International Journal of Information Technology & Decision Making

Corporate competence sets can be expanded through capital investment and be dynamically changed overtime, which can explain the phenomenon of "taking loss at the ordering time and making profit at the time of delivery". Such phenomenon has existed in practice for a long time, but there are no mathematical model that can explain it adequately. This paper utilizes multiple criteria and multiple constraint levels linear programming (MC²LP) model and its extended techniques to explore the linear programming models with changeable parameters. The parameters include: unit profit, available resources, and input-output coefficients of production function. With those parameters changed with capital investment and/or time, we study how to find dynamic best solutions to make "taking loss at the ordering time and making profit at the time of delivery" feasible. For more general cases we also sketch a generalized mathematical programming model with changeable parameters and control variables.

The questions of the formalization of the estimates of the phenomenon state, which can be used in the decision-making multicriterion problems [Vector-Valued Optimization Problems in Control Theory (Academic Press, New York, 1979)] taking into account the metrics of the space of partial criteria are considered. Any phenomenon is described by some system of characteristic parameters, which values are presented by the coordinates of points in the space of states of the given phenomenon. A notion of the "ideal" state determined by optimal values of each characteristic parameter separately and independently of others is used. Estimate of any state is determined by the distance between that state and the "ideal" one. That permits to choose from a multitude of the given states the best one, to which conforms a minimum distance to the "ideal" state. In the general case of the curved space the distances are measured by geodesic lines drawn by using a metric tensor of the given space. The metric tensor components themselves are determined by the solution of the differential equations set up from the condition of minimization of a certain functional.

The need to cope with complicated natural disaster system calls for a sophisticated way of analyzing it with the help of fuzzy methods. Therefore, four models are suggested succeedingly to represent a fuzzy probability distribution with a small sample. In this paper, we inspect the four models in detail and evaluate their performance on an emulation test.

The main objective of the paper is to investigate the risk analysis problems when a precise but unknown probability distribution of states of nature belongs to a set of continuous probability distributions restricted by some known lower and upper distributions and when utility functions are nonmonotone. Methods for choosing "optimal" distributions among the set of distributions and for computing the expected utilities are proposed. Some special cases of sets of distributions, including possibility distributions, step functions, belief functions are studied under the same conditions. Various numerical examples illustrate the proposed methods.

This paper introduces a selection approach that conveys to management where to focus organizational resources to gain the maximum benefit of converting to e-business, by making an educated decision regarding which business processes should be e-enabled (converted to electronic delivery) first. The selection model is based on criteria developed using two proposed axioms: the benefit axiom and the effort axiom. The benefit axiom addresses the company's needs in terms of expected gains manifested in cost saving and/or time savings; while the effort axiom addresses the technical point of view of e-enablement in terms of the amount of effort needed to e-enable business processes. The two axioms are incorporated to construct a multi-criteria selection model using the Analytic Hierarchy Process.

In many applications such as credit risk management, data are represented as high-dimensional feature vectors. It makes the feature selection necessary to reduce the computational complexity, improve the generalization ability and the interpretability. In this paper, we present a novel feature selection method — "Least Squares Support Feature Machine" (LS-SFM). The proposed method has two advantages comparing with conventional Support Vector Machine (SVM) and LS-SVM. First, the convex combinations of basic kernels are used as the kernel and each basic kernel makes use of a single feature. It transforms the feature selection problem that cannot be solved in the context of SVM to an ordinary multiple-parameter learning problem. Second, all parameters are learned by a two stage iterative algorithm. A 1-norm based regularized cost function is used to enforce sparseness of the feature parameters. The "support features" refer to the respective features with nonzero feature parameters. Experimental study on some of the UCI datasets and a commercial credit card dataset demonstrates the effectiveness and efficiency of the proposed approach.

This study proposes a multi-objective decision approach for evaluating the enterprise computing solutions in international tourism. We first analyze one tour operator in Thailand as well as its specific requirements for enterprise computations. In the decision processes for enterprise computation, several experts are invited to evaluate the alternatives based on a multi-tier electronic business architecture. This study designs two models for evaluating the information systems investments in international tourism. The first model maximizes the overall utility for each criterion, and the other model maximizes the utility for every expert. Because each alternative for enterprise computation involves approximate costs and efforts, we represent the cost data with fuzzy sets and possibility distributions.

Design patterns have become an important part of modern object-oriented design. It is necessary to evaluate and compare the effectiveness of different methods of pedagogical documentation in teaching design patterns. We compared patterns-style documentation with minimalist documentation. Patterns style disagrees with minimalism in that contextual information is not minimal — it usually contains information other than what needs to be done. The discoveries in this empirical study were two fold. First, the MANOVA (multivariate analyses of variance) shows that the effect of patterns-style documentation and minimalism are different. Second, we proposed a prediction model using a regression modeling technique. Considering these results, we formulate some guidelines for effective framework documentation by applying five of the design patterns within the Swing environment as our case study. Different documentation philosophies are better for different goals. For a simple task, use minimalist documentation. For a much more complex problem such involving five of the design patterns, our empirical results suggest using patterns-style documentation.

Effectively selling products online is a challenging task. Today's product domains often contain a dizzying variety of brands and models with highly complex sets of characteristics. This paper addresses the problem of supporting product search and selection in domains containing large numbers of alternatives with complex sets of features. A number of online shopping websites provide product choice assistance by making direct use of Multi-Attribute Utility Theory (MAUT). While the MAUT approach is appealing due to its solid theoretical foundations, there are several reasons that it does not fit well with people's decision making behavior.

This paper presents an approach designed to better fit with people's natural decision making process. The system is called VMAP for Visualizing Multi-Attribute Preferences. VMAP provides on one screen both a multi-attribute preference tool (MAP-tool) and a product visualization tool (V-tool). The product visualization tool displays the set of available products, with each product displayed as a point in a 3D attribute space. By viewing the product space, users can gain an overview of the range of available products, as well as an understanding of the relationships between their attributes. The MAP-tool integrates expression of preferences and filter conditions, which are then immediately reflected in the V-tool display. In this way, the user can immediately see the consequences of his expressed preferences on the product space.

The VMAP system is evaluated on a number of factors by comparing users' subjective ratings of the system to those of a more traditional MAUT product selection tool. The results show that while VMAP is somewhat more difficult to use than a traditional MAUT product selection tool, it provides better flexibility, provides the ability to more effectively explore the product domain, and produces more confidence in the selected product.

IT business value research examines the organizational performance impacts of information technology. In this paper, we apply the value configuration of the value shop to describe and measure organizational performance. The value shop consists of the five primary activities of problem understanding, solutions to problems, decisions on actions, implementation of actions, and evaluations of actions in an iterative problem-solving cycle. Police investigation work is defined as value shop activities. Our empirical study of Norwegian police results in significant relationships between information technology use and investigation performance for all primary activities. The most important primary activities for IT use are problem understanding and implementation of actions, as both significantly improve value shop performance.