Fuzzy assessment of performance of a decision making units using DEA:a non-radical approach

Data Envelopment Analysis (DEA) is recognized as a modern approach to the assessment of performance of a set of homogeneous Decision Making Units (DMUs) that use similar sources to produce similar outputs. While DEA commonly is used with precise data, recently several approaches are introduced for evaluating DMUs with uncertain data. In the existing approaches many information on uncertainties are lost. For example in the defuzzification, the a-level and fuzzy ranking approaches are not considered. In the tolerance approach the inequality or equality signs are fuzzified but the fuzzy coefficients (inputs and outputs) are not treated directly. The purpose of this paper is to develop a new model to evaluate DMUs under uncertainty using Fuzzy DEA and to include a-level to the model under fuzzy environment. An example is given to illustrate this method in details.

Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector

Guest editorial Ali Emrouznejad is a Senior Lecturer at the Aston Business School in Birmingham, UK. His areas of research interest include performance measurement and management, efficiency and productivity analysis as well as data mining. He has published widely in various international journals. He is an Associate Editor of IMA Journal of Management Mathematics and Guest Editor to several special issues of journals including Journal of Operational Research Society, Annals of Operations Research, Journal of Medical Systems, and International Journal of Energy Management Sector. He is in the editorial board of several international journals and co-founder of Performance Improvement Management Software. William Ho is a Senior Lecturer at the Aston University Business School. Before joining Aston in 2005, he had worked as a Research Associate in the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University. His research interests include supply chain management, production and operations management, and operations research. He has published extensively in various international journals like Computers & Operations Research, Engineering Applications of Artificial Intelligence, European Journal of Operational Research, Expert Systems with Applications, International Journal of Production Economics, International Journal of Production Research, Supply Chain Management: An International Journal, and so on. His first authored book was published in 2006. He is an Editorial Board member of the International Journal of Advanced Manufacturing Technology and an Associate Editor of the OR Insight Journal. Currently, he is a Scholar of the Advanced Institute of Management Research. Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector This special issue aims to focus on holistic, applied research on performance measurement in energy sector management and for publication of relevant applied research to bridge the gap between industry and academia. After a rigorous refereeing process, seven papers were included in this special issue. The volume opens with five data envelopment analysis (DEA)-based papers. Wu et al. apply the DEA-based Malmquist index to evaluate the changes in relative efficiency and the total factor productivity of coal-fired electricity generation of 30 Chinese administrative regions from 1999 to 2007. Factors considered in the model include fuel consumption, labor, capital, sulphur dioxide emissions, and electricity generated. The authors reveal that the east provinces were relatively and technically more efficient, whereas the west provinces had the highest growth rate in the period studied. Ioannis E. Tsolas applies the DEA approach to assess the performance of Greek fossil fuel-fired power stations taking undesirable outputs into consideration, such as carbon dioxide and sulphur dioxide emissions. In addition, the bootstrapping approach is deployed to address the uncertainty surrounding DEA point estimates, and provide bias-corrected estimations and confidence intervals for the point estimates. The author revealed from the sample that the non-lignite-fired stations are on an average more efficient than the lignite-fired stations. Maethee Mekaroonreung and Andrew L. Johnson compare the relative performance of three DEA-based measures, which estimate production frontiers and evaluate the relative efficiency of 113 US petroleum refineries while considering undesirable outputs. Three inputs (capital, energy consumption, and crude oil consumption), two desirable outputs (gasoline and distillate generation), and an undesirable output (toxic release) are considered in the DEA models. The authors discover that refineries in the Rocky Mountain region performed the best, and about 60 percent of oil refineries in the sample could improve their efficiencies further. H. Omrani, A. Azadeh, S. F. Ghaderi, and S. Abdollahzadeh presented an integrated approach, combining DEA, corrected ordinary least squares (COLS), and principal component analysis (PCA) methods, to calculate the relative efficiency scores of 26 Iranian electricity distribution units from 2003 to 2006. Specifically, both DEA and COLS are used to check three internal consistency conditions, whereas PCA is used to verify and validate the final ranking results of either DEA (consistency) or DEA-COLS (non-consistency). Three inputs (network length, transformer capacity, and number of employees) and two outputs (number of customers and total electricity sales) are considered in the model. Virendra Ajodhia applied three DEA-based models to evaluate the relative performance of 20 electricity distribution firms from the UK and the Netherlands. The first model is a traditional DEA model for analyzing cost-only efficiency. The second model includes (inverse) quality by modelling total customer minutes lost as an input data. The third model is based on the idea of using total social costs, including the firm’s private costs and the interruption costs incurred by consumers, as an input. Both energy-delivered and number of consumers are treated as the outputs in the models. After five DEA papers, Stelios Grafakos, Alexandros Flamos, Vlasis Oikonomou, and D. Zevgolis presented a multiple criteria analysis weighting approach to evaluate the energy and climate policy. The proposed approach is akin to the analytic hierarchy process, which consists of pairwise comparisons, consistency verification, and criteria prioritization. In the approach, stakeholders and experts in the energy policy field are incorporated in the evaluation process by providing an interactive mean with verbal, numerical, and visual representation of their preferences. A total of 14 evaluation criteria were considered and classified into four objectives, such as climate change mitigation, energy effectiveness, socioeconomic, and competitiveness and technology. Finally, Borge Hess applied the stochastic frontier analysis approach to analyze the impact of various business strategies, including acquisition, holding structures, and joint ventures, on a firm’s efficiency within a sample of 47 natural gas transmission pipelines in the USA from 1996 to 2005. The author finds that there were no significant changes in the firm’s efficiency by an acquisition, and there is a weak evidence for efficiency improvements caused by the new shareholder. Besides, the author discovers that parent companies appear not to influence a subsidiary’s efficiency positively. In addition, the analysis shows a negative impact of a joint venture on technical efficiency of the pipeline company. To conclude, we are grateful to all the authors for their contribution, and all the reviewers for their constructive comments, which made this special issue possible. We hope that this issue would contribute significantly to performance improvement of the energy sector.

A critique of a model for an academic staff activity database developed to aid a department in strategic and operational decision making

This paper discusses the use of a Model developed by Aston Business School to record the work load of its academic staff. By developing a database to register annual activity in all areas of teaching, administration and research the School has created a flexible tool which can be used for facilitating both day-to-day managerial and longer term strategic decisions. This paper gives a brief outline of the Model and discusses the factors which were taken into account when setting it up. Particular attention is paid to the uses made of the Model and the problems encountered in developing it. The paper concludes with an appraisal of the Model’s impact and of additional developments which are currently being considered. Aston Business School has had a Load Model in some form for many years. The Model has, however, been refined over the past five years, so that it has developed into a form which can be used for a far greater number of purposes within the School. The Model is coordinated by a small group of academic and administrative staff, chaired by the Head of the School. This group is responsible for the annual cycle of collecting and inputting data, validating returns, carrying out analyses of the raw data, and presenting the mater ial to different sections of the School. The authors of this paper are members of this steer ing group.

The influence of context on the strategic decision-making process:a review of the literature

This paper critically reviews the strategic decision-making process literature, with a specific focus on the effects of context. Context refers to the top management team, strategic decision-specific characteristics, the external environment and firm characteristics. This literature review also develops an illustrative framework that incorporates these four different categories of contextual variables that influence the strategic decision-making process. As a result of the variety and pervasiveness of contextual variables featured within the literature, a comprehensive and up-to-date review is essential for organizing and synthesizing the extant literature to explicate an agenda for future research. The purpose of this literature review is threefold: first, to critically review the strategic decision-making process literature to highlight the underlying themes, issues, tensions and debates in the field; second, to identify the opportunities for future theory development; and third, to state the methodological implications arising from this review. © 2013 British Academy of Management and John Wiley & Sons Ltd.

An empirical examination of the strategic decision-making process:the relationship between context, process, and outcomes

Despite concerted academic interest in the strategic decision-making process (SDMP) since the 1980s, a coherent body of theory capable of guiding practice has not materialised. This is because many prior studies focus only on a single process characteristic, often rationality or comprehensiveness, and have paid insufficient attention to context. To further develop theory, research is required which examines: (i) the influence of context from multiple theoretical perspectives (e.g. upper echelons, environmental determinism); (ii) different process characteristics from both synoptic formal (e.g. rationality) and political incremental (e.g. politics) perspectives, and; (iii) the effects of context and process characteristics on a range of SDMP outcomes. Using data from 30 interviews and 357 questionnaires, this thesis addresses several opportunities for theory development by testing an integrative model which incorporates: (i) five SDMP characteristics representing both synoptic formal (procedural rationality, comprehensiveness, and behavioural integration) and political incremental (intuition, and political behaviour) perspectives; (ii) four SDMP outcome variables—strategic decision (SD) quality, implementation success, commitment, and SD speed, and; (iii) contextual variables from the four theoretical perspectives—upper echelons, SD-specific characteristics, environmental determinism, and firm characteristics. The present study makes several substantial and original contributions to knowledge. First, it provides empirical evidence of the contextual boundary conditions under which intuition and political behaviour positively influence SDMP outcomes. Second, it establishes the predominance of the upper echelons perspective; with TMT variables explaining significantly more variance in SDMP characteristics than SD specific characteristics, the external environment, and firm characteristics. A newly developed measure of top management team expertise also demonstrates highly significant direct and indirect effects on the SDMP. Finally, it is evident that SDMP characteristics and contextual variables influence a number of SDMP outcomes, not just overall SD quality, but also implementation success, commitment, and SD speed.

A review of multi-criteria decision-making methods for bioenergy systems

Bioenergy schemes are multi-faceted and complex by nature, with many available raw material supplies and technical options and a diverse set of stakeholders holding a raft of conflicting opinions. To develop and operate a successful scheme there are many requirements that should be considered and satisfied. This paper provides a review of those academic works attempting to deal with problems arising within the bioenergy sector using multi-criteria decision-making (MCDM) methods. These methods are particularly suitable to bioenergy given its multi-faceted nature but could be equally relevant to other energy conversion technologies. Related articles appearing in the international journals from 2000 to 2010 are gathered and analysed so that the following two questions can be answered. (i) Which methods are the most popular? (ii) Which problems attract the most attention? The review finds that optimisation methods are most popular with methods choosing between few alternatives being used in 44% of reviewed papers and methods choosing between many alternatives being used in 28%. The most popular application area was to technology selection with 27% of reviewed papers followed by policy decisions with 18%. © 2012 Elsevier Ltd.

An examination of intuition in strategic decision-making:the role of context

Intuition is a vitally important concept in strategic decision making research because it enables decision-makers to rapidly detect patterns in dynamic environments in order to cope with the time-pressured, ill-structured and non-routine nature of strategic decision-making. Despite a growing body of conceptual literature emphasising the importance of intuition in strategic decision-making; there has been very little development of theory explaining the contextual factors that cause intuition to be used in the strategic decision-making process. This paper demonstrates that by integrating different contextual variables a clear understanding of the influences on the use of intuition in strategic decision-making can be developed. This article develops an integrative theoretical model together with testable research propositions, which if empirically examined, would make a substantial contribution to knowledge.

The influence of patient's age on clinical decision-making about coronary heart disease in the USA and the UK

This paper examines UK and US primary care doctors' decision-making about older (aged 75 years) and midlife (aged 55 years) patients presenting with coronary heart disease (CHD). Using an analytic approach based on conceptualising clinical decision-making as a classification process, it explores the ways in which doctors' cognitive processes contribute to ageism in health-care at three key decision points during consultations. In each country, 56 randomly selected doctors were shown videotaped vignettes of actors portraying patients with CHD. The patients' ages (55 or 75 years), gender, ethnicity and social class were varied systematically. During the interviews, doctors gave free-recall accounts of their decision-making. The results do not establish that there was substantial ageism in the doctors' decisions, but rather suggest that diagnostic processes pay insufficient attention to the significance of older patients' age and its association with the likelihood of co-morbidity and atypical disease presentations. The doctors also demonstrated more limited use of 'knowledge structures' when diagnosing older than midlife patients. With respect to interventions, differences in the national health-care systems rather than patients' age accounted for the differences in doctors' decisions. US doctors were significantly more concerned about the potential for adverse outcomes if important diagnoses were untreated, while UK general practitioners cited greater difficulty in accessing diagnostic tests.

Investigating the influence of African American and African Caribbean race on primary care doctors' decision making about depression

This paper explores differences in how primary care doctors process the clinical presentation of depression by African American and African-Caribbean patients compared with white patients in the US and the UK. The aim is to gain a better understanding of possible pathways by which racial disparities arise in depression care. One hundred and eight doctors described their thought processes after viewing video recorded simulated patients presenting with identical symptoms strongly suggestive of depression. These descriptions were analysed using the CliniClass system, which captures information about micro-components of clinical decision making and permits a systematic, structured and detailed analysis of how doctors arrive at diagnostic, intervention and management decisions. Video recordings of actors portraying black (both African American and African-Caribbean) and white (both White American and White British) male and female patients (aged 55 years and 75 years) were presented to doctors randomly selected from the Massachusetts Medical Society list and from Surrey/South West London and West Midlands National Health Service lists, stratified by country (US v.UK), gender, and years of clinical experience (less v. very experienced). Findings demonstrated little evidence of bias affecting doctors' decision making processes, with the exception of less attention being paid to the potential outcomes associated with different treatment options for African American compared with White American patients in the US. Instead, findings suggest greater clinical uncertainty in diagnosing depression amongst black compared with white patients, particularly in the UK. This was evident in more potential diagnoses. There was also a tendency for doctors in both countries to focus more on black patients' physical rather than psychological symptoms and to identify endocrine problems, most often diabetes, as a presenting complaint for them. This suggests that doctors in both countries have a less well developed mental model of depression for black compared with white patients. © 2014 The Authors.

The Problems of Forming the Inner Personal Conflict as a Factor which Influences Decision-making

the article views examine the problems concerning with the sources of origin of unconscious the inner personal conflicts and the way the presence of this factor is reflected on the decision-making process by a person.

Synergetic Methods of Complexation in Decision Making Problems

Synergetic methods of data complexation are proposed that make it possible to obtain a maximal amount of available information using a limited number of channels. Along with freedom degrees reducers, a mechanism of freedom degrees discriminators is proposed that enables all the channels to take part in the development of a cooperative decision in accordance with their informativeness in a current situation.

Operating Model of Knowledge Quantum Engineering for Decision-Making in Conditions of Indeterminacy

The operating model of knowledge quantum engineering for identification and prognostic decision- making in conditions of α-indeterminacy is suggested in the article. The synthesized operating model solves three basic tasks: Аt-task to formalize tk-knowledge; Вt-task to recognize (identify) objects according to observed results; Сt-task to extrapolate (prognosticate) the observed results. Operating derivation of identification and prognostic decisions using authentic different-level algorithmic knowledge quantum (using tRAKZ-method) assumes synthesis of authentic knowledge quantum database (BtkZ) using induction operator as a system of implicative laws, and then using deduction operator according to the observed tk-knowledge and BtkZ a derivation of identification or prognostic decisions in a form of new tk-knowledge.

Understanding dynamics of strategic decision-making in venture creation:a process study of effectuation and causation

This study draws upon effectuation and causation as examples of planning-based and flexible decision-making logics, and investigates dynamics in the use of both logics. The study applies a longitudinal process research approach to investigate strategic decision-making in new venture creation over time. Combining qualitative and quantitative methods, we analyze 385 decision events across nine technology-based ventures. Our observations suggest a hybrid perspective on strategic decision-making, demonstrating how effectuation and causation logics are combined, and how entrepreneurs’ emphasis on these logics shifts and re-shifts over time. We induce a dynamic model which extends the literature on strategic decision-making in venture creation.

Methods of Adaptive Extraction and Analysis of Knowledge for Knowledge-base Construction and Fast Decision Making

An approach for knowledge extraction from the information arriving to the knowledge base input and also new knowledge distribution over knowledge subsets already present in the knowledge base is developed. It is also necessary to realize the knowledge transform into parameters (data) of the model for the following decision-making on the given subset. It is assumed to realize the decision-making with the fuzzy sets’ apparatus.

Model of Active Structural Monitoring and Decision-making for Dynamic Identification of Buildings, Monuments and Engineering Facilities

Structural monitoring and dynamic identification of the manmade and natural hazard objects is under consideration. Math model of testing object by set of weak stationary dynamic actions is offered. The response of structures to the set of signals is under processing for getting important information about object condition in high frequency band. Making decision procedure into active monitoring system is discussed as well. As an example the monitoring outcome of pillar-type monument is given.