Using interactive multiobjective methods to solve DEA problems with value judgements

Data envelopment analysis (DEA) is a performance measurement tool that was initially developed without consideration of the decision maker (DM)'s preference structures. Ever since, there has been a wide literature incorporating DEA with value judgements such as the goal and target setting models. However, most of these models require prior judgements on target or weight setting. This paper will establish an equivalence model between DEA and multiple objective linear programming (MOLP) and show how a DEA problem can be solved interactively without any prior judgements by transforming it into an MOLP formulation. Various interactive multiobjective models would be used to solve DEA problems with the aid of PROMOIN, an interactive multiobjective programming software tool. The DM can then search along the efficient frontier to locate the most preferred solution where resource allocation and target levels based on the DM's value judgements can be set. An application on the efficiency analysis of retail banks in the UK is examined. Comparisons of the results among the interactive MOLP methods are investigated and recommendations on which method may best fit the data set and the DM's preferences will be made.     

Qualitative findings of students’ perception on practice of self-regulated strategies in online community discussion

With rapid emergence to the birth of the knowledge era, an active and interactive learning environment is fundamental to any teaching and learning process. Over time, negative implications of traditional learning environment are mounting, and measures must be taken to challenge it. With extensive research on available learning tools, it has become more feasible to provide our students with better learning opportunities. Such was the anticipation in the Malaysian educational context. Hence, this paper aims to address qualitative findings of a study on self-regulated learning strategies conveyed through the proposed interactive e-learning community (iELC). In duration of eight weeks, students (n = 50) from regular national secondary schools were subjected to online learning tasks aimed to advance strategies on self-regulated learning. To date, strategies of self-regulated learning have been perceived as key to learner to succeed academically and after the schooling years. By means of semi-structured interview, data were collected from small group and one-to-one perception. Results were directed at the overall development of self-regulated learning strategies and its diffusion in the iELC discussion platform. Conclusively, these findings add significantly to the understanding of self-regulated learning strategies in an online learning environment.     

Institutional pressures and mindful IT management: The case of a container terminal in China

Although it has been only ten years in operations, Yantian International Container Terminals Limited (YICT) has evolved as a world-class container terminal and become the first Chinese port to receive the Best Global Container Port of the Year award for 2005–2006, as well as being designated China's best container terminal in terms of operational efficiency for 2008. A success factor of YICT is its extensive use of IT in managing and enhancing the productivity and services of the port operations. Premised upon Institutional Theory and the organizational mindfulness perspectives (preoccupation with failure and success, reluctance to simplify interpretations, sensitivity to operations, commitment to resilience, and reliance on expertise over formal authority) and using case study methodology, we examined the institutional pressures faced by YICT in its IT management (ITM), and determined how YICT coped with its institutional pressures during its IT development.     

Recognition and analysis of cell nuclear phases for high-content screening based on morphological features

Automated analysis of molecular images has increasingly become an important research in computational life science. In this paper some new and efficient algorithms for recognizing and analyzing cell phases of high-content screening are presented. The conceptual frameworks are based on the morphological features of cell nuclei. The useful preprocessing includes: smooth following and linearization; extraction of morphological structural points; shape recognition based morphological structure; issue of touching cells for cell separation and reconstruction. Furthermore, the novel detecting and analyzing strategies of feed-forward and feed-back of cell phases are proposed based on gray feature, cell shape, geometrical features and difference information of corresponding neighbor frames. Experiment results tested the efficiency of the new method.     

Dual wakeup design for wireless sensor networks

Energy is often identified as the single most important resource in wireless battery-powered sensor networks. While current wakeup schemes in the literature promise to conserve energy in such networks, they apply several assumptions that may not be always true. First, all applications are assumed to require sensing coverage and network connectivity continuously; Second, a random dense deployment of sensors is always assumed possible; And third, the sensing ranges can be easily modeled by some sensing circles. In this paper, we show that these assumptions are not always valid, and propose sensor node wakeup schemes based on combinatorics block design to address energy-related issues when common assumptions fail. Another distinguishing feature of our work is also the proposal of a dual wakeup design for sensing and communications as these are two very different tasks. Finally, we verified our proposed schemes with simulations and experiments.     

Instructional animations can be superior to statics when learning human motor skills

Based on the assumption of a working memory processor devoted to human movement, cognitive load theory is used to explore some conditions under which animated instructions are hypothesised to be more effective for learning than equivalent static graphics. Using paper-folding tasks dealing with human movement, results from three experiments confirmed our hypothesis, indicating a superiority of animation over static graphics. These results are discussed in terms of a working memory processor that may be facilitated by our mirror-neuron system and may explain why animated instructional animations are superior to static graphics for cognitively based tasks that involve human movement.     

Immunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioning

The development of evolutionary algorithms for optimization has always been a stimulating and growing research area with an increasing demand in using them to solve complex industrial optimization problems. A novel immunity-based hybrid evolutionary algorithm known as Hybrid Artificial Immune Systems (HAIS) for solving both unconstrained and constrained multi-objective optimization problems is developed in this research. The algorithm adopts the clonal selection and immune suppression theories, with a sorting scheme featuring uniform crossover, multi-point mutation, non-dominance and crowding distance sorting to attain the Pareto optimal front in an efficient manner. The proposed algorithm was verified with nine benchmarking functions on its global optimal search ability as well as compared with four optimization algorithms to assess its diversity and spread. Sensitivity analysis was also carried out to investigate the selection of key parameters of the algorithm. It is found that the developed immunity-based hybrid evolutionary algorithm provides a useful means for solving optimization problems and has successfully applied to the problem of global repositioning of containers, which is one of a constrained multi-objective optimization problem. The developed HAIS will assist shipping liners on timely decision making and planning of container repositioning operations in global container transportation business in an optimized and cost effective manner.     

A local experts organization model with application to face emotion recognition

This paper presents a novel approach for recognizing human facial emotion in order to further detect human suspicious behaviors. Instead of relying on relative poor representation of facial features in a flat vector form, the approach utilizes a format of tree structures with Gabor feature representations to present a facial emotional state. The novel local experts organization (LEO) model is proposed for the processing of this tree structure representation. The motivation for the LEO model is to deal with the inconsistent length of features in case there are some features failed to be detected. The proposed LEO model is inspired by the natural hierarchical model presented in natural organization, where workers (local experts) reports to their supervisor (fusion classifier), whom in turn reports to upper management (global fusion classifier). Moreover, an Asian emotion database is created. The database contains high-resolution images of 153 Asian subjects in six basic pseudo-emotions (excluding neutral expression) in three different poses for evaluating our proposed system. Empirical studies were conducted to benchmark our approach with other well-known classifiers applying to the system, and the results showed that our approach is the most robust, and less affected by noise from feature locators for the face emotion recognition system.     

Identification of tool and product effects in a mixed product and parallel tool environment

In the semiconductor manufacturing industry, production resembles an automated assembly line in which many similar products with slightly different specifications are manufactured step-by-step, with each step being a complicated physiochemical batch process performed by a number of tools. This constitutes a high-mix production system for which effective run-to-run control (RtR) and fault detection control (FDC) can be carried out only if the states of different tools and different products can be estimated. However, since in each production run, a specific product is performed on a specific tool, absolute individual states of products and tools are not observable. In this work, a novel state estimation method based on analysis of variance (ANOVA) is developed to estimate the relative states of each product and tool to the grand average performance of this station in the fab. The method is formulated in the form of a recursive state estimation using the Kalman filter. The advantages of this method are demonstrated using simulations to show that the correct relative states can be estimated in production scenarios such as tool-shift, tool-drift, product ramp-up, tool/product-offline and preventive maintenance (PM). Furthermore, application of this state estimation method in RtR control scheme shows that substantial improvements in process capabilities can be gained, especially for products with small lot counts. The proposed algorithm is also evaluated by an industrial application.