International Journal of Information Technology & Decision Making

Our research focuses on the use of local context through relation matching to improve retrieval effectiveness. An information retrieval (IR) model that integrates relation and keyword matching has been used in this work. The model takes advantage of any existing relational similarity between documents and query to improve retrieval effectiveness. It gives high rank to a document in which the query concepts are involved in similar relationships as in the query, as compared to those in which they are related differently. A conceptual graph (CG) representation has been used to capture relationship between concepts. A simplified form of graph matching has been used to keep our model computationally tractable. Structural variations have been captured during matching through simple heuristics. Four different CG similarity measures have been proposed and used to evaluate performance of our model. We observed a maximum improvement of 7.37% in precision with the second CG similarity measure. The document collection used in this study is CACM-3204. CG similarity measure proposed by us is simple, flexible and scalable and can find application in many IR related tasks like information filtering, information extraction, question answering, document summarization, etc.

Conventional approaches to text analysis and information retrieval which measured document similarity by considering all information in texts are relatively inefficiency for processing large text collections in heterogeneous subject areas. Previous researches showed that evidence from passage can improve retrieval results. But it also raised questions about how passage is defined, how they can be ranked efficiently, and what is their proper rule in long structure documents. Moreover, the frequency of "the" with important sentence is efficiently to summarize the text by dexterity way. We previously proposed an approach for extracting sentences which including article "the" by some restrict rules to carry out effectiveness passages. Based on previous approaches, this paper presents a new Passage SIMilarity (P-SIM) measurements between documents based on effectiveness passages after extracting them using article "the". Moreover, our new approach showing that this method is more efficient than traditional methods. Also, Recall and Precision are achieved by 92.6% and 97.5% respectively, depending on extracted passages. Furthermore, Recall and Precision significantly improved by 38.3% and 44.2% over the traditional method. The proposed methods are applied to 3,990 articles from the large tagged corpus.

This paper describes a web-based Decision Agent (DA) that provides a customer-oriented approach for selecting products in online orders. The proposed approach is focused on enhancing the front-end of company's business portal by segmenting prospective online customers and directing them to use decision modules that improve user perception in terms of effort, accuracy, and acceptance. The user's Consistency Ratio (CR) in evaluating the attributes of online products is used to categorize prospected users and to direct them to proceed with one of three decision modules, Analytical Hierarchy Process (AHP), Simple Multi-Attribute Rating Technique (SMART), and Direct Scaling Method (DSM). The proposed approach is applied to an online cellular phone selection case study. Experimental results showed the fit of assigned decision modules to customer classes where users made smart phone selections with less effort and higher reported convenience and satisfaction. The approach can be tailored for other online products and can be integrated into the websites of producers and online stores in a wide range of business applications.

The continuous-valued Hopfield neural network (CHN) is a popular and powerful metaheuristic method for combinatorial optimization. However, it is difficult to select appropriate penalty parameters for constraints so as to obtain a feasible and desirable solution by CHN. Thus, various improved models have been proposed. Matsuda proposed a CHN named optimal CHN and showed theoretical results on selecting parameters. On the other hand, Smith et al. proposed the projection CHN which projects a solution onto the feasible region and thus needs not select penalty parameters.

In this paper, we point out some drawbacks of these two models and propose a new CHN with an efficient projection technique for the quadratic assignment problem, which overcomes these drawbacks. Moreover, we show that the proposed model can always find a feasible solution and that it has the local convergence property. Finally, we verify advantages of the proposed model through some numerical experiments.

The notion of forms as a way of organizing and presenting data has been used since the beginning of the World Wide Web. Web-based forms have evolved together with the development of new markup languages, in which it is possible to provide validation scripts as part of the form code to test whether the intended meaning of the form is correct. However, for the form designer, part of this intended meaning frequently involves other features which are not constraints by themselves, but rather attributes emerging from the form, which provide plausible conclusions in the context of incomplete and potentially inconsistent information. As the value of such attributes may change in presence of new knowledge, we call them defeasible attributes. In this paper, we propose extending traditional web-based forms to incorporate defeasible attributes as part of the knowledge that can be encoded by the form designer. The proposed extension allows the specification of scripts for reasoning about form fields using a defeasible knowledge base, expressed in terms of a Defeasible Logic Program.

In this paper, we first discuss the space frame of multidimensional dynamic credit evaluation (MDCE) theoretically, and then propose the conception of identical credit status and pseudo-identical credit status. Taking advantage of space conformational analysis and optimization techniques, a geometrical method is also founded. By using our method, the credit qualities of all evaluated objects can be ascribed to a suitable credit rating.

The wide variety of disasters and the large number of activities involved have resulted in the demand for separate Decision Support System (DSS) models to manage different requirements. The modular approach to model management is to provide a framework in which to focus multidisciplinary research and model integration. A broader view of our approach is to provide the flexibility to organize and adapt a tailored DSS model (or existing modular subroutines) according to the dynamic needs of a disaster. For this purpose, the existing modular subroutines of DSS models are selected and integrated to produce a dynamic integrated model focussed on a given disaster scenario. In order to facilitate the effective integration of these subroutines, it is necessary to select the appropriate modular subroutine beforehand. Therefore, subroutine selection is an important preliminary step towards model integration in developing Disaster Management Decision Support Systems (DMDSS). The ability to identify a modular subroutine for a problem is an important feature before performing model integration. Generally, decision support needs are combined, and encapsulate different requirements of decision-making in the disaster management area. Categorization of decision support needs can provide the basis for such model selection to facilitate effective and efficient decision-making in disaster management. Therefore, our focus in this paper is on developing a methodology to help identify subroutines from existing DSS models developed for disaster management on the basis of needs categorization. The problem of the formulation and execution of such modular subroutines are not addressed here. Since the focus is on the selection of the modular subroutines from the existing DMDSS models on basis of a proposed needs classification scheme.

We studied the process of production scheduling in a large chemical plant. Scheduling in that environment is inherently a group process because multiple experts are needed to construct a schedule and to manage its execution. A mathematical formulation of the production scheduling problem yields a mixed-integer linear programming model too large to solve in a reasonable time with current technology. We therefore use an intelligent decision support system (DSS) to heuristically find a satisficing solution to the production scheduling problem. Our DSS is based on a model of the collaborative nature of the task, and it focuses on the communication, argumentation, and reconciliation strategies undertaken by individuals. Using actual production schedules, we show that our DSS can lead to measurable improvements over humanly-designed plans, where the quality of the schedule is measured using the objective function of the mathematical formulation of the problem.

We deal with the problem of priority elicitation in the analytic hierarchy process (AHP) on the basis of imprecise pair-wise comparison judgements on decision elements. We propose a min–max goal programming formulation to derive the AHP priorities in the case that the decision maker provides preference judgements in the form of interval numbers. By applying variable transformations, we formulate a linear programming model that is capable of estimating the priorities from both consistent and inconsistent interval judgements. The proposed method is illustrated by numerical examples.

A collaborative planning framework combining the Lagrangian Relaxation method and Genetic Algorithms is developed to coordinate and optimize the production planning of the independent partners linked by material flows in multiple tier supply chains. Linking constraints and dependent demand constraints were added to the monolithic Multi-Level, multi-item Capacitated Lot Sizing Problem (MLCLSP) for supply chains. Model MLCLSP was Lagrangian relaxed and decomposed into facility-separable sub-problems based on the separability of it. Genetic Algorithms was incorporated into Lagrangian Relaxation method to update Lagrangian multipliers, which coordinated decentralized decisions of the facilities in supply chains. Production planning of independent partners could be appropriately coordinated and optimized by this framework without intruding their decision authorities and private information. This collaborative planning schema was applied to a large set problem in supply chain production planning. Experimental results show that the proposed coordination mechanism and procedure come close to optimal results as obtained by central coordination in terms of both performance and robustness.