Modern Research in Decision Making

Abstract Delocation is one of the areas of location science that, according to decision-makers' policies and environmental impacts, the existing facilities are reduced or closed. In this study, a merging facility delocation model for the so-called military banks of the Islamic Republic of Iran has been developed. In terms of governance and in order to convince the stakeholders, an equity approach is considered using fuzzy constraints. Also, due to the diversity of the brands and many factors to determine the importance of branches, the efficiency score of the data envelopment analysis method using the input-based CCR model was considered as the weight of the branches. To solve the problem, the augmented Epsilon-constraint method has been used. The results of the study for branches of banks showed that the objectives of equity and efficiency were inconsistent and produced non-dominated solution. Qom city has been considered to better understand the issue of facility merger. The model has been solved using GAMS software and Bonmin solver.

Abstract This article presents a novel simulation-optimization framework for credit scoring and optimal bank facility parameter determination. The method comprises three stages:
1. Data Preparation: Collecting financial statements and facility data from 1,000 Iranian corporate clients (2017-2021), with 11 critical features selected from 30 variables using the Minimum Redundancy Maximum Relevance (MRMR) algorithm.
2. Credit Scoring: Five classification models—Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGB), and Random Forest (RF)—are evaluated via accuracy, F1-score, and AUC. Random Forest (RF) outperforms others (accuracy: 89.2%, AUC: 0.93).
3. Simulation-Optimization: A Memetic Algorithm (MA) optimizes three variables—facility amount, interest rate, and repayment period—across four scenarios. The MA integrates a pre-trained RF model to estimate default probabilities dynamically.
Key outcomes:
• Adjusting parameters (34% lower facility amounts, 25% reduced interest rates, 40% longer repayment terms) cuts default rates from 38% to 20%.
• The approach enhances bank profitability by 19% through risk-adjusted loan pricing.
This methodology bridges AI-driven credit assessment with metaheuristic optimization, offering a scalable solution for credit risk mitigation in emerging markets. By enabling real-time adaptation to customer risk profiles, it empowers banks to balance profitability and risk exposure effectively.

Abstract Selecting an appropriate social customer relationship management strategy is recognized as one of the most important factors for success in implementing this technology. Based on this, in this research, the design of a model for selecting social customer relationship management strategy using a fuzzy inference system was addressed. The present research is a applied-development study in terms of purpose and a mixed-methods research in terms of nature. To achieve the research objective, the required data were first collected through literature review and interviews with experts, and then a hybrid fuzzy inference system consisting of two fuzzy expert systems was designed to select a social customer relationship management strategy. The first expert system was created by integrating three subsystems: organizational factors, customer factors, and environmental factors. Following this, a single-layer inference system was designed to address strategy factors. The research statistical population in the qualitative section consisted of academic and industry experts in the field of marketing and customer relationship management, 13 of whom was selected as samples through the snowball sampling method. In the phase of formulating the rules of the fuzzy inference system, 46 insurance industry experts were selected through convenience sampling. To analyze the interview data, content analysis was used, and to develop a model for selecting social customer relationship management strategies, the fuzzy inference system method was employed using MATLAB software. The results indicate that the designed system has good validity.

Abstract Iran's heavy dependence on oil exports requires more attention to the development of non-oil exports, especially in the agricultural sector and the medicinal plant industry. This article examines and models the market growth of medicinal plants in Iran using a system dynamics approach from a holistic perspective. The main objective of the research is to identify the key dynamics in the medicinal plant market and provide solutions to improve this market. System dynamics allows the analysis of interactions between key factors and the market growth of medicinal plants. Research data were obtained from library sources and interviews with 11 experts, including industrialists, academics, and researchers. The dynamic model of the problem was developed, formulated, validated, and simulated in Vensim software to examine and compare different scenarios based on dynamic hypotheses. In this regard, the results of implementing the four proposed scenarios in the model were also analyzed. The results indicate that by implementing policies such as: 1) raising awareness among doctors, 2) increasing the skills and knowledge of farmers and improving the performance of Iranian merchants, 3) increasing primary advertising and establishing general and specialized stores, and 4) improving the quality and diversity of products and packaging, it will be possible to grow and sustainably develop the medicinal plant market in Iran.

Abstract In this study, a coordinated decision-making structure is examined in a two-level model involving a producer and a distributor in the film-making supply chain using a revenue-sharing contract. Demand in the presented model depends on the producer’s social responsibility costs and the distributor’s marketing effort. This study seeks to optimize the cinema ticket price the level of marketing effort related to the distributor’s decisions, the price of the distribution rights, and the level of social responsibilities related to the producer’s decisions. The variables of the presented model are analyzed and examined in two scenarios of decentralized decision-making and coordinated decision-making, and the values of the decision variables as well as the profit of the supply chain members and the total profit of the supply chain are compared, and optimal strategies are analyzed. To move from a decentralized decision-making model to a coordinated decision-making model, a partnership contract based on marketing cost sharing, social responsibility costs as a share of the partnership, and revenue sharing based on the amount of bargaining power in a way that the members of the cinema supply chain benefit from this partnership is presented. The results show that the profit of the supplier and distributor under the revenue-sharing partnership agreement (in the sharing range of 0.285 to 0.795) improves compared to decentralized decision-making. Also, in the range outside the sharing rate, the revenue-sharing partnership agreement cannot coordinate and decentralized decision-making in which the supply chain members make decisions independently and individually will have priority.

Abstract Recognizing intuition in management and proactively addressing biases can significantly lead to better evaluation of decisions and subsequently improve decision-making quality. The subject of Bias is not only explored in management literature but also in fields like economics, psychology, medicine, law, education, and others, each examining biases and related issues from its own perspective. Therefore, this research, using a management approach, will study cognitive biases and their types. The aim of this systematic review is to provide a comprehensive and up-to-date overview of research on biases affecting strategic decision-making, leading to a better understanding of managers’ decision-making patterns. By identifying cognitive biases influencing strategic decision-making, this research will help managers make better decisions by being aware of potential pitfalls and provide researchers with a comprehensive list of these biases for conducting more structured research.48 biases influencing strategic decision-making were identified and presented. Since the meanings of many biases overlap, the summary table separates “similar titles” to avoid repetition. To compare biases with related meanings where the differences are not negligible, a separate column, “Biases with Related Meanings,” distinguishes these types of biases. A categorization of biases was also provided to offer a framework for studying them. The biases presented in this research were categorized into three groups: cognitive limitations, motivational biases, and a hybrid category. The cognitive nature of biases relates to human limitations in receiving, processing, and storing information. However, the motivational nature stems from human instincts and basic needs. The main underlying mechanism of motivational biases is self-interest