Modern Research in Decision Making

Abstract هدف اصلی این تحقیق ارائه یک سیستم خبره توانمندسازی شبکه فروش صنعت بیمه با استفاده از سیستم استنتاج فازی است. این پژوهش از نظر کاربردی و از نظر روش، توصیفی - پیمایشی است. جامعه آماری پژوهش شامل خبرگان صنعت بیمه و مدیران، کارشناسان فروش و امور نمایندگان صنعت بیمه است. در این پژوهش ابتدا طیفی از عوامل موثر بر توانمندسازی نمایندگان در صنعت بیمه شناسایی و در اختیار خبرگان قرار گرفت و پس از مصاحبه عمیق با آنها 5 عامل اصلی به عنوان عوامل نهایی شناسایی شدند. با استفاده از معادلات ساختاری روابط بین عوامل شناسایی شد و با احتساب ضریب آلفای کرونباخ برای تمامی عوامل شناسایی شده بین 0.876 تا 0.963 است که نتیجه نشان از روایی قابل قبول سنجش این عوامل بود. همچنین همبستگی متغیرهای پژوهش بوسیله آزمون پیرسون مورد ارزیابی قرار گرفت. در انتها پس از طراحی الگوی مفهومی پژوهش، تک تک عوامل در الگوی ترسیمی و مفهومی پژوهش با استفاده از سیستم استنتاج فازی FIS در نرم فزار متلب مورد ارزیابی قرار گرفت. همچنین سیستم ارائه شده بر روی سه گروه از نمایندگان صنعت بیمه نیز پیاده‌سازی گردید. نتایج این پژوهش نشان داد که مدل ارائه‌شده توانمندی بالایی در سنجش عوامل کلیدی توانمندسازی نمایندگان بیمه دارد و می‌تواند فرآیند تصمیم‌گیری در مدیریت شبکه فروش بیمه را به شکل مؤثر پشتیبانی کند. انعطاف‌پذیری و قابلیت تفسیرپذیری سیستم آن را به ابزاری کاربردی برای بهبود عملکرد نمایندگان و شرکت‌های بیمه تبدیل کرده و امکان اجرای راهبردهای توانمندسازی در سطوح عملیاتی و استراتژیک را فراهم می‌سازد.

Abstract Inventory management of seasonal products with uncertain quality involves challenging decisions under simultaneous demand and quality uncertainty, while most existing approaches, after inspection, still rely on a rigid binary “accept-or-return” rule. This study develops a three-state threshold newsvendor model for a seasonal perishable product, in which inspection errors, customer returns, logistical limits on replacement, and two-channel discount-sensitive demand are incorporated in an integrated way, and the supplier’s participation constraint is explicitly enforced in designing the optimal policy. Owing to nonlinearity and the presence of min/max operators, the expected profit function is not analytically tractable; therefore, a combined simulation–optimization framework is adopted, where expected profit is estimated via Monte Carlo simulation and the optimal policy is obtained using Bayesian optimization and benchmarked against standard metaheuristics. Numerical results show that, relative to the accept/return policy, activating the replacement mechanism within the three-state framework increases the retailer’s expected profit by about 14.3% and the supplier’s profit by about 6.2%, while simultaneously reducing shortages and waste significantly. Scenario analysis under critical conditions indicates that model performance is robust to parameter changes and that managed replacement acts as an effective risk-hedging instrument. Sensitivity analysis further reveals that, in addition to purchase and selling prices, operational variables such as replacement effectiveness and inspection accuracy have a direct impact on shortages and waste. These findings suggest that moving from traditional full-return contracts toward “smart” contracts embedding a capped replacement mechanism can substantially improve both profitability and operational performance in seasonal perishable supply chains.

Abstract The disparity between the official exchange rate and the market rate has created opportunities for currency manipulators to exploit the system. On the other hand, importers of goods are evaluated by the national banking network regardless of their past foreign exchange and financial performance, and collateral is required from them in the form of domestic currency. The main objective of this study is to design an intelligent deep learning model for risk assessment and collateral determination for importers, in such a way that the final model, with the highest level of accuracy and precision, is capable of analyzing large-scale performance data. For this purpose, operational data were first clustered using the K-means method, and the results of the clustering were then used as input for classification models including Random Forest, XGBoost, and Keras Sequential neural networks. The performance of each model was evaluated using the F-measure index. Finally, the combined K-means–RNN model was selected as the best-performing model with the highest accuracy and precision. Using this model, customers were classified into three risk categories, and an optimal mix of cash and non-cash collateral was determined for each group.
The findings indicate that the proposed model is capable of effectively classifying customers based on their performance history and can serve as a robust tool for credit and foreign exchange risk management in the banking sector.

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 In the Iranian pharmaceutical industry, distributors widely rely on trade credit as the primary demand-management instrument to preserve market share. However, unplanned and excessive extensions of credit periods have become a major source of pharmacy liquidity crises, sharp reductions in order volumes, occasional bankruptcies, and, ultimately, diminished patient access to essential medicines. This study develops an optimal trade-credit coordination model and demonstrates that scientifically calibrating the credit duration, using this prevalent industry practice, can substantially alleviate financial distress. The model is formulated within a Stackelberg leader–follower framework and incorporates temperature-dependent Weibull deterioration to capture the cold-chain constraints of pharmaceutical products. Concave fractional programming is employed to establish that the annual total profit function is strictly pseudo-concave, thereby guaranteeing the existence and uniqueness of a global optimal solution for feasible contract parameters. The solution methodology combines an efficient iterative search algorithm with exhaustive brute-force validation implemented in Python. The proposed approach requires no additional financial resources or regulatory changes and can be readily implemented using existing industry practices. Numerical results indicate profit improvements of up to 19.7% and more than a threefold increase in order quantity relative to the decentralized baseline. Overall, the framework provides distributors and pharmacies (retailers) with a practical, cost-free means of transforming a common demand-management instrument into an effective coordination mechanism that enhances profitability, cash-flow stability, and patient access to medicines.

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.