Modern Research in Decision Making

Abstract With increasing market uncertainty and the growing complexity of competitive environments, supply chains have become more vulnerable to disruptions. Even minor fluctuations in demand or reductions in capacity can generate widespread negative impacts on overall supply chain performance. As a result, developing mathematical models that capture this complexity has become more challenging and requires appropriate solution methodologies. This study proposes an effective solution approach for the robust, scenario-based multi-objective mathematical model developed by Nazari et al. (2024), which seeks to reduce total system costs and environmental emissions. A key innovation of the model is the simultaneous consideration of demand uncertainty—driven by inflation rates—and capacity reductions caused by disruptions. Because exact solution methods become impractical for large-scale, multi-objective problems, two powerful metaheuristic algorithms were employed: a multi-objective extension of the emerging Honey Badger Algorithm (HBA) and the well-known Non-dominated Sorting Genetic Algorithm II (NSGA-II). To evaluate the performance of the proposed model and algorithms, a numerical case study was designed and executed. The results indicate that the proposed model is highly effective in handling uncertainty and supports more optimal decision-making aligned with economic, social, and environmental sustainability. Furthermore, the computational results demonstrate that the HBA algorithm achieves higher efficiency and accuracy in solving hard (NP-hard) problems with large, complex search spaces.

Abstract Energy is one of the basic needs of human life. Production, distribution and optimal consumption of energy have always been one of the basic questions in economics and planning. Energy is one of the main factors of economic growth and development of countries. Human's ever-increasing demand for all kinds of available energy and non-renewable resources to achieve economic development has always faced an increase in environmental pollution and various diseases for human life. The aim of this study is to model the supply chain of biofuels using urban sewage waste in Iran. For this purpose, the combination of system dynamics modeling method and integer mathematical programming model has been used. The period studied in this research is 1388-1397 and its simulation is considered until 1410. The assumptions considered in this research are: with the increase in the payment of subsidies to biofuels, the price of biofuel will decrease; with the increase in the conversion rate of sewage waste into fuel, the price of biofuel will decrease; and with the increase in the price of biomass, Biomass production capacity increases. Also, by doubling the price of green fuel, the production capacity of green fuel increases by 2.9 percent. The results of the simulation are the design of an optimal supply chain of urban biomass resources and also the maximization of the profit of the country's biological refineries.

Abstract The objective of this study is to design a data-driven model for extracting effective rules influencing the product acceptance rate in food industry production lines. To this end, first, 25 variables related to lean manufacturing principles were identified, and using the fuzzy Delphi method, six key variables were selected, including the percentage of conformity of raw materials with quality standards, production scrap rate, exceptional approval rate of the final product, average raw material storage time, work shift, and product return rate. Subsequently, a database consisting of 4,200 real records from two years of production line performance was collected, and after data preprocessing, modeling was conducted using machine learning algorithms. Among these, the decision tree algorithm (CART) was employed as the primary model for extracting interpretable decision rules, and its performance was compared with other classification algorithms. The results indicated that the “percentage of raw material conformity” and the “scrap rate” had the greatest impact on product acceptance. The decision tree model achieved an accuracy of 91% in classifying samples into acceptance and non-acceptance categories. Moreover, the extracted rules revealed transparent and interpretable relationships among the variables and provided a basis for developing decision-support patterns for production and quality control managers. Overall, the findings indicate the effectiveness of lean production–based data-driven approaches in improving quality prediction and reducing waste in the case study of this research.

Abstract With the expansion of operational data and the growing complexity of business environments—particularly in the retail industry—accurate demand forecasting has become a key requirement for effective decision-making and efficient supply chain management. The nonlinear nature, high volatility, and noise inherent in sales data often lead to poor and unreliable performance of traditional forecasting methods. This challenge is further amplified in Iran due to the high level of uncertainty arising from economic fluctuations, shifts in trade policies, logistical constraints, and the impacts of sanctions. In response to these needs, the present study introduces the GODFIS algorithm, a Global Optimization-based Dynamic Fuzzy Inference System, designed to provide accurate, robust, and adaptive forecasts in highly volatile environments. The architecture of GODFIS—through the integration of online learning, dynamic clustering, recursive updating of consequent parameters, and a noise-filtering mechanism—enables precise modeling of complex relationships without the need for full retraining. To evaluate its performance, the proposed algorithm is tested on two categories of data: (1) benchmark datasets with non-stationary behaviors, and (2) real-world data from an Iranian retailer. The results indicate that GODFIS delivers substantial improvements in accuracy, stability, and adaptation speed compared to classical methods, machine learning models, and evolving systems. Overall, the findings suggest that GODFIS, by offering interpretability, scalability, and computational efficiency, can serve as a dynamic forecasting framework for retail operational environments—especially under Iran’s unstable conditions—and play a significant role in optimizing inventory decisions and managing uncertainty.

Abstract Contemporary agriculture is increasingly confronted with challenges related to sustainability, adaptation to complex environments, and innovation development; challenges that can no longer be addressed solely through technological interventions or formal policy measures. In many agricultural systems, the core problem is not the lack of knowledge or innovation, but weaknesses in the absorption, internalization, interpretation, and application of new knowledge within organizational structures and operational processes. Drawing on the Viable System Model (VSM) as a cybernetic framework, this article reconceptualizes absorptive capacity as a mechanism for the emergence and institutionalization of innovation in agricultural systems. This study adopts a qualitative approach and is conducted through a case study of the Farijan Agro-Industrial Complex. Thematic analysis was employed for data analysis, while the Perez Rios diagnostic approach was used to operationalize the VSM framework. In addition, selected analytical considerations derived from the VIPLAN approach and Jackson’s checklist were used to address stakeholder interactions and structural pathologies. The findings indicate that absorptive capacity functions as a distributed mechanism across organizational subsystems and plays an important role in transforming external knowledge into operational and structural innovation. The results further show that the Viable System Model, through strengthening communication flows, recursive learning, and alignment across organizational levels, provides a suitable foundation for enhancing absorptive capacity and sustaining innovation in agricultural systems. This study argues that innovation in agriculture is fundamentally a systemic phenomenon that depends less on technological interventions and more on organizational architecture and knowledge capabilities for institutionalizing innovation.

Abstract This research investigates the predictability of Bitcoin price directions following investor overreaction, a prominent anomaly widely documented in behavioral finance. Leveraging a multimodal dataset—integrating transactional variables, technical indicators, on-chain metrics, and social signals derived from Google Trends—this study analyzes market dynamics from August 2017 to December 2023 and identifies 303 distinct overreaction episodes. To construct a reliable predictive framework, a rigorous three stage feature selection pipeline was implemented, combining Random Forest screening, correlation matrix analysis, and Variance Inflation Factor (VIF) assessment. This process yielded 11 highly informative and non multicollinear variables that served as the input features for the proposed multivariate Long Short Term Memory (LSTM) model. The LSTM model achieved a directional prediction accuracy of 67%, substantially outperforming the classical Logistic Regression benchmark with 53.4% accuracy. Moreover, the consistently declining and stable training and validation loss curves demonstrate strong generalization performance and confirm the model’s robustness against overfitting. Empirical findings reveal statistically significant short term market inefficiencies and predictable abnormal returns following overreaction events at the 99% confidence level. Overall, the study provides compelling evidence of exploitable behavioral patterns within the Bitcoin market and offers a practical foundation for developing advanced algorithmic trading strategies and dynamic risk management systems in cryptocurrency markets.

Abstract In recent years, increasing environmental fluctuations, resource constraints, and crises such as the COVID-19 pandemic have increased the need to design sustainable and resilient supply chains. considering the dimensions of the circular economy, the concepts of the Fifth Industrial Revolution, and conditions of combined uncertainty. The main innovation of the study is the linking of the results of multi-criteria decision-making with a multi-objective model and the simultaneous operationalization of the circular economy and the components of the Fifth Industrial Revolution in network design. In the first stage, the criteria and sub-criteria for evaluating distribution centers in the dimensions of sustainability, resilience, agility, digitalization, human-centeredness, and circular economy were identified and weighted using the best-worst fuzzy-stochastic method. Then, distribution centers and sales channels were ranked using the fuzzy-random TOPSIS method. Next, a multi-objective mathematical model was developed for the design of a closed-loop supply chain network that considers forward and reverse flows, supplier selection, capacity allocation, development of collection and recycling centers, and establishment of an information sharing system under uncertainty. The proposed model was solved using a modified lexicographic-Chebishof multi-option goal programming method and its case study was conducted at Behran Oil Company. The results showed that the resilience criterion is the most important in the evaluation of distribution centers, and the use of alternative suppliers, excess capacity, and information sharing system play an effective role in promoting resilience, sustainability, and network efficiency. The results confirm the effectiveness of the proposed framework in improving resilience, sustainability, and network efficiency.

Abstract Road traffic accidents are among the most significant humanitarian crises, causing substantial loss of life and considerable economic and social costs worldwide. According to the World Health Organization, road traffic accidents claim approximately 1.19 million lives annually. This study aimed to develop and simulate a system dynamics model to improve the performance of road traffic accident response and rescue operations of the Iranian Red Crescent Society in Yazd Province. The model was developed based on real-world data from rescue and relief operations during 2018–2022. Key variables were identified through a literature review and expert judgment and organized into four subsystems: human resources, casualty response, training and empowerment, and communication and coordination. Subsequently, a stock-and-flow model was developed and validated using behavior reproduction, extreme conditions, and sensitivity analysis tests. Three policy scenarios—an increase in the training budget, reduction of nuisance calls through deterrent measures implemented by legally authorized institutions, and a combined policy—were simulated over the period from April 2022 to March 2025. The results showed that the nuisance-call reduction scenario decreased response time from 11.1 to 9.7 minutes (12.6%) and increased the triage rate from 227 to 268 persons (18.1%). Increasing the training budget raised the triage rate to 233 persons (2.6%). Under the combined scenario, the triage rate increased to 272 persons, representing a 19.8% improvement over the baseline and a 1.5% improvement over the nuisance-call reduction scenario. Therefore, the simultaneous implementation of both policies produced the greatest improvement in overall response performance.

Abstract One of the main issues in Iran’s economy is the government’s debt to the Central Bank, which has been increasing over the years and has exerted destructive effects on the economy.
The purpose of this research is to examine the relationship between “government debt to the Central Bank” and “Okun’s Misery Index” using the Vector Autoregression model, the Engle-Granger cointegration test, and other necessary tests over the period 1979–2025 with EViews software.
The findings show that both variables are stationary of order two, and the Engle-Granger cointegration test confirmed a long-run equilibrium relationship between them. Impulse response functions indicated that a one-standard-deviation positive shock to government debt has a limited short-run effect on the Misery Index, but a positive and increasingly significant effect in the medium and long run. Forecast error variance decomposition reveals that short-run fluctuations in the Misery Index are mainly explained by its own shocks, while the share of government debt shocks rises to approximately 12 percent by the tenth period. Consequently, a one-percent change in government debt leads to a 1.1 percent change in the Misery Index.
These findings not only reveal the causal relationships between the two variables but also provide quantitative insights for constructing policy decision-making models, enabling a shift from intuitive toward model-based and evidence-based decision-making.

Abstract Location theory is an interstice field of optimization and operations research. In the classic location problem, the goal is finding the location of one or more facilities such that some criteria such as transportation cost, the sum of distances passed by clients, total service time and cost of servicing are minimized. In this paper, we consider the goal location problem. In the goal location problem, the ideal is locating the facility in the distances ri, from the i-th client. However, in the most instances, the solution of this problem doesn’t exist. Therefore, we consider the minimizing of distances between clients and ideal point. The minimizing sum of square errors and minimizing absolute errors under Lp norm are considered as the objective function. We use the Weiszfeld like, Gauss-Newton and imperialist competitive algorithms for solving the problem. Then we compare the results which obtained by these methods for some test problems.

Abstract A growing business environment with growing uncertainty, unexpected dangers and quick unavoidable changes increases the probability of intense disturbance in corporations' supply chain. This trend that accompanies by natural disasters e.g. flood, tsunami, earthquake and etc. increases the necessity of resiliency and development of supply chains specially in pharmaceutic supply chains which are more sensitive. Managers require tools monitoring their supply chain resiliency against disturbance. The main purpose of this research is measurement and assessment of supply chain resiliency in pharmaceutical industry. In this study; considering complex adaptive systems (CASs) approach under the title of theory lens; the supply chain of two Iranian pharmaceutical corporations (Iran Daroo and Ghazi pharmaceutical Company) were chosen to be examined. In the following, the SCR dimensions and factors in the CAS framework were identified by systematic literature review. Afterward, this research proposes the integrated and systematic method by combination of Interpretive Structural Modeling (ISM), DEMATEL, graph theory and matrix approach (GTMA) and importance-performance analysis (IPA) to measure and assess the level of resiliency of both supply chains. Finally, conclusions from this research can support the manager’s analysis of resiliency and selection of effectiveness risk mitigation strategies in their supply chain and simplifies decision-making. This novel approach causes a competitive advantage to achieve market share even during a disruption.

Abstract Abstract
This paper aims to define and design a model of world class manufacturing (WCM) in the automotive industry, using Interpretive Structural Modeling (ISM). World class manufacturing model enables organization to pursue their activities and competitions in the global scope. In addition, this will not be fulfilled unless the organization can be assessed in accordance with the best world-class industry and competition. This model consists of eight main pillars and twenty-three sub-elements in the form of technical and managerial elements classified and crystallized. The main elements of the model include business processes, flexibility, technology and electronic tools, electronic supply chain management, new product development, human capital, competitive strategies and performance evaluation. Although each of the pillars, major and minor, has unique influence on the structure of a model, none alone will be able to assist organization in achieving its main goal (world class manufacturing), so in order to establish the integrity of the pillars, interpretative structural modeling (ISM) technique is used. Sampling method is non-randomized targeted. The sample is taken from the elites and experts, and the results in form of model diagram are outlined by using interactive network of principal and subsidiary organs (dimensions and indicators): this shows the road to the world class manufacturing. Additionally, in this paper varied methods of ISM cognitive mapping are noticed.

Abstract A set of environmental and, social factors along with the economic issues play the special role in the supply chain susutainability in the high risk industries. The aim of the present study is to explain and analyze the dimensions and components of supply chain sustainability dimension, with respect to the scope and extent of the concept of sustainability in the supply chain issues. For this purpose, we used qualitative research approaches and meta-synthesis tools, which includes seven steps, and started systematic analysing and evaluation of the results and findings of previous researches. In general, the concept of supply chain sustainability is identified and classified in three dimensions, 14 theme and 84 component. Finally, based on content analysis, the impact factor of identified components in the 94 final researches are identified, by using quantitative methods of Shannon entropy.  

Abstract Upon equipping the banks to Electronic payment and receiving systems as well as the use of credit cards, most of the customers do their bank transactions by using credit cards and through the use of credit channels such as ATM machines, POS sale terminals, phone banking, internet banking, etc. The customers are now better able to find their required services and products and they may even change their own bank because of the type of services required. Therefore, managing customer relations is inevitable for banks. One of the helpful instruments in managing customer relations is data mining. Four data mining methods including decision tree, simple Biz, Vicinity neighbor K and combinatory model were used in this study to identify the most profitable services used by the customers. Each of these methods has been investigated on real data and the efficiency of each method has been examines. The results of model evaluations showed that vicinity neighbor K’s accuracy in finding the profitable services was equal to 93.26%, that of Biz was 74.83% and that of decision tree was 97.18%. in addition, the accuracy of combinatory model was 94.80%. Further, the combinatory model was successful in accurately identifying 96.01% of the normal services and it was also successful to accurately identify 94.44% of the services. Therefore, we may conclude that it has a far better performance as compared with Biz model and Vicinity Neighbor K. the evaluations results showed that combinatory model is more accurate to use as compared with other existing models.

Abstract In this paper, effective factors on retention and profitability of customers a state owned bank was studied using random forests and regression forests methods. The data was collected form Bank Sepah database. The statistical population consists of 169 corporations holding different types of deposit accounts and utilized one of the different types of services such as sending and receiving payment orders, letters of credit (LC) and foreign exchange facilities during the research period (1389-1391), simultaneously. In this paper, the accuracy of the results of random forests method is compared with the results of logistic regression and bagging methods using area under the Receiver Operating Characteristic (ROC) curve. Furthermore, the results of regression forests method was compared to those of linear regression by calculating Mean Absolute Percentage Error (MAPE). Then, we tried to determine the importance of effective independent variables on dependent ones, i.e. next purchase, activity defection, profit drop and profit continuity using random forests and regression forests. The results show that in the case of offering currency facilities to the customers active in the production fields leading to opening LCs and receiving more payment orders will increase the probability of customer retention. In addition, an increase in the amount of foreign exchange facilities and payment orders offered by banks due to the rate of return of foreign exchange facilities, banking fees, income resulting from issued warranties and selling currencies plays an important role in the profitability of customers.

Abstract Analysis decision-making patterns of buying behavior of customers and providing a predictive model is one of the challenges and areas of interest to researchers which can be widely used in the field of localization products. This study aimed to analyze and model the behavior of older customers decide to buy organic products with the hybrid approach. The research was done in two steps linked together. In the first step the reliability and validity of a questionnaire with 33 question was evaluated respectively by Cronbach's alpha and confirmatory factor analysis first and second order was approved. Opinion of 388 old customer using nine indicators were collected, then, using cluster analysis K-means based on Davies-Bouldin and sylvite criterion the optimal clusters was identified. And elderly clients in the two clusters were unwilling and eager to buy organic products was classified. In the second step purchasing behavior using decision tree models were analyzed and the optimum model was extracted "if-then" rules associated with each cluster were presented. The results showed that in both unwilling and eager cluster, education index predict the decisive factor in the decision to buy organic products, It also seems that the consumption of organic products among the elderly is not in good condition in this context proposals were presented for each cluster.