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Dr. Yahia Zare Mehrjerdi,
Volume 6, Issue 2 (9-2015)
Abstract
Abstract This author introduces the concept of Stepwise Strategy Approach (SSA) for dealing with a number of problems arises in the current age of technology. This new idea is combined with the knowledge of Grey Theory for adding flexibility to decision making process. Grey theory is useful for grasping the ambiguity exists in the utilized information and the fuzziness appears in the human judgments and preferences. This article is a very useful source of information for Fuzzy Grey and decision making using more than one decision makers in fuzzy environment. A case study on system selection comprised of 12 attributes and 4 alternatives is constructed and solved by the proposed method and the results are analyzed. For the validation of the results obtained by the Grey theory, the fuzzy VIKOR and Fuzzy TOPSIS were employed for computational purposes. The results of these three approaches on the proposed case study are closely related. Due to the fact that this author proposes the “Stepwise Strategy” approach for implementing a new technology in industries, where already the management of an older compatible type of technology is in existence, along with the grey theory concept and data whitenization approach, its contribution to the literature of operations research is highly recognizable.
Jahanyar Bamdadsofi, Razieh Birank, Fatemeh Mohammadnezhad Chari,
Volume 15, Issue 2 (12-2024)
Abstract
During the COVID-19 pandemic and the resulting constraints, businesses have encountered changes in their customers’ behaviors and business environments. Scholars have emphasized on the role of digital transformation as a response to these challenges. This study investigates the level of digital maturity in seven dimensions of digital transformation presented by Kane (2017) in small service businesses (SSBs). A mixed method combining the logic of statistical accuracy of questionnaire analysis with the realistic aspects of discourse analysis was applied. The results revealed that digital transformation does not fully occur among the SSBs. Instead, some extent of digitalization happened in various areas such as communications with customers and the digitalization of some aspects of services. Besides, the study revealed that the most frequent pathways taken by SSBs toward digitalization are the less capital-intensive and technology-based ones . Furthermore, customers are involved in three types of relational activities categorized as “transactional”, “intercommunication”, and “information sharing”.
Amirhossein Malakouti Semnani, Sohrab Kordrostami, Amirhossein Refahi Sheikhani, Mohammad Hossein Moattar,
Volume 16, Issue 2 (8-2025)
Abstract
Insurance companies face the critical challenge of identifying “good customers”—policyholders who consistently pay premiums with minimal or no claims—within large, heterogeneous datasets. This study proposes and evaluates a hybrid machine learning framework to predict good customer status using an enhanced insurance dataset that integrates demographic, financial, and policy-related features. The framework combines an XGBoost classifier, a soft-voting ensemble of RandomForest and LightGBM, and a custom Transformer Encoder, with all models tuned using the Optuna hyperparameter optimization library to enhance predictive accuracy and interpretability.
The methodology includes preprocessing steps such as categorical encoding and standardization of numerical variables (e.g., age, BMI, premium with GST), followed by a novel label engineering scheme that defines good customers as those whose premiums exceed the mean plus one standard deviation and have no claim history. The dataset is split into training (80%) and testing (20%) subsets. Two hybrid architectures are developed: Model A, which fuses the predicted probabilities from XGBoost and the Transformer Encoder using a 60–40 weighting, and Model B, which employs a soft-voting ensemble of RandomForest and LightGBM. Ablation studies quantify the contribution of each component, while performance is assessed using accuracy, AUC, F1-score, and Matthews Correlation Coefficient, supported by visual tools such as correlation heatmaps, ROC curves, and confusion matrices.
Experimental results show that Model A attains an accuracy of 0.8720 and an AUC of 0.9140, whereas Model B achieves an accuracy of 0.8850 and an AUC of 0.9260 after systematic hyperparameter tuning. Removing either the Transformer or XGBoost markedly degrades Model A, while omitting RandomForest or LightGBM leads to smaller performance drops in Model B, underscoring the value of ensemble diversity. Overall, the proposed framework provides a practical tool for insurance customer segmentation and profitability-oriented decision-making, and its open-source implementation facilitates replication, extension with additional features or larger datasets, and potential real-time deployment in operational insurance environments.
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