International Journal of Information Technology & Decision Making

Intellectual property (IP) is an exponentially consequential driver of competitive advantage nowadays. We bring in the technical views in the use of SIP technology as a more strategic weapon to elaborate our proposed strategies — design for manufacturability (DFM) adder, and home depot strategies through transaction cost approach that are able to be practically applying in the semiconductor foundry industry with added value by all means. DFM adder strategy is an effective solution specifying wafer processing enhancement while home depot strategy is an entire solution massing on product design integration. More substantially, these strategies are totally awesome mechanisms that can be matching and winning in the evolving marketplace for foundry industry in the 21st century.

In this paper, we test the applicability of a decision support system (DSS) that is developed to optimize the sequence of surgeries in the day-care center of the UZ Leuven Campus Gasthuisberg (Belgium). We introduce a multi-objective function in which children and prioritized patients are scheduled as early as possible on the day of surgery, recovery overtime is minimized, and recovery workload is leveled throughout the day. This combinatorial optimization problem is solved by applying a pre-processed mixed integer linear programming model. We report on a 10-day case study to illustrate the performance of the DSS. In particular, we compare the schedules provided by the hospital with those that are suggested by the DSS. The results indicate that the DSS leads to both an increased probability of obtaining feasible schedules and an improved quality of the schedules in terms of the objective function value. We further highlight some of the major advantages of the application, such as its visualization and algorithmic performance, and also report on the difficulties that were encountered during the study and the shortcomings that currently delay its implementation in practice, as this information may contribute to the success rate of future software applications in hospitals.

The prosperity of emerging technology may be triggered by the occurrence of rare events, and once emerging technology booms, there may be an enormous market demand for it. This paper explores the firm's commercial and R&D investment decisions on emerging technology in real options framework. We think that, for the fear that a rare event which occurs prior to R&D success would trigger the emerging technology boom, the firm should carry out prospective R&D in the emerging technology innovation. So the impacts of the R&D hazard rate and the intensity of rare event arrival on the prospective R&D investment strategy are mainly addressed. There are two results. First, the firm is most likely to take the strategy at low, rather than high, probability of technological boom in market-driven innovation. Second, in technology-driven innovation, the huge potential profit of emerging technology in booming period can arouse more attention toward the firm. When the growth rate of profit flow in booming period rises to some degree, the firm may take this strategy unconditionally.

Since in the feature space the eigenvector is a linear combination of all the samples from the training sample set, the computational efficiency of KPCA-based feature extraction falls as the training sample set grows. In this paper, we propose a novel KPCA-based feature extraction method that assumes that an eigenvector can be expressed approximately as a linear combination of a subset of the training sample set ("nodes"). The new method selects maximally dissimilar samples as nodes. This allows the eigenvector to contain the maximum amount of information of the training sample set. By using the distance metric of training samples in the feature space to evaluate their dissimilarity, we devised a very simple and quite efficient algorithm to identify the nodes and to produce the sparse KPCA. The experimental result shows that the proposed method also obtains a high classification accuracy.

Most of the real-world multicriteria decision-making (MCDM) problems contain a mixture of quantitative and qualitative criteria; therefore quantitative MCDM methods are inadequate for handling this type of decision problems. In this paper, a MCDM method based on the Fuzzy Sets Theory and on the Analytic Hierarchy Process (AHP) is proposed. This method incorporates a number of perspectives on how to approach the fuzzy MCDM problem, as follows: (1) combining quantitative and qualitative criteria; (2) expressing criteria pair-wise comparison in linguistic terms and performance of the alternative on each criterion in linguistic terms or exact values when criterion is qualitative or quantitative, respectively; (3) converting all the assessments into trapezoidal fuzzy numbers; (4) using the difference minimization method to calculate the local weight of criteria, employing the algebraic operations of fuzzy numbers based on the concept of α-cuts; (5) calculating the global weight of criteria and the global performance of each alternative using geometric mean and the weighted sum, respectively; (6) using the centroid method to rank the alternatives. Finally, an illustrative example on evaluation of several combined cooling, heat, and power production systems is used to demonstrate the effectiveness of the proposed methodology.

To manipulate semantic web and integrate different data sources efficiently, automatic schema matching plays a key role. A generic schema matching method generally includes two phases: the linguistic similarity matching phase and the structural similarity matching phase. Since linguistic matching is an essential step for effective schema matching, developing a high accurate linguistic similarity matching scheme is required. In this paper, a schema matching approach called Similarity Yield Matcher (SYM) is proposed. In SYM, a lexical decision tree is presented to determine the linguistic similarity matching of the first phase. A structural matching algorithm is then proposed to find the structure similarity between two tree schemas. The proposed schema matching approach was evaluated by testing on several benchmarks of real schemas and comparing with other methods. The experimental results show that the proposed lexical decision tree substantially improves the linguistic similarity matching effectively and efficiently. The proposed SYM algorithm also performs high effectiveness on 1–1 schema matching.

One of the most popular approaches to Collaborative Filtering is based on Matrix Factorization (MF). In this paper, we focus on improving MF-based recommender's accuracy by homogeneous ensemble methods. To build such ensembles, we investigate a series of methods primarily in two aspects: (i) manipulating the training examples, including bagging, AdaBoost, and Forward Stepwise Additive Regression; (ii) injecting randomness to the base models' training settings, including randomizing the initializing parameters and randomizing the training sequences. Each method is evaluated on two large, real datasets, and then the effective methods are combined to form a cascade MF ensemble scheme. The validation results on experiment datasets demonstrate that compared to a single MF-based recommender, our ensemble scheme could obtain a significant improvement in the prediction accuracy.

Using the method of agent-based computational finance, this paper designs ten experiments to examine the impacts of the index futures market, typical investment strategies, and different trading mechanisms on the volatility of the Chinese stock market, taking into account the behavior of investors. We have the following results. First, the volatility of the stock market decreases with the index future market and cross-market arbitrageurs. Second, different investment strategies have different effects on stock market volatility. In many cases, both market-imitating and stop-loss strategies can increase stock market volatility. Third, the mechanism of price limits for the index futures market can help to stabilize the fluctuation of the stock market.