Modern Research in Decision Making

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.