International Journal of Information Technology & Decision Making

Structural and parametric identification of nonlinear continuous dynamic systems with a closed cycle on a set of continuous block-oriented models with feedback is considered. The method of structural identification in the steady state based on the observation of the system's input and output variables at the input periodic influences is proposed. The solution of the parameter identification problems, which can be immediately connected with the structural identification problem, is carried out in the steady and transient states by the method of least squares. The structural and parametric identification algorithms are investigated by means of both theoretical analysis and computer modeling.

Providing recommendations based on distributed data has received an increasing amount of attention because it offers several advantages. Online vendors who face problems caused by a limited amount of available data want to offer predictions based on distributed data collaboratively because they can surmount problems such as cold start, limited coverage, and unsatisfactory accuracy through partnerships. It is relatively easy to produce referrals based on distributed data when privacy is not a concern. However, concerns regarding the protection of private data, financial fears due to revealing valuable assets, and legal regulations imposed by various organizations prevent companies from forming collaborations. In this study, we propose to use random projection to protect online vendors' privacy while still providing accurate predictions from distributed data without sacrificing online performance. We utilize random projection to eliminate the aforementioned issues so vendors can work in partnerships. We suggest privacy-preserving schemes to offer recommendations based on vertically or horizontally partitioned data among multiple companies. The recommended methods are analyzed in terms of confidentiality. We also analyze the superfluous loads caused by privacy concerns. Finally, we perform real data-based trials to evaluate the accuracy of the proposed schemes. The results of our analyses show that our methods preserve privacy, cause insignificant overheads, and offer accurate predictions.

In this paper, we combine evolution strategy (ES) with ordinal optimization (OO), abbreviated as ES + OO, to solve real-time combinatorial stochastic simulation optimization problems with huge discrete solution space. The first step of ES + OO is to use an artificial neural network (ANN) to construct a surrogate model to roughly evaluate the objective value of a solution. In the second step, we apply ES assisted by the ANN-based surrogate model to the considered problem to obtain a subset of good enough solutions. In the last step, we use the exact model to evaluate each solution in the good enough subset, and the best one is the final good enough solution. We apply the proposed algorithm to a wafer testing problem, which is formulated as a combinatorial stochastic simulation optimization problem that consists of a huge discrete solution space formed by the vector of threshold values in the testing process. We demonstrate that (a) ES + OO outperforms the combination of genetic algorithm (GA) with OO using extensive simulations in the wafer testing problem, and its computational efficiency is suitable for real-time application, (b) the merit of using OO approach in solving the considered problem and (c) ES + OO can obtain the approximate Pareto optimal solution of the multi-objective function resided in the considered problem. Above all, we propose a systematic procedure to evaluate the performance of ES + OO by providing a quantitative result.

In this paper, the cardinality constrained quadratic model for binary quadratic programming is used to model and solve the graph bisection problem as well as its generalization in the form of the task allocation problem with two processors (2-TAP). Balanced graph bisection is an NP-complete problem which partitions a set of nodes in the graph G = (N, E) into two sets with equal cardinality such that a minimal sum of edge weights exists between the nodes in the two separate sets. 2-TAP is graph bisection with the addition of node preference costs in the objective function. We transform the general linear k-TAP model to the cardinality constrained quadratic binary model so that it may be efficiently solved using tabu search with strategic oscillation. On a set of benchmark graph bisections, we improve the best known solution for several problems. Comparison results with the state-of-the-art graph partitioning program METIS, as well as Cplex and Gurobi are presented on a set of randomly generated graphs. This approach is shown to also work well with 2-TAP, comparing favorably to Cplex and Gurobi, providing better solutions in a much shorter time.

This paper is focused on the use of Knowledge Management techniques to develop environments of Web-Based Education to leverage the individual learning process for learners. Evolving use of Knowledge Management plays an important role to enhance problem solving skills. Recently, innovative approaches for integrating Knowledge Management into practical teaching activities have been ignored. The knowledge is defined as combination and organization of data and information in given context, and Knowledge Management provides acquire, storage, sharing, application and creation of knowledge objects. A new Architecture for the development of adaptive and intelligent Web-Based Education systems is presented. This architecture is based on a Learning Model for Personalized Knowledge Management.

The importance of understanding the reasons for past decisions is not a new subject. However, there seems to be a gap when it comes to verifying the efficiency of tools for understanding past decisions. In this paper, we show that the ability to perform decision reconstruction using a Group Support System (GSS) can provide a flexible solution to the problem, but only if the information model underlying it is able to provide bidirectional support to the phases of a decision-making process. For this, we present a general information model to support the decision-making process, as well as the decision reconstruction process. We tested these ideas by setting up a case study where we used a GSS, based on our model, to analyze a simulated public contracting process. We present a discussion of the results.

When a Make-To-Order (MTO) enterprise receives a bidding invitation from a customer, he does not know the demand is price-sensitive or time-sensitive. In order to increase the possibility of winning this contingent order, the enterprise tenders his bid with two options characterized with price and delivery time. One is for time-sensitive demand, and it ensures that the products will be delivered as soon as possible. The other is for price-sensitive demand, and it ensures that the products will be produced at the lowest cost. The enterprise is comprised of several plants that jointly produce custom products and each plant's decision is made in a distributed way. Hence, the enterprise has to coordinate the production plans of these plants to generate the two options. Since Analytical Target Cascading (ATC) can solve multi-level hierarchical distributed problem and its convergence is proven, it is adopted to coordinate the plants' planning. This paper proposes a unique bidding option generating process in which an extended ATC model is utilized to produce time-sensitive and price-sensitive options by adjusting the coefficient of delivery tardiness. The numerical study in this paper demonstrates (1) the extended ATC model works effectively; (2) ATC with augmented Lagrangian relaxation (AL-ATC) is more effective in producing solutions than ATC with quadratic penalty function (QP-ATC); (3) ATC is an effective coordination tool by comparing the solutions made by ATC and All-In-One (AIO).