International Journal of Information Technology & Decision Making

Decision making is a vital function in the age of machine learning and artificial intelligence; however, its physical realization and theoretical fundamentals are not yet well understood. In our former study, we demonstrated that single photons can be used to make decisions in uncertain, dynamically changing environments. The two-armed bandit problem was successfully solved using the dual probabilistic and particle attributes of single photons. In this study, we present a category theoretic modeling and analysis of single-photon-based decision making, including a quantitative analysis that agrees well with the experimental results. The category theoretic model unveils complex interdependencies of the entities of the subject matter in the most simplified manner, including a dynamically changing environment. In particular, the octahedral structure and the braid structure in triangulated categories provide better understandings and quantitative metrics of the underlying mechanisms for the single-photon decision maker. This study provides insight and a foundation for analyzing more complex and uncertain problems for machine learning and artificial intelligence.

This work focuses on the text-based recommendation challenge on the Web. In fact, with the emergence of electronic media and the explosion of news articles’ volumes on the Web, it has become difficult to suggest recommendations that best suit users’ interests and preferences. In this work, we propose a model of recommendation whose main objective is to guide Internet users in the great mass of news on the Web. Indeed, our contributions are based on three main points, namely, (1) the online semantic analysis of news articles based on their textual content, (2) the incremental segmentation of news articles into categories while taking into account the scalability problem and (3) the dynamic nature of the proposed recommendation approach that adapts its suggestions based on the users’ context and behaviors. An experimental study was conducted on a real-world use case in order to validate and evaluate the quality and the scalability of our proposal within a production environment.

This paper proposes a decision model with a dashboard for systematically selecting and improving the performance of supplier in green supply chain management. The model combines both the decision-making trial and evaluation laboratory-based analytical network process (DANP) with a fuzzy integral method. The DANP method is used to construct the network system and derives the weights. The fuzzy integral method is applied to avoid inconsistent assumptions between the interdependence relation and linear aggregation for obtaining the aspiration-gaps of green suppliers. Last, the visualized dashboard allows for a clear representation of the interdependent-network structure of the attributes to assist companies in the selection of suitable suppliers, as well as assisting the suppliers in improving their performance with higher levels of service. A Taiwanese Electronics Company is demonstrated as an example. The results demonstrate that the dashboard can enable managers to make better decisions from a systematic perspective.

This paper proposes a new tri-objective scheduling algorithm called Heterogeneous Reliability-Driven Energy-Efficient Duplication-based (HRDEED) algorithm for heterogeneous multiprocessors. The goal of the algorithm is to minimize the makespan (schedule length) and energy consumption, while maximizing the reliability of the generated schedule. Duplication has been employed in order to minimize the makespan. There is a strong interest among researchers to obtain high-performance schedules that consume less energy. To address this issue, the proposed algorithm incorporates energy consumption as an objective. Moreover, in order to deal with processor and link failures, a system reliability model is proposed. The three objectives, i.e., minimizing the makespan and energy, while maximizing the reliability, have been met by employing a method called Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). TOPSIS is a popular Multi-Criteria Decision-Making (MCDM) technique that has been employed to rank the generated Pareto optimal schedules. Simulation results demonstrate the capability of the proposed algorithm in generating short, energy-efficient and reliable schedules. Based on simulation results, we observe that HRDEED algorithm demonstrates an improvement in both the energy consumption and reliability, with a reduced makespan. Specifically, it has been shown that the energy consumption can be reduced by 5–47%, and reliability can be improved by 1–5% with a 1–3% increase in makespan.

The issue of efficiency analysis of network and multi-stage systems, as one of the most interesting fields in data envelopment analysis (DEA), has attracted much attention in recent years. A pure serial three-stage (PSTS) process is a specific kind of network in which all the outputs of the first stage are used as the only inputs in the second stage and in addition, all the outputs of the second stage are applied as the only inputs in the third stage. In this paper, a new three-stage DEA model is developed using the concept of three-player Nash bargaining game for PSTS processes. In this model, all of the stages cooperate together to improve the overall efficiency of main decision-making unit (DMU). In contrast to the centralized DEA models, the proposed model of this study provides a unique and fair decomposition of the overall efficiency among all three stages and eliminates probable confusion of centralized models for decomposing the overall efficiency score. Some theoretical aspects of proposed model, including convexity and compactness of feasible region, are discussed. Since the proposed bargaining model is a nonlinear mathematical programming, a heuristic linearization approach is also provided. A numerical example and a real-life case study in supply chain are provided to check the efficacy and applicability of the proposed model. The results of proposed model on both numerical example and real case study are compared with those of existing centralized DEA models in the literature. The comparison reveals the efficacy and suitability of proposed model while the pitfalls of centralized DEA model are also resolved. A comprehensive sensitivity analysis is also conducted on the breakdown point associated with each stage.

This paper describes a knowledge-based decision support system (KB-DSS) to improve the preparedness of crisis situations induced by natural and technological hazards. The proposed KB-DSS aims to manage the potential cascading effects generated by a triggering hazard assessing the possible event time histories based on interconnected probabilistic simulation models. From a methodological point of view, a decision model based on two Multi-Criteria Decision-Making (MCDM) algorithms follows a cascading effect simulation model. This combination allows to support the decision maker in comparing a set of mitigation strategies on the basis of their expected impacts and his priorities. The algorithm is based on an ensemble approach, which combines decisions over an array of possible impact scenarios, instead of only relying on the average impact scenario. An application of the KB-DSS to the case of a possible reactivation of Nea Kameni volcano in Santorini is presented to show how the proposed architecture could be applied to a real case. The proposed methodology supports the emergency planners in making the best decisions supporting them also in the choice of the best timing for the intervention.

In this paper, we present IMPROMPTU, a distributed resource consolidation manager for larger-scale commodity computing clouds. The main contribution of this work is two-fold. First, IMPROMPTU fully distributes the responsibility of resource consolidation management among autonomous node agents that have a one-to-one mapping with the physical machines in the cloud. Second, autonomous node agents manage virtual to physical machine resource consolidation using multiple criteria decision analysis (MCDA) through PROMETHEE II method. MCDA has been previously used within the context of computing systems, particularly in fields such as multi-agent systems, data mining, and wireless communications. However, to the best of our knowledge, IMPROMPTU represents the first fully distributed MCDA approach applied to the problem of autonomous resource consolidation management for commodity computing clouds. Moreover, IMPROMPTU improves on our previous studies by introducing key extensions to enhance the granularity of the MCDA model. Simulation results show that the proposed solution provides a strong alternative to prior resource consolidation management approaches for the key industry problem of mitigating SLA violations. This establishes solid groundwork for further applications and extensions of MCDA to this important problem domain.

In stock markets around the world, financial analysts continuously monitor and screen chart patterns (technical patterns) to predict future price trends. Although a plethora of methods have been proposed for classification of these patterns, there is no uniform standard in defining their shapes. To facilitate the classification and discovery of chart patterns in financial time series, we propose a novel domain-specific language called “Financial Chart Pattern Language” (FCPL). The proposed language is formally described in Extended Backus–Naur Form (EBNF). FCPL allows incremental composition of complex shapes from simple basic units called primitive shapes. Hence, patterns defined in FCPL can be reused for composing new chart patterns. FCPL separates the specification of a chart pattern from the mechanism of its implementation. Due to its simplicity, FCPL can be used by stock market experts and end users to describe the patterns without programming expertise. To highlight its capabilities, several representative financial chart patterns are defined in FCPL for illustration. In the experiments, we classify several representative chart patterns from the datasets of HANG SENG INDEX (HSI), NYSE AMEX COMPOSITE INDEX (NYSE), and Dow Jones Industrial Average (DJI).

When a diverse group of experts share their knowledge to ascertain and solve a particular multiple criteria decision-making (MCDM) problem, uncertainty arises from several sources. In those cases, the interval multiplicative preference relation (IMPR) approach is a useful technique when verifying consistency. As illustrated in this paper, the validation and the improvement of consistency require robust analysis tools and algorithms. The proposed methodology provides reliable and consistent IMPR, which can be quantified in terms of row geometric mean method (RGMM) or the eigenvalue method (EM). In this manner, decision makers’ choices are implicitly including their uncertainty while maintaining acceptable consistency. The present approach is based on the Hadamard’s dissimilarity operator and through an algorithm, the derivation of a reliable and consistent IMPR is synthesized. In order to illustrate our results and compare them appropriately with other methodologies, a few examples are addressed and solved.

In this study, we propose Euclidean best–worst method (Euclidean BWM), which does not require any other extra calculations and analysis compared to nonlinear Chebyshev BWM. Using numerical examples, we illustrate and discuss the efficiency of the Euclidean BWM based on minimizing Euclidean norm instead of Chebyshev norm and using the consistency index matrix. Obtained results show that Euclidean BWM is an efficient tool resulting in reliable unique solutions, regardless of the number of the criteria, comparing with the linear and nonlinear model of the Chebyshev BWM. Moreover, we develop a MAPLE package “BWM” using only pairwise comparison vectors as the arguments to obtain the unique solution of a given problem by both the Euclidean BWM and linear model of Chebyshev BWM.