International Journal of Information Technology & Decision Making

Predictive maintenance represents a promising application of Artificial Intelligence in the industrial context. The evaluation and selection of predictive maintenance algorithms primarily rely on statistical measures such as absolute and relative prediction errors. However, a purely statistical approach to algorithm selection may not necessarily lead to the optimal economic outcome, as the two types of prediction errors are negatively correlated, thus, cannot be jointly optimized, and are associated with different costs. As the current literature lacks corresponding guidance, we developed a decision model for industrial full-service providers, applying an economic perspective to selecting predictive maintenance algorithms. The decision model was instantiated and evaluated in a real-world setting with a European machinery company providing full-service solutions in the field of car wash systems. Building on sensor data from 4.9 million car wash cycles, the instantiation demonstrates the applicability and effectiveness of the decision model with fidelity to a real-world phenomenon. In sum, the decision model provides economic insights into the trade-off between the algorithms’ error types and enables users to focus on economic concerns in algorithm selection. Our work contributes to the prescriptive knowledge of algorithm selection and predictive maintenance in line with the consideration of different types of cost.

Ontologies are attracting increasing attention in software engineering research due to their ability to precisely model the semantic aspects of systems. Enriching software system models using ontology principles, especially in the process of developing interactive systems like Service-Oriented Architecture (SOA), can lead to the automated production of high-quality codes. Additionally, ontology-aware specification of the service from the abstract to the concrete level leads to early precise extraction of the service required by the users. This paper introduces a model-driven ontology-aware service development process to reduce the burden of code generation. This integrated approach utilizes a stepwise refinement methodology facilitated by a novel refinement algorithm to automate SOA development. The effectiveness of the approach is evaluated using three proposed parameters which examine the characteristics of the refined model in each refining step through some practical SOA case studies. Finally, we calculate the recall, precision, F-measure, accuracy, and also time analysis of discovered querying services for various scenarios in AWS and Netflix before and after applying ontology. The results show an average of 17% improvement in these metrics after applying ontology.

Corporate reputation is one of the most valuable assets a company must maintain to remain in business, and it becomes even more critical during crises such as COVID-19, which pose a severe threat not only to employees, customers, and the general public but also to the company’s fundamental survival. The purpose of this study is to identify themes affecting corporate reputation and assess how leading companies responded to the COVID-19 crisis. This study targeted the top 100 (RQ score higher than 50) companies according to the 23rd Annual Reputation Quotient from Axios-Harries Poll 100 RQ report. Employing cluster and sentiment analysis, the study explores CEO letters to understand how they emphasize reputation factors and employ impression management tactics. Findings of our study illustrate nuanced reactions and attitudes of CEOs toward issues presented by COVID-19, shed light on the intricacies of reputation management during times of crisis.

In multi-criteria decision-making, the Choquet integral can handle the interaction between criteria; however, the interaction between criteria is not only related to the criteria themselves but may also depend on the performance values of criteria. We present a further extension of the Choquet integral considering this fact. A classic example is used to illustrate the limitations of the Choquet integral, and a performance-dependent assumption is proposed that describes the interactions influenced by the performance of alternatives on criteria. Based on this assumption, we propose an alternative capacity and construct an improved Choquet integral to obtain alternative’s global utility. A preference model based on the improved Choquet integral with a wider range of applicability and more realistic decision results is proposed. Finally, a numerical example is provided to illustrate the feasibility and validity of our reference model.

The English Translation of the Quran Tafsir (ETQT) is essential to understanding and interpreting Allah’s words. Clustering is a common text mining technique for eliciting meaningful knowledge from a text collection. It is commonly used when the selected datasets lack typical ground truths. To the best of our knowledge, no study has evaluated and benchmarked ETQTs to select the most comprehensive and appropriate one. The process of evaluating and benchmarking ETQTs falls under the multicriteria decision-making (MCDM) problem because of different issues, namely, multiple internal clustering validation criteria, data variation, and trade-offs between different criteria. The fuzzy decision by opinion score method (FDOSM) is one of the most recommended MCDM ranking methods in the literature to address the said issues. FDOSM has been extended under different fuzzy set (FS) environments to address issues of uncertainty and vagueness caused by expert feedback subjectivity. Although prior versions of FDOSM improved the uncertainty and vagueness issues, they remain open issues. Therefore, this paper extended FDOSM into the complex Pythagorean fuzzy decision by opinion score method (CPFDOSM) to evaluate and benchmark ETQTs. The proposed method consists of two main phases. The first phase formulates decision-matrix-based cluster algorithms and internal cluster validation criteria. The second phase (CPFDOSM development) prioritizes ETQTs and selects the optimum one. Data generation is performed on five different cluster algorithms and six internal cluster validation criteria using 16 ETQTs based on three decision-makers (DMs). Results show the following: (1) 6.25% of the individual decision-making results are identical among the three DMs, whereas 93.75% ($n=15/16$) are different when $\delta =0.5$ and $\delta =2$. When $\delta =0.5$. In addition, T7 has consistent ranks (Rank=16) across all DMs, whereas T14 has consistent ranks (Rank=1) across all DMs when $\delta =1$. (2) The results of the group decision-making reveal T14 is the most comprehensive and appropriate ETQT across all $\delta$ values. Objective validation and comparison analysis show the proposed method is ranked systematically.

Sustainable supplier management literature is mainly on sustainable supplier selection but sustainable supplier performance monitoring & evaluation studies are scarce. Furthermore, the studies do not differentiate the sustainability evaluation criteria between the supplier selection and monitoring & evaluation stages. To bridge this gap, this study aimed to monitor & evaluate sustainability performance of the suppliers of a focal manufacturing company in automotive sector with the TBL approach. Toward that end, we questioned and tried to identify first the sustainable supplier selection and monitoring & evaluation differentiating criteria, and then, how we can rank and rate the sustainable suppliers for developmental purposes in the monitoring & evaluation phase. 21 criteria were determined for sustainable supplier performance in monitoring & evaluation by a detailed literature review and taking the opinions of the focal company. Then, decision makers assessed sustainability dimensions and determined criteria related to these dimensions. The weights of dimensions and criteria were calculated by Interval-Valued Intuitionistic Fuzzy AHP method. Then, sustainability performances of selected suppliers from the focal company’s portfolio were ranked by using Fuzzy EDAS, Fuzzy CODAS and Fuzzy MOORA methods. The results show that sustainable supplier monitoring & evaluation criteria involve a mix of external criteria (rules and regulations) and internal criteria (suppliers’ values). This finding helps us understand how we can have a more relaxed criteria set involving basic external criteria while selecting suppliers to have access to a more innovative and diverse supplier space and then have more challenging internal and external criteria to monitor & evaluate those suppliers toward true sustainability.

We design, implement and assess in this paper a new architecture of a possibilistic mono- and cross-language information retrieval (IR/CLIR) system. The latter is useful to experiment query disambiguation, expansion and translation processes in both IR and CLIR frameworks. We take advantage of possibility theory to overcome the problems of query disambiguation and expansion in an uncertain and imprecise IR/CLIR context. We investigate the impact of combining possibilistic query disambiguation with expansion on IR/CLIR efficiency. A co-occurrence graph representation is exploited to quantify the similarity between query terms and their semantically close words (expansion task) or between query terms and their possible meanings (disambiguation task). We extend the possibilistic mono-language query disambiguation approach to a cross-language framework. We conduct a set of experiments using ROMANSEVAL data collection, the French–English parallel text corpus Europarl and the CLEF-2003 French–English IR/CLIR test collection. Results highlight some statistically significant improvements of our possibilistic approaches when compared to some state-of-the-art IR/CLIR works.

One of the problems facing online marketing in omni-channel environments is efficient budget allocation to each channel. The complexity of this problem is greater in omni-channel environments due to the greater number of channels and the need to analyze data sources containing user interaction information. To solve this problem, different attribution models have been proposed to assign the weight that each channel has in the acquisition of a product, also known as conversion. Each of these attribution models adopts a strategy to define the weighting of channels. The decision-making strategy is established using the company’s expert knowledge, which can contain different criteria depending on the department to which the expert belongs. The aim of this research is to present a new multicriteria attribution model for online marketing (MAMOM) based on Analytical Hierarchical Process, that resolves this type of problem. MAMOM is a meta-model that takes as input information related to channel features, user interactions and even decisions from other strategies. This information is integrated to enable the integration of interdepartmental strategies as well as to obtain a dynamic attribution model based on expert interviews. The expert assessment procedure is carried out subjectively and simply with a pairwise assessment of the criteria, finally MAMOM obtains a final formulation to calculate the investment to be made in each channel if the experts’ opinions are considered. Results show that first five channels selected by the MAMOM are nearly identical to those that would be obtained with the traditional models but with a different sequence caused by experts’ knowledge. This result shows the capacity of MAMOM to factor in expert opinion and experience for making the investment outcome more aligned with the tactical and strategic objectives of a given online marketing campaign.

In financial network models, to assess systemic risk, a general understanding and prediction of precisely how a single financial institution is associated with systemic risk from the network perspective remains lacking. This paper proposes a framework for predicting and assessing system crises through inferring the cause–effect relationships between financial institutions and system state, which is structured in three steps: the assessment stage for system state based on the mean-variance framework, the prediction stage based on a Bayesian network and the reliability stage based on the Markov process. By applying them to monthly returns of financial institutions, it implies the need to pay attention to insurance and Broker sectors while regulating the banking system on the Bayesian network theory. Moreover, we find that the measure contains predictive power both during tranquil periods and during financial crisis periods. The results can be applied to derive interventions in financial crisis management with regard to systemic risk prediction and system state reliability.

The complex system has been widely applied in aerospace, chemical industry, manufacturing, military equipment and other fields. To ensure its safe and reliable operation, it is necessary for technicians to grasp the health state of the system effectively. However, if there are perturbations in the observation data, the evaluation result may be inaccurate. Besides, the effectiveness of evaluation result can be affected by perturbation, which means that the threshold of perturbation intensity should be determined. To solve these problems, in this paper, a new health evaluation method of complex systems is proposed based on the evidential reasoning (ER) rule by considering perturbations. First, the structure of the evaluation model is constructed. Second, an ER rule-based health evaluation model is proposed, and the idea of perturbation analysis is introduced for the robustness analysis of the model. Third, a parameter optimization model is established, and the upper bound of perturbation intensity is estimated accurately. The threshold can measure the effectiveness of the evaluation result and the anti-perturbation ability of the system in engineering practice. Finally, a case study of the WD615 model diesel engine is conducted to validate the effectiveness of the proposed method.