Due to the importance of public transportation, which has become today an important urban problem with special conditions, this study was conducted aimed at determining the parameters affecting the mathematical programming model of the appropriate transportation system. The study was performed by modeling method, and the problem was solved in three different sizes (small, medium and large) to show the efficiency of the model and the proposed solution methods. Some common test problems in the literature were selected and the required characteristics of the problem under study were defined for them as needed, since there are no sample test problems in the literature to examine the performance of the developed algorithms. The results indicated that some of the most important parameters affecting the designed mathematical model were movement risks, fatigue and drowsiness while driving, demands at the source node, demands at the destination node, number of vehicles across the network, the time interval between requests, and the time interval between two consecutive requests. The mentioned parameters were so effective that little changes in parameters such as demands at the source node, demands at the destination node, and the number of vehicles across the network significantly increased time and decreased profitability. the company has a significant effect on the debt cost rate and capital cost rate and does not have a significant effect on stock prices. Finally, the share price shows little sensitivity to the company's financial structure.
Nowadays, information-making is growing rapidly due to the developments in technology; thus, it is critical to maintain information and establish integrated systems for storage and office automation in any organization; thereby, the establishment of datacenters has become a requirement for them. Despite offering models related to some parts of the data centers, there are still ambiguities in identifying the influential and key variables. This study aimed at offering a dynamic model for the parts related to the attractiveness of data center services by using Vensim software. Five reinforcing loops and seven balancing loops were extracted through studying the review of literature; then, the relevant stock and flow variables have been defined. Afterwards, the model is presented in the form of cause and effect model and the stock-flow model. Also, various scenarios have been studied, including increasing the training capacity and attractiveness, imposing an overload on the network and switch failure, upgrading the bandwidth carrying capacity, reducing the maximum maintenance. According to the results and sensitivity analysis done in this research, it can be said that the attractiveness of services, the acquired skills, and the maximum feasible maintenance along with the bandwidth carrying capacity, storage, and processing power of the data centers is so vital in determining the performance level of data centers and the attractiveness of services.
It is of special importance to procure medicines as strategic commodities . The purpose of this study is to manage a drug supply network using a non-radial data envelopment analysis (DEA) model. This study is conducted in private hospitals in Tehran. In this regard, a drug supply chain at three levels of suppliers, distributors, and consumers (pharmacies and hospitals) is examined. Indicators and limitations of the network are identified and then the appropriate model is formulated using mathematical equations on the basis of a non-radial DEA model. In order to validate the proposed model, data related to 30 hospitals during 2019 and 2020 is evaluated. According to this model, 5 hospitals had efficient supply networks and 25 hospitals had deficient supply networks. The proposed new model is able to evaluate the multilevel supply network and intermediate components in hospitals. The problems of the drug supply network are due to the increase in demands for the time period under consideration and the existence of inconsistencies in the drug supply chain in hospitals, which has led to an increase in inefficient hospitals.
This study aims to investigate the factors influencing the formation of organic services marketing in the tourism industry. According to the research conducted by the researcher, organic services are a new definition in the service industry. Organic services should represent high quality and health, just as organic products do. This research was conducted using a mixed qualitative and quantitative approach. The grounded theory was used in the qualitative part, through which the concepts and categories were identified in three stages of open, axial, and selective coding. Data were collected using in-depth and semi-structured interviews. The snowball sampling was used, reaching saturation with a total of 13 experts in the ecotourism and nature tourism industry. Five subcategories of influencing factors on the axial phenomenon were obtained from the qualitative method. The axial phenomenon had four subcategories itself. The results obtained from the qualitative part were examined using the quantitative method of structural equations. All five causal factors (climatic attractiveness and novelty, abundance of traditional and rural houses, experience of silence and peace, enjoyment and experience of health tourism, and people companionship and synergy of tourism) affected the axial phenomenon of the tourism industry significantly and positively.
Every company should think about producing new goods, the production of alternative goods is necessary in order to stabilize the level of sales or new sales; Customers also want new products, and if the company neglects to do so, competitors will certainly do so. Therefore, the present article seeks to provide a model appropriate to the conditions of gas refining companies for product development. In this study, the qualitative research method has been used as a grand theory; Using theoretical sampling and theoretical saturation, 20 experts of gas refining companies (Parsian, South Pars, Fajr Jam) were interviewed. Based on the basic theory, the data were analyzed in three stages of coding (open, axial and selective) using MAXQDA software. In the open coding phase, the data were labeled conceptually and then those that were common to each other were named into a category; the obtained categories were 43 categories. In the central coding stage, logical relationships were found between the categories and the obtained categories were included in the open coding under more general categories; finally, in the selective coding stage, all categories were summarized in one main category. The main category of the research was named "Interfering factors in new product development". The final result of this research, according to the researcher's attention and reflection on the details of the process of each of the three refining companies, is to provide an existing common model for all three companies and also to provide a common desired model for all three companies. In the open coding phase, the data was labeled conceptually and then those that were common to each other were named into a category; The obtained categories were 43 categories. In the central coding stage, logical relationships were found between the categories and the obtained categories were included in the open coding under more general categories; Finally, in the selective coding stage, all categories were summarized in one main category. The main category of the research was named "Interfering factors in new product development". The final result of this research, according to the researcher's attention and reflection on the details of the process of each of the three refining companies studied, providing a common model for all three companies and also providing a common model for all three companies.
Every company should think about producing new goods, the pAdvances in communication technology have led to changes in business practices. Shared businesses are a new way of presenting products through a platform in the virtual environment. In this business, people's assets are shared on a peer-to-peer basis. In addition to generating revenue, this business has created employment, increased productivity, reduce energy consumption and environmental pollution in the world. This study tries to investigate the barriers to the development of shared businesses in Tehran. The research method in this study is qualitative and the data collection tool is a semi-structured interview. The informed statistical population includes 30 university lecturers, elites, graduates, entrepreneurs and manufacturers. The subjects of the recorded interviews were extracted by the lecturers of the Business Management Department and analyzed using the network themes method in MAXQDA software. Findings show that 64 barriers have been identified as the basic themes and after reviewing the opinions of experts, these barriers have been classified into 6 main themes. These barriers are: trust barriers, technology barriers, knowledge and information barriers, economic barriers, political barriers and managerial barriers.
The issue of proper career path development is important according to the knowledge management model for employees in many risky businesses in the country, especially since the outbreak of coronavirus and international sanctions against our country, so this study aims to design a model of progress. The career path was based on the knowledge management model in start-up businesses. The statistical population studied in this study consists of 877 people working in new businesses. Statistical sample size 269 people were selected through random sampling method. For statistical analysis, SPSS software version 20 and pls3 were used and the alpha level was considered 05p 05 0.05. The results showed that the value of t in all paths between the research factors is equal to and greater than 2.58. As a result, there is a significant relationship between the main factors and their sub-factors in the conceptual-analytical model of the research and also the results showed that the fit of the general research model is appropriate and strong. In general, it can be said that between the first step of the career path with the second step of the career path, between the second step of the career path with the third step of the career path and between the third step of the career path with the fourth step of the career path of employees There is a direct and significant relationship between start-up business employees in the conceptual-analytical model of career path.
Being aware of the waiting time for selling residential units is one of the important issues in the housing sector for the majority of people, especially investors. There are several factors affecting the waiting time for selling residential units. Determining the influential factors on the time period of selling real estates can lead to an informed decision making by real estate consultants, sellers as well as those seeking to buy real estates. Using a real estate database in Iran, the present paper proposes a two-module procedure. The first module deals with implementation of association rule mining. Using the well-known association rule mining techniques namely FP-Growth, several association rules have been extracted which indicate the effective factors on the waiting time for selling residential units. Generated association rules have been evaluated based on metrics such as support, confidence and lift and finally the best rules are selected. The main objective of the second module is to develop a fuzzy inference system which can determine the factors influencing the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments. In conclusion section, a number of suggestions for future studies are presented. For example, machine learning algorithms such as neural networks, decision trees, etc. can also be used to predict the duration of residential units’ sale. The main objective of the second module is to develop a fuzzy inference system which can learn about the factors that influence the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it takes to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments.
In recent decades, mentoring has been hailed as an important human resource management strategy, a career tool, and a learning activity in the workplace, and has played an important role in enhancing the positive outcomes of a high-performance work system. Therefore, the purpose of this study is to provide a model of mentoring system in the Ministry of Interior based on a mixed approach. First, based on the data theory of the foundation, 12 experts were interviewed. Sampling was done purposefully with a network of experts and continued until the theoretical saturation was reached. According to the research model, causal, pivotal, contextual, interventionist, strategic and consequences variables were identified. The results showed that mentoring will have consequences such as increasing knowledge sharing, organizational performance excellence, productivity, job satisfaction, psychological well-being, client satisfaction, increasing public trust in the organization, increasing trust in government and improving political understanding. For quantitative validation of the model and study of component relationships, the structural equation modeling approach was used using the partial least squares method using smart-PLS software. The statistical sample of this department was 237 managers and experts of the Ministry of Interior. The results of the quantitative step showed that the relationships in the model with appropriate impact coefficients were confirmed.
The purpose of this paper is to prepare a hybrid simulation model of system dynamics (SD) - agent-based modeling (ABM) to investigate the mobile service consumers’ behavior in the country. By using the suggested in this article, first the necessity to analysis consumers’ behavior is explained and then the effective factors on mobile ecosystem which influence the consumers’ behavior are explained . For each factor affecting on the ecosystem, related simulations were performed and then by combining system dynamics modeling - agent -based modeling, the behavior of mobile service consumers in the first operator of the country (Hamrahe Aval Company) was examined and finally the income affected by that behavior is analyzed . Results indicated that if no funds by the operator are allocated to the development of native applications and digital platform, after 2 years, the number of active customers of the operator will decrease because of the activities of cultural organizations. By the operator entering into the field of native application production and digital transformation, consumers tended to use more data services instead of voice calls, but because of different data and voice tariffs, the operator's income will not change much in the next 2 years. With the increase of marketing and advertising activities, despite the greater consumer inclination to use data, the main income of the operator decreases, which also needs more attention of policymakers in this area .