International Journal of Information Technology & Decision Making

We have presented an approach to automatic document summarization. In the proposed approach, text summarization is modeled as a quadratic integer-programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. To solve the optimization problem we have created a novel differential evolution algorithm. Experimental results on DUC2005 and DUC2007 data sets showed that the proposed approach outperforms the other methods.

Wrong decisions or inappropriate selection of equipment may lead to increase in cost and reduction in efficiency and effectiveness. Selecting right equipment has always been a key factor in the success of the process it is used for. In this study, superiority and inferiority ranking (SIR) methodis utilized for evaluation of most suitable offer for procurement of equipment installed inside a facility, whereas, analytical hierarchy process (AHP) is used to calculate the weights of factors that influence procurement decision. To achieve this target, a methodological framework of a series of interviews are conducted, then two questionnaire surveys are developed for identifying the important factors affecting the selection process of equipment and determining their relative importance. A solution of the problem is then designed in a model using AHP and SIR methods in addition to using the simple additive weighting (SAW) and technique for order preference by similarity to the ideal solution (TOPSIS) procedures to generate the superiority and inferiority flows. The model is generic and flexible and is used for the application of the multiple criteria decision making (MCDM) methods in the procurement process. The model also offers an efficient and convenient tool that aids its users to act in an orderly and methodical thinking, and guides them in making logical and robust decisions. A case study is presented to demonstrate the use of the developed model and sensitivity analysis is carried out to measure the robustness of the model in different scenarios.

Based on Jacquet-Lagreze's permutation method, QUALIFLEX is an outranking model that investigates all possible permutations of alternatives with respect to the consequences of all criteria. The purpose of this paper is to develop a QUALIFLEX-based method for multiple criteria group decision making within a decision environment of interval-valued intuitionistic fuzzy sets. We conduct a statistical inference approach with finite population correction to construct interval-valued intuitionistic fuzzy numbers. In addition, we incorporate the relative importance of decision makers and fuse individual opinions to form collective ratings using a modified method with weighted interval estimations. In view of diversiform preference types (weak order, strict order, difference order, interval bound, and ratio bound), we represent multiple decision makers' various forms of preference structures and assess criterion weights under incomplete information. By means of score functions, accuracy functions, membership-uncertainty indices, and hesitation-uncertainty indices, a ranking procedure is employed to identify a criterion-wise preference of alternatives. A QUALIFLEX-based model is then established to measure the level of concordance of the complete preference order for handling multiple criteria group decisions. The feasibility of the proposed method is illustrated by a practical problem relating to the selection of a landfill site. As indicated in the application, the proposed method is useful for handling complicated group decision-making problems that involve comprehensive criteria and limited alternatives.

This paper proposes a new approach that uses people's social interaction behavior collected by mobile phones and vital signs collected by wireless body area networks (WBAN) for epidemic control. By this approach, infectious people who are socially active can be quickly identified to be quarantined. To realize this approach, we introduce a notion of critical network and critical node identification algorithm. Observing some resource constraints such as quarantine cost and hardware limitation, we focus on optimizing the proposed approach such that high epidemic control effectiveness is achieved while the corresponding overhead is minimized. Our simulation results demonstrate that our approach can effectively control the spread of epidemic diseases in various situations.

In this paper, an integrated data characteristic testing scheme is proposed for complex time series data exploration so as to select the most appropriate research methodology for complex time series modeling. Based on relationships across different data characteristics, data characteristics of time series data are divided into two main categories: nature characteristics and pattern characteristics in this paper. Accordingly, two relevant tasks, nature determination and pattern measurement, are involved in the proposed testing scheme. In nature determination, dynamics system generating the time series data is analyzed via nonstationarity, nonlinearity and complexity tests. In pattern measurement, the characteristics of cyclicity (and seasonality), mutability (or saltation) and randomicity (or noise pattern) are measured in terms of pattern importance. For illustration purpose, four main Chinese economic time series data are used as testing targets, and the data characteristics hidden in these time series data are thoroughly explored by using the proposed integrated testing scheme. Empirical results reveal that the natures of all sample data demonstrate complexity in the phase of nature determination, and in the meantime the main pattern of each time series is captured based on the pattern importance, indicating that the proposed scheme can be used as an effective data characteristic testing tool for complex time series data exploration from a comprehensive perspective.

A knowledge discovery model has been developed to manage the facts discovered in criminal cases in the court of law and to identify the relevant diagnostic issues. This study focuses on the offence against life and body section of the criminal law codes of Thailand. To identify the criminal case diagnostic issues, a set of artificial neural networks (ANN) classifiers is heuristically configured and modularly organized to operate upon the discovered facts. This modular network of ANNs forms an effective system in terms of determining power and ability to trace or infer the relevant reasoning of such a determination. Experiments have been conducted to demonstrate the applicability of ANN for various case studies and to generate comparative results for providing insights into both technical and legal aspects of these cases. In this study, a modular ANN with the support of Principal Component Analysis (PCA) as an automatic input selection mechanism provided the best results with accuracy up to 99%, using 10-fold cross-validation. A sample case is included to illustrate the effectiveness of the proposed system.

Recent research on Software Architecture has recovered its original emphasis on keeping track of design decisions and their rationales during software development, compiling them under the name of architectural knowledge (AK). This knowledge is composed of decision assets, which relate to each other creating a decision network structure. We argue that relationships in these networks of AK contain valuable information, in particular when they describe negative semantics. We use antipatterns to exploit and manage these negative relationships systematically. After examining and classifying the kinds of AK relationships, we describe a method that enriches this network by means of antipattern structures. To show the feasibility and suitability of this approach, we provide a proof-of-concept by applying it to an existing process, ATRIUM, with a concrete example, the Gas Station metaphor. Results of the use of the presented approach into three different projects with different complexities show both its feasibility and applicability.

High-rise buildings with a considerable number of elevators represent a major logistic problem concerning saving space and time due to economic reasons. For this reason, complex Elevator Group Control Systems are developed in order to manage the elevators properly. Furthermore, the subject of energy is acquiring more and more industrial relevance every day as far as sustainable development is concerned. In this paper, the first entirely dynamic Fuzzy Logic Elevator Group Control System to dispatch landing calls so as to minimize energy consumption, especially during interfloor traffic, is proposed. The fuzzy logic design described here constitutes not only an innovative solution that outperforms usual dispatchers but also an easy, cheap, feasible and reliable solution, which is possible to be implemented in real industry controllers.

Behavioral decision making is an area of multidisciplinary research attracting growing interest of scientists and practitioners, economists, and business people. A wide spectrum of successful theories is present now, including Prospect theory, multiple priors models, studies on altruism, trust and fairness. However, these theories are developed for precise and complete information, whereas real information concerning a decision maker's (DM) behavior and environment is imperfect, qualitative, and, as a result, often described in natural language (NL). We suggest an approach based on modeling a DM's behavior by a set of states. Each state represents a certain principal behavior. In our approach, states of nature and DM's states constitute a single space of combined states. For formalizing relevant information described in NL, we use fuzzy set theory. The utility model is based on Choquet-like integration over combined states. The investigations show that Expected Utility, Choquet Expected Utility and Cumulative Prospect Theory are special cases of the suggested approach. We apply the suggested approach to solving a benchmark and a real-life decision problem. The obtained results show validity of the suggested approach.