A fuzzy decision support method for customer preferences analysis based on Choquet integral

The explosion of the Web 2:0 platforms, with massive volume of user generated data, has presented many new opportunities as well as challenges for organizations in understanding consumer's behavior to support for business planning process. Feature based sentiment mining has been an emerging area in providing tools for automated opinion discovery and summarization to help business managers with achieving such goals. However, the current feature based sentiment mining systems were only able to provide some forms of sentiments summary with respect to product features, but impossible to provide insight into the decision making process of consumers. In this paper, we will present a relatively new decision support method based on Choquet Integral aggregation function, Shapley value and Interaction Index which is able to address such requirements of business managers. Using a study case of Hotel industry, we will demonstrate how this technique can be applied to effectively model the user's preference of (hotel) features. The presented method has potential to extend the practical capability of sentiment mining area, while, research findings and analysis are useful in helping business managers to define new target customers and to plan more effective marketing strategies.

Managing manufacturing technology investments: An intelligent learning system approach

This paper proposes an intelligent decision-support system for managing manufacturing technology investments. The intelligent system is a hybrid integration of two information processing modules: case-based reasoning and fuzzy ARTMAP – a supervised adaptive resonance theory (ART) neural network with a multi-dimensional map. The developed system captures a company's strategic information, provides facilities to quantify qualitative attributes and analyses them alongside the quantitative attributes in an evaluation framework. Through the system, similar cases can be retrieved to enable managers to make effective use of their knowledge and experience of previously delivered technologies and projects as an input to the prioritization of future projects. Other salient features of the system include its ability to adapt and absorb new knowledge and responses pertaining to significant events in the business environment, as well as to extract and elucidate information from the knowledge database for explaining and justifying its analysis. The applicability of the developed system is evaluated using a real case study in collaboration with a pharmaceutical manufacturing firm.

BI-based organizations : a sensemaking perspective

Business intelligence (BI) offers opportunities for managers to master vast data resources for operational and strategic gains, and allows BI-based organizations to generate significant business value. While several researchers emphasized the importance of BI to assist making quality decisions, no study explored the use of BI for improved understanding of business before such decisions are made and assessing the impact of the actions derived from these decisions. To fill this gap we use the theory of organizational sensemaking. The presented research uses hermeneutic phenomenology to study the experiences of decision-makers in using BI-generated insights to guide their actions while altering business processes, structures and information. The study emphasizes the necessity of using BI in the creation and maintenance of individual and organizational identity, as well as, enactment of this identity on the business and its environment, which need to be molded in response to changing circumstances.

Medical data classification using interval type-2 fuzzy logic system and wavelets

This paper introduces an automated medical data classification method using wavelet transformation (WT) and interval type-2 fuzzy logic system (IT2FLS). Wavelet coefficients, which serve as inputs to the IT2FLS, are a compact form of original data but they exhibits highly discriminative features. The integration between WT and IT2FLS aims to cope with both high-dimensional data challenge and uncertainty. IT2FLS utilizes a hybrid learning process comprising unsupervised structure learning by the fuzzy c-means (FCM) clustering and supervised parameter tuning by genetic algorithm. This learning process is computationally expensive, especially when employed with high-dimensional data. The application of WT therefore reduces computational burden and enhances performance of IT2FLS. Experiments are implemented with two frequently used medical datasets from the UCI Repository for machine learning: the Wisconsin breast cancer and Cleveland heart disease. A number of important metrics are computed to measure the performance of the classification. They consist of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. Results demonstrate a significant dominance of the wavelet-IT2FLS approach compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus useful as a decision support system for clinicians and practitioners in the medical practice. copy; 2015 Elsevier B.V. All rights reserved.

Modelling a helicopter training continuum to support system transformation

his study investigates the role of system dynamics (SD) modeling to support strategic decision making for an aviation training continuum that is going through major change. The Australian helicopter training continuum (HTC) is currently undergoing transformation, with restructure and consolidation of training schools and training platforms across multiple services. In this research, we introduce a novel SD-based HTC simulation architecture to facilitate the discovery of relationships between student and instructor development and flow dynamics. The proposed simulation architecture employs hybrid push – pull flow control to quantify transience and estimate recovery time after a policy change or disturbance. This architecture allows for multiple student and instructor types, and their respective intake levels and pass rates. Here the instructor variables include availability, specialization and experience. Enos (2011) successfully explored the application of SD modeling to understand the behavior for combat aviation training in an individual school. This research employs a similar modeling philosophy, but takes a higher level view of the system by looking across multiple training schools, which introduces complexity due to pooling, latency and the amplification of affects across the system. The ability to identify causal relationships allowed stakeholders to develop a deeper understanding of the underlying systemic problems, such as delayed transitions between schools and instructor shortages, whilst the hybrid “push-pull” design allowed us to quantify the pooling of students between schools.

A mobile cloud computing framework integrating multilevel encoding for performance monitoring in telerehabilitation

Recent years have witnessed a surge in telerehabilitation and remote healthcare systems blessed by the emerging low-cost wearable devices to monitor biological and biokinematic aspects of human beings. Although such telerehabilitation systems utilise cloud computing features and provide automatic biofeedback and performance evaluation, there are demands for overall optimisation to enable these systems to operate with low battery consumption and low computational power and even with weak or no network connections. This paper proposes a novel multilevel data encoding scheme satisfying these requirements in mobile cloud computing applications, particularly in the field of telerehabilitation. We introduce architecture for telerehabilitation platform utilising the proposed encoding scheme integrated with various types of sensors. The platform is usable not only for patients to experience telerehabilitation services but also for therapists to acquire essential support from analysis oriented decision support system (AODSS) for more thorough analysis and making further decisions on treatment.

Computer-aided design decision support: interfacing knowledge and information

Computer-aided design decision support has proved to be an elusive and intangible project for many researchers as they seek to encapsulate information and knowledge-based systems as useful multifunctional data structures. Definitions of ‘knowledge', ‘information', ‘facts', and ‘data' become semantic footballs in the struggle to identify what designers actually do, and what level of support would suit them best, and how that support might be offered. The Construction Primer is a database-drivable interactive multimedia environment that provides readily updated access to many levels of information aimed to suit students and practitioners alike. This is hardly a novelty in itself. The innovative interface and metadata structures, however, combine with the willingness of national building control legislators, standards authorities, materials producers, building research organisations, and specification services to make the Construction Primer a versatile design decision support vehicle. It is both compatible with most working methodologies while remaining reasonably future-proof. This paper describes the structure of the project and highlights the importance of sound planning and strict adhesion to library-standard metadata protocols as a means to avoid the support becoming too specific or too paradigmatic.

Work teams : perceptions of a ready-made support system?

Much research on work teams has been focused at the team-level, considering such issues as effectiveness, productivity, and overall interaction. Using qualitative in-depth interviews, the author has asked the question: what is the experience of the individual working within a team? This paper discusses one theme to have emerged, that of perceptions of support being provided within the team. Respondents' accounts presented an expectation that support would be forthcoming from other team members. The discussion considers the experiences of respondents both when this support was received and when it was not, prompting a reconsideration of our understanding of work teams. Further discussion shows how the expectation and provision of this support has implications for how individuals view both teams and organizational work in general, and how consideration of this issue can assist managers in the renewal of employees' energy and well-being.

A decision support tool for resource allocation in batch manufacturing

A decision support tool for production planning is discussed in this paper to perform the job of machine grouping and labour allocation within a machining line. The production plans within the industrial partner have been historically inefficient because the relationship between the cycle times, the machine group size, and the operator's utilisation hasn't been properly understood. Starting with a simulation model, a rule-base has been generated to predict the operator's utilisation for a range of production settings. The resource allocation problem is then solved by breaking the problem into a series of smaller sized tasks. The objective is to minimise the number of operators and the difference between the maximum and minimum cycle times of machines within each group. The results from this decision support tool is presented for the particular case study.

Work teams: perceptions of a ready-made support system?

Much research on work teams has been focused at the team-Ievel,  considering such issues as effectiveness, productivity and overall  interaction. Using qualitative in-depth interviews, the author has asked the question: what is the experience of the individual working within a team? This paper discusses one theme to have emerged, that of perceived emotional support being provided within the team. Respondents' descriptions of emotional support are discussed in terms of acceptance and respect, and of caring. The discussion shows how the provision of this support has implications for how individuals view teams in general, and indicates areas for future research.

Toward a synergy between simulation and knowledge management for business intelligence

The adoption of simulation as a powerful enabling method for knowledge management is hampered by the relatively high cost of model construction and maintenance. A two-step procedure, based on a divide and conquer strategy, is proposed in this paper. First, a simulation program is partitioned based on a reinterpretation of the model-view-controller architecture. Individual parts are then connected, in terms of abstraction, to guard against possible changes that resulted from shifting user requirements. We explore the applicability of these design principles through a detailed discussion of an industry case study. The knowledge-based perspective guides the design of architecture to accommodate the need of emulation without compromising the integrity of the simulation program. The synergy between simulation and a knowledge management perspective, as shown in the case study, has the potential to achieve the objectives of rapid development of models, with low maintenance cost. This could, in turn, facilitate an extension of the use of simulation in the knowledge management domain.

Decision support tool for production planning of a machining line

A decision support tool for production planning was developed to perform the difficult and time consuming task of allocating resources within the industrial partner's machining line, consisting of identical Computerized Numerically Controlled machines. The production-planning tool identified significant labour savings in a number of the industrial partner's production plans.