offset rotor.

When the IBC with the single harmonic input using the 2/rev actuation frequency, amplitude of 2° and control phase angle of 120° (2P/2°/120°) is considered for X2TD rotor, the rotor vibration is reduced by about 26.37% only and the rotor effective lift-to-drag ratio increases slightly by 0.98%. When X2TD rotor uses the IBC with multiple harmonic inputs (2P/2°/45° + 5P/1°/90°), the rotor hub vibratory loads and airframe vibration responses are reduced by 44.69% and from 0.48 to 79.10%, respectively, while rotor effective lift-to-drag ratio is improved by 0.77%, as compared to the baseline without IBC.

This study is the first study to use the 2/rev actuation for IBC to the four-bladed lift-offset coaxial rotor and to investigate to obtain simultaneously the rotor vibration reduction, rotor performance improvement and airframe vibration reduction, using IBC with multiple harmonic inputs.

To provide a good insight into solving a multi‐response optimization problem using neuro‐fuzzy model and Taguchi method of experimental design.

Over the last few years in many manufacturing organizations, multiple response optimization problems were resolved using the past experience and engineering judgment, which leads to increase in uncertainty during the decision‐making process. In this paper, a four‐step procedure is proposed to resolve the parameter design problem involving multiple responses. This approach employs the advantage of both artificial intelligence tool (neuro‐fuzzy model) and Taguchi method of experimental design to tackle problems involving multiple responses optimization.

The proposed methodology is validated by revisiting a case study to optimize the three responses for a double‐sided surface mount technology of an electronic assembly. Multiple signal‐to‐noise ratios are mapped into a single performance statistic through neuro‐fuzzy based model, to identify the optimal level settings for each parameter. Analysis of variance is finally performed to identify parameters significant to the process.

The proposed model will be validated in future by conducting a real life case study, where multiple responses need to be optimized simultaneously.

It is believed that the proposed procedure in this study can resolve a complex parameter design problem with multiple responses. It can be applied to those areas where there are large data sets and a number of responses are to be optimized simultaneously. In addition, the proposed procedure is relatively simple and can be implemented easily by using ready‐made neural and statistical software like Neuro Work II professional and Minitab.

This study adds to the literature of multi‐optimization problem, where a combination of the neuro‐fuzzy model and Taguchi method is utilized hand‐in‐hand.

To investigate the relationship between performance frontier and operations strategy. A two-level conceptual framework is proposed based on performance elements that act as output/input variables and delimit the scope of the frontier analysis.

The framework proposition is based on the fourth round of high-performance manufacturing survey data. A representative set of variables for assessing performance based on operations strategy constructs is defined through multivariate data analysis techniques. The main method used is the principal component analysis.

The proposed first-level conceptual framework formalizes the relationships between performance frontier analysis techniques and operations strategy, delimiting the scope and the structural definitions. The second-level conceptual framework defines the constructs of the input and output dimensions for frontier analysis studies.

The paper contribution is developed in the gap of market-led orientation to study operations strategy performance frontier since most related literature focuses on capabilities development with a main focus on the resource-based view (RBV) approach. A conceptual framework based on the competitive priorities is therefore proposed to represent the operations strategy in the view of the frontier techniques. The value lies in defining performance measures which are not a straightforward task as the growth of organization competitiveness and complexity require multiple performance measures. A deeper understanding of frontier estimation on the operations strategy context is also provided, contributing to positively influence firms to succeed in the current dynamic competitive environments.

Analysis of Information Options offers new tools for evaluating investments in research, mineral exploration, logistics, energy transmission, and other information operations. With Information Options, the underlying assets are information assets and the rules governing exercise are based on the realities of the information realm (infosphere). Information Options can be modeled as options to “purchase” information assets by paying the cost of the information operations involved. Information Options arise at several stages of value creation. The initial stage involves observation of physical phenomena with accompanying data capture. The next refinement is to organize the data into structured databases. Then bits of information are selected from storage and synthesized into an information product (such as a management report). Next, the information product is presented to the user via an efficient interface that does not require the user to be a field expert. Information Options are similar in concept to real options but substantially different in their details, since real options have physical objects as the underlying assets and the rules governing exercise are based on the realities of the physical world. Also, while exercising a financial option typically kills the option, Information Options may include multiple exercises. Information Options may involve high volatility or jump processes as well, further enhancing their value. This chapter extends several important real option applications into the information realm, including jump process models and models for valuing options to synthesize any of n information items into any of m output assets.

Data envelopment analysis (DEA) is a multicriteria technique which can take into account multiple inputs and outputs to produce a single aggregate measure of relative efficiency for a set of comparable units. DEA takes into consideration other objectives by including the appropriate variables as part of the DEA model. However, as we will demonstrate, collapsing all the inputs and outputs of several objectives into one aggregate performance measure weakens DEA's ability to discriminate the individual impact of each of these objectives. In this chapter, we apply a multiple objective extension to DEA, called multiple objective DEA (MODEA), which simultaneously controls the weights assigned to the variables found in more than one objective. This MODEA approach more fully measures the impact of each objective and allows the decision-maker to address trade offs among these objectives. The usefulness of the MODEA approach is demonstrated by applying it to the hypothetical example.

This paper seeks to measure and evaluate the productive efficiency of health‐care organizations (hospitals) using a multicriteria Data Envelopment Analysis (DEA) methodology. DEA is a special linear programming model for deriving the comparative efficiency of multiple‐input multiple‐output Decision‐Making Units (DMUs). DEA provides management with information regarding the relatively best practice hospitals in the observation set and locates the relatively inefficient hospitals by comparison with the best practice ones. In addition, it indicates the magnitude of these inefficiencies. The paper selects a set of inputs and outputs for 15 hospitals for a three‐year period. The hospital input measures included are: number of bed days, number of physicians, and number of health personnel. The output measures included are: number of patient days, number of minor operations, and number of major operations. The DEA models are solved using an IBM PC optimization modeling system called LINDO (linear, interactive, discrete optimizer).

Data Envelopment Analysis (DEA) is a procedure to measure the relative efficiency of decision‐making units in situations where there are multiple inputs and outputs with no obvious objective way of aggregating these multiple inputs and outputs into a meaningful index of productive efficiency. A systematic application of DEA in Saudi Arabia is presented – carried out at a small college to assess the relative efficiency of teaching staff. The DEA technique and the data are described, and the results of the analysis are discussed.

The purpose of this paper is to develop and test a model of the relative performance of open source software (OSS) projects.

This paper evaluates the relative performance of OSS projects by evaluating multiple project inputs and multiple project outputs by using a data envelopment analysis (DEA) model. The DEA model produces an efficiency score for each project based on project inputs and outputs. The method of producing an efficiency score is based on the convex envelopment technology structure. The efficiency measure quantifies a “distance” to an efficient frontier.

The DEA model produced an index of corresponding intensities linking an inefficient project to its benchmark efficient project(s). The inefficiency measures produced an ordering of inefficient projects. Eight projects were found to be “efficient” and used as benchmarking projects.

This research is limited to only security‐based OSS projects. Future research on other areas of OSS projects is warranted.

The result of this research is a practical model that can be used by OSS project developers to evaluate the relative performance of their projects and make resource decisions.

This research extends the work of previous studies that have examined the relative performance of software development projects in a traditional development environment. As a result of this research, OSS projects can now be adequately benchmarked and evaluated according to project performance. An OSS project manger can effectively use these results to critically evaluate resources for their project and judge the relative efficiency of the resources.

Deriving a measure of efficiency of public-funded organizations (primarily not-for-profit organizations) and ranking these efficiency measures have been major subjects of debate and discussion. The purpose of this paper is to evaluate the relative performances of public-funded research and development (R&D) organizations functioning across multiple countries working on similar research streams. The authors use multiple measures of inputs and outputs for this purpose.

The authors use the data envelopment analysis (DEA) as the primary methodology of analysis The keywords highlighting the major research areas in the field of non-metrology, conducted by National Physical Laboratory (NPL), India, were utilized to select the global comparators working on similar research streams. These global comparators were three R&D organizations located in the USA and one each located in Germany and Japan. The relative efficiencies of the organizations were assessed with the following output variables – external cash flow, and the numbers of technologies transferred, publications and patents; and the following input variables – amount of grants received from the parent body, and the number of scientific personnel working in these public R&D organizations. The authors follow the output-oriented measure of efficiency at constant return to scale and variable return to scale, along with scale efficiencies.

The performance of NPL, India under multiple dimensions has been evaluated relative to its global comparators – the National Institute for Materials Science, Japan; the National Renewable Energy Laboratory, USA; Fritz Haber Institute of the Max Planck Society, Germany; the National Centre for Atmospheric Research, USA; and the Oak Ridge National Laboratory, USA. The study indicates suggested measures and a set of targets to achieve the best possible performance for NPL and other R&D organizations. In most cases of efficient local but not so efficient global efficiency scores indicate that, on an average, the actual scale of production has diverged from the most productive scale size.

The approach highlights the utilization of the DEA methodology for relative R&D performance assessment of global comparators. The discriminatory analysis has brought into sharp focus the dichotomy between local efficiency and global efficiency scores of these units and issues of scale size and regional disparities. The outcome of this approach is dependent upon correct selection of input and output variables and data availability.

The study results have profound implications for the management of public R&D institutions across nations working on similar-focused research streams, but functioning within different societal, economic, and political contexts.

The present work, being perhaps one of the few multinational studies of relative performance assessment of pubic-funded R&D organizations working on similar research streams, signifies the relevance of such an approach in the field of R&D/innovation management. This has opened up new avenues for further research in this area.

This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical studies. The survey shows the apparent shift from index procedures and traditional OLS estimation of production and cost functions to stochastic frontier methods and Data Envelopment Analysis (DEA) methods over the past three decades. Most of the airline productivity and efficiency studies over the last decade adopt some variant of DEA methods. Researchers in the 1980s and 1990s were mostly interested in the effects of deregulation and liberalization on airline productivity and efficiency as well as the effects of ownership and governance structure. Since the 2000s, however, studies tend to focus on how business models and management strategies affect the performance of airlines. Environmental efficiency now becomes an important area of airline productivity and efficiency studies, focusing on CO₂ emission as a negative or undesirable output. Despite the fact that quality of service is an important aspect of airline business, limited attempts have been made to incorporate quality of service in productivity and efficiency analysis.