Journal of Applied Research on Industrial Engineering
Volume:9 Issue: 3, Summer 2022

Because of the dissemination of Impulse Buying (IB) behavior in consumers its academic studies have increased over the last decade. Because in large stores, sales have to be increased, the behavior of consumers in IB to be taken into account by the researchers and managers of the stores. The purpose of this paper is to model agent-based the IB behavior of consumers (customers), with regards to the factors of discount and swarm in the purchase. In terms of executive purpose and with Agent-Based Modeling (ABM) approach, the present paper examines the existing reality of consumer IB behavior. This paper develops consumption models, examines and analyzes Consumer Behavior (CB) under the NetLogo software environment. In comparing the optimal points of discounts and sales volume in both discount and swarm-discount functions that lead the stores to maximize profits and sales volume simultaneously, it can be debated that with running this model (swarm-discount) stores would be gaining more sales by less discounts. Results could describe customer behavior by implementing discount and swarm factors. Understanding the customer behavior prepared the comparing possibility of customer behavior in store in each introduced mathematical model. The contributions could be considered in two points of view. On the applicable view, this research can provide the managers and decision makers with significant information, includes possibility of forecasting sales volume and incomes of any policies in stores, so the comparing of policies and strategies analysis would be possible. This method is rather less expensive, because of virtual environment nature. Users of this model can study other sections by changing the research assumptions.

In this paper, optimal control problem is applied to Human Papillomavirus (HPV) and Herpes Simplex Virus type 2 (HSV-2) coinfection model formulated by a system of ordinary differential equations. Optimal control strategy is employed to study the effect of combining different intervention strategy on the transmission dynamics of HPV-HSV-II coinfection diseases. The necessary conditions for the existence of the optimal controls are established using Pontryagin’s Maximum Principle. Optimal control systems were performed with help of Runge-Kutta forward-backward sweep numerical approximation method. Finally, numerical simulation illustrated that a combination of all controls is the most effective strategy to minimize the disease from the community. The results shows that the size of infectious population are minimized by using different control strategies.

In Home Health Care (HHC) operations, one of the major aims of HHC centers is to timely meet patients' demands. According to the feedback from HHC centers, their decision-makers deal with some complexity in scheduling and routing of their health workers. Inspired by this point, the present research introduces a new HHC routing and scheduling problem considering different skill levels of health workers and different levels of patients’ needs. So, in such a condition, a highly qualified health worker can visit those patients who need lower-skilled demands while a low-qualified health worker cannot visit those who request higher skills. In this way, the total cost of the system will be lower compared to the situation in which the patients' needs exactly match the health workers' skills. Moreover, we consider that the maximum number of homes each health worker is tasked to visit during the day is specified and if more patients than this specified limit are assigned to each health worker, an additional cost will be imposed on the center in proportion to the excess number of patients. Since patient satisfaction, which is obtained with timely visits, is important for each HHC center, a hard time window is considered for each patient. The presented model is solved using the GAMS software with the CPLEX solver. Along with the MIP approach, a metaheuristic algorithm based on a Simulated Annealing (SA) algorithm is adopted to solve the problem. The results give the managers insight into this method of cost management in comparison with manual and traditional traditional planning. This study may help the decision-makers of HHC centers make more accurate decisions which, in turn, result in timelier service provision, increase the patients' satisfaction level, and improve the overall efficiency of HHC centers.

Human life today is intertwined with abundant trade and economic exchanges, and life would not be possible without trade and commerce. One of the main pillars of financial exchanges are banks and financial and credit institutions, which, as the vital arteries of the economy, are responsible for transferring funds and keeping the economy alive. In the world of economic competition between organizations, profitability and proper performance for stakeholders are the basic principles of the organization's survival. To increase profitability, banks must take measures that, in addition to reducing costs, increase the level of service and customer satisfaction. The best way to do this is to use new technologies and orient the bank's policies to provide services in person and independent of time and place. The use of new technologies in the banking system sometimes leads to customers' distrust and distrust of the bank. Therefore, solutions to detect fraud in banking transactions should be provided. This article aims to discover a model for face-to-face transactions and to establish a system to block fraudulently issued transactions. Therefore, a big data clustering method is designed to timely identify bribery in banking transactions. The results show that using the big data clustering method in the fastest time can detect and stop possible fraud in customers' banking transactions.

One of the most important issues in financial, economic, and accounting matters is the phenomenon of bankruptcy and its prediction. There is presented a hybrid method of Genetic Algorithm (GA) and Adaptive Neural-Fuzzy Network (ANFIS) model to evaluate predicting the bankruptcy of companies listed on the Tehran Stock Exchange. The statistical population of this research is the successful and bankrupt manufacturing companies in Tehran Stock Exchange and in this research, there is a different way as opposed to previous and purposeful research and all companies can prevent their possible bankruptcy with accurate forecasting. In this way, the statistical population includes 136 companies consisting of bankrupt and non-bankrupt companies. In order to construct prediction models, four variables were first selected: 1) independent sample t-test, 2) Correlation Matrix (CM), 3) Step-by-step Diagnostic Analysis (SDA), and 4) Principal Component Analysis (PCA). The final financial ratios were selected from 19 financial ratios that using selected financial ratios and a hybrid model of ANFIS and GA and the results of the proposed model and its comparison with the hybrid model of GA and Group Method of Data Handling (GMDH) shows the high capability of the proposed GA-ANFIS model in bankruptcy prediction modeling and its superiority over Group Method of Data Handling with GA-GMDH method. The results also show that the CM-GA-ANFIS model is known as the best model for predicting bankruptcy of companies listed on the Tehran Stock Exchange. The main reason for choosing the model (GA-ANFIS) is that in addition to the fact that for the first time a combination of two methods ANFIS and GA is used to predict the bankruptcy of companies, and also in none of the studies conducted in both areas which further highlights the need for the present study.

The present research aims to design a model for the creation and development of knowledge-based cooperative companies in Semnan province by a mixed qualitative-quantitative approach. The qualitative approach was based on grounded theory and the quantitative approach was based on structural equation analysis. In terms of purpose, this research is an applied study, and in terms of method, it is a descriptive survey study. The population includes the knowledge-based companies of Semnan province. Out of the mentioned data collection was done by library studies and semi-structured interviews in the qualitative phase and questionnaire in the quantitative phase. In the qualitative phase, the factors and the data obtained from the interviews were analyzed by Atlas. ti8 and grounded theory coding proposed by Strass and Corbin. The components and indicators of the creation and development of knowledge-based cooperative companies were identified on this basis. In the quantitative phase, Lisrel software and IBM SPSS statistics. The 26 software were used to apply the interpretive structural equation for developing the final research model. The findings include the indicators of components of creation, and development of knowledge-based cooperative companies in Semnan province and the model proposed for this purpose. The most important of these components: education and research, technology, management strategies and policy-making, new platforms and infrastructures, expansion of knowledge application, knowledge-based innovation and creativity.

One of the critical concerns in the audit court is to study budgetary deviations of the executive organizations. Audit court seeks methods to evaluations the executive organizations based on their budget deviations. The aim of this study is to rank executive agencies aimed at the improvement of their performance. We use a ranking method based on data envelopment analysis that can simultaneously use multi-indexes for ranking and we use budget split indexes of the audit court for ranking of executive organizations. The results enable managers to identify the best and worst executive agencies based on the considered indexes of the budget split of the audit court. The objectives of this paper are to investigate which executive organizations have more budget deviations. Any organization that had a lower rank shows that it has based on the indexes under evaluation more deviation. To study the performance process of each of the executive agencies, we collected data for two years and analyzed the performance of the executive agencies during these two years.

5S is an important industrial engineering technique which is used worldwide in a wide range of industrial and service type organizations for workplace management. Improvement in efficiency and productivity, and reduction in waste and idle time etc. are some of its benefits. This paper presents a fundamental understanding of 5S technique and review of some important past work on implementation of 5S in various organizational setups. It is worth mentioning that safety has been identified as to be included as the 6thS under this technique. The main aim of this paper is to facilitate scholars, researchers, and engineers of industrial engineering field by providing knowledge and develop understanding of 5S technique so that they may further implement it in various scenarios of the workplace organization.

Demand forecasting can have a significant impact on reducing and controlling companies' costs, as well as increasing their productivity and competitiveness. But to achieve this, accuracy in demand forecasting is very important. On this point, in the present study, an attempt has been made to analyze the time series related to the demand for a type of women's luxury handbag based on a framework and using machine learning methods. For this purpose, five machine learning models including Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN), Discrete Wavelet Transform-Neural Networks (DWTNN), and Group Model of Data Handling (GMDH) were used. The comparison of the models was also based on the accuracy of the forecasting according to the values of forecasting errors. The RMSE, MAE error measures as well as the R, correlation coefficient were used to assess the forecasting accuracy of the models. The RBFNN model had the best performance among the studied models with the minimum error values and the highest correlation value between the observed values and the outputs of the model. But in general, by comparing the error values with the data range, it is concluded that the models performed reasonably well.