Sharif University of TechnologySharif Journal of Industrial Engineering & Management2676-474141120250823The Impact of Marketing Capabilities on Export Performance by Considering the Role of Competitive Advantage Using the Structural Equations Method (Case Study: Food Export Companies in Tehran Province)The Impact of Marketing Capabilities on Export Performance by Considering the Role of Competitive Advantage Using the Structural Equations Method (Case Study: Food Export Companies in Tehran Province)1112364310.24200/j65.2023.61972.2346FATahaKeshavarzDepartment of Industrial Engineering, Semnan University, Semnan, Iran.Mohammad RezaAmuzadehDepartment of Industrial Engineering, Semnan University, Semnan, Iran.Journal Article20230306The purpose of this study is to investigate the impact of marketing capabilities on export performance by considering the role of competitive advantage in food industry export companies. The research is classified as applied from the point of view of purpose and descriptive from the point of view of method and nature. Most of the previous studies in the field of export performance have examined the relationship between capabilities and performance, but not much research has been done on marketing capabilities and their impact on competitive advantage. Another category that plays an important role in export performance and is closely related to marketing communications is distribution channels in foreign markets. Based on this, deciding on the method of transferring the goods to the place of purchase or consumption is one of the important decisions faced by managers of export companies; Because finding a suitable distribution channel in international markets is so complicated. In this study, the statistical population is food industry export companies in Tehran province and the respondents to the questionnaire are CEOs and senior managers of the commercial and export departments of these companies. After collecting the questionnaires, the data is analyzed by SPSS, and SmartPLS softwares. The validity of the questionnaire has been examined through content and structure validity, as well as its reliability through Cronbach's alpha test and composite reliability. The results show that the questions have high reliability. In terms of structure validity, which was analyzed by SmartPLS software, it is determined that all the questions have good validity. Finally, the results of data analysis and hypothesis testing show that: marketing intelligence, pricing, marketing communications, distribution, and innovation have an impact on competitive advantage. Also, the results show that competitive advantage affects export performance. Meanwhile, the mediating role of competitive advantage in the relationship between the mentioned components and export performance is confirmed. According to the relationships between research variables, better planning can be done to improve export performance and gain a competitive advantage.The purpose of this study is to investigate the impact of marketing capabilities on export performance by considering the role of competitive advantage in food industry export companies. The research is classified as applied from the point of view of purpose and descriptive from the point of view of method and nature. Most of the previous studies in the field of export performance have examined the relationship between capabilities and performance, but not much research has been done on marketing capabilities and their impact on competitive advantage. Another category that plays an important role in export performance and is closely related to marketing communications is distribution channels in foreign markets. Based on this, deciding on the method of transferring the goods to the place of purchase or consumption is one of the important decisions faced by managers of export companies; Because finding a suitable distribution channel in international markets is so complicated. In this study, the statistical population is food industry export companies in Tehran province and the respondents to the questionnaire are CEOs and senior managers of the commercial and export departments of these companies. After collecting the questionnaires, the data is analyzed by SPSS, and SmartPLS softwares. The validity of the questionnaire has been examined through content and structure validity, as well as its reliability through Cronbach's alpha test and composite reliability. The results show that the questions have high reliability. In terms of structure validity, which was analyzed by SmartPLS software, it is determined that all the questions have good validity. Finally, the results of data analysis and hypothesis testing show that: marketing intelligence, pricing, marketing communications, distribution, and innovation have an impact on competitive advantage. Also, the results show that competitive advantage affects export performance. Meanwhile, the mediating role of competitive advantage in the relationship between the mentioned components and export performance is confirmed. According to the relationships between research variables, better planning can be done to improve export performance and gain a competitive advantage.https://sjie.journals.sharif.edu/article_23643_16b031f2d900d91ae2a02bc876a12692.pdfSharif University of TechnologySharif Journal of Industrial Engineering & Management2676-474141120250922Designing a Decision Support System for Portfolio Management using Data Science Methods (Tehran Stock Exchange)Designing a Decision Support System for Portfolio Management using Data Science Methods (Tehran Stock Exchange)12372364610.24200/j65.2024.63158.2375FANavidJavaheriDepartment of Industrial Engineering, Faculty of Engineering, Meybod University, Meybod, Iran.NajmehNeshatDepartment of Industrial Engineering, Faculty of Engineering, Meybod University, Meybod, Iran.AbbasaliJafari NodoushanDepartment of Industrial Engineering, Faculty of Engineering, Meybod University, Meybod, Iran.Journal Article20221122Increasing profitability and reducing risk always requires choosing a smart investment path while taking advantage of data analysis; Therefore, it is necessary to provide a technique in the form of a decision support system for stock portfolio management while better understanding the position of data science. In this research, while combining data science methods with the Markowitz model, classification and forecasting models have also been created. Detecting financial fraud of companies is also effective; Hierarchical and Cummins algorithms have also been used in order to cluster active companies in the Tehran Stock Exchange. In terms of data classification, the linear support vector machine classification algorithm has been identified with 70% accuracy in comparison with the decision tree, Nyobies, nearest neighbour, and multilayer perceptron algorithms, and in terms of building prediction models, the decision tree with the minimum amount of error, in comparison with Nyobies algorithms, is the closest Neighbor, multilayer perceptron has been identified. The primary data in the current research includes twenty titles of financial indicators and daily data of stock prices of companies in the Python programming space. It was concluded that choosing stocks from among the clusters formed by different stocks of companies will reduce the risk of diversifying the stock portfolio. The mentioned system will be able to generalize as a comprehensive model for data in different time intervals and the indicators desired by the analyst, thus helping investors in a fast and accurate analytical way. The performance of the final technique will be such that the investor first selects several desired companies according to the heard, analysis, and news; It predicts the price performance of companies; Then, it separates the companies that have succeeded in accepting the condition by using the clustering model and separates the portfolio of various stocks. It forms according to the amount of risk and expected return. Importantly, as mentioned, the presented classification and forecasting models, in addition to being used in the formation and management of the stock portfolio, will be able to be effective in predicting the possible fraud of companies.Increasing profitability and reducing risk always requires choosing a smart investment path while taking advantage of data analysis; Therefore, it is necessary to provide a technique in the form of a decision support system for stock portfolio management while better understanding the position of data science. In this research, while combining data science methods with the Markowitz model, classification and forecasting models have also been created. Detecting financial fraud of companies is also effective; Hierarchical and Cummins algorithms have also been used in order to cluster active companies in the Tehran Stock Exchange. In terms of data classification, the linear support vector machine classification algorithm has been identified with 70% accuracy in comparison with the decision tree, Nyobies, nearest neighbour, and multilayer perceptron algorithms, and in terms of building prediction models, the decision tree with the minimum amount of error, in comparison with Nyobies algorithms, is the closest Neighbor, multilayer perceptron has been identified. The primary data in the current research includes twenty titles of financial indicators and daily data of stock prices of companies in the Python programming space. It was concluded that choosing stocks from among the clusters formed by different stocks of companies will reduce the risk of diversifying the stock portfolio. The mentioned system will be able to generalize as a comprehensive model for data in different time intervals and the indicators desired by the analyst, thus helping investors in a fast and accurate analytical way. The performance of the final technique will be such that the investor first selects several desired companies according to the heard, analysis, and news; It predicts the price performance of companies; Then, it separates the companies that have succeeded in accepting the condition by using the clustering model and separates the portfolio of various stocks. It forms according to the amount of risk and expected return. Importantly, as mentioned, the presented classification and forecasting models, in addition to being used in the formation and management of the stock portfolio, will be able to be effective in predicting the possible fraud of companies.https://sjie.journals.sharif.edu/article_23646_43dd85640a4f0039ed64e5c590e65158.pdfSharif University of TechnologySharif Journal of Industrial Engineering & Management2676-474141120250922Locating Telecommunications Masts for Mobile Network Coverage Considering Sustainable Development Indices by Using GIS and BWM Technique: Case Study of Urmia CountyLocating Telecommunications Masts for Mobile Network Coverage Considering Sustainable Development Indices by Using GIS and BWM Technique: Case Study of Urmia County28382374810.24200/j65.2024.63622.2384FARonakAbbasiDepartment of Industrial Engineering, Technical and Engineering Faculty, Urmia University, IranSaeedFazayeliDepartment of Industrial Engineering, Technical and Engineering Faculty, Urmia University, IranRezaBabazadehDepartment of Industrial Engineering, Technical and Engineering Faculty, Urmia University, IranJournal Article20240103Optimum location for the establishment of mobile phone towers with the aim of covering all demand with high signal quality and low cost is an important challenge for telecommunications companies. Therefore, if a proper decision is not taken, it may lead to environmental damage, economic problems, and user dissatisfaction. Therefore, operators of telecommunication networks should plan for the optimal location of mobile telephone towers in order to achieve the goals of sustainable social, economic, and environmental development. The purpose of this research is to show how to use ArcGis10.8 software tools and the best-worst technique for the optimal location of mobile phone towers using sustainable development criteria. These criteria are: distance from the fault, distance from historical and cultural places, distance from parks, distance from the river, distance from medical centers (hospitals and clinics), distance or proximity from rural areas, proximity to the main road, distance from residential areas, distance from schools and educational centers, height, land slope, and slope direction. The innovations used in this research include the use of multi-criteria spatial modeling, including the best-worst technique and its integration with geographic information system techniques for locating mobile phone towers and investigating the optimal location for their deployment. along with the application of new criteria of sustainable development, which was done for the first time in Urmia city. The weights of the criteria were obtained by the best-worst technique, and the data required for weighting were collected by interviewing experts who were experts of the telecommunications company. The data related to each quality was entered into the ArcGis10.8 software, and various processing and fuzzy overlay operations were performed to prepare the optimal final map. The general results obtained showed that it is not possible to use the masts in a wide area from the west of the county, and the best points go towards the eastern areas near Lake Urmia, which can be due to the selection of the criteria.Optimum location for the establishment of mobile phone towers with the aim of covering all demand with high signal quality and low cost is an important challenge for telecommunications companies. Therefore, if a proper decision is not taken, it may lead to environmental damage, economic problems, and user dissatisfaction. Therefore, operators of telecommunication networks should plan for the optimal location of mobile telephone towers in order to achieve the goals of sustainable social, economic, and environmental development. The purpose of this research is to show how to use ArcGis10.8 software tools and the best-worst technique for the optimal location of mobile phone towers using sustainable development criteria. These criteria are: distance from the fault, distance from historical and cultural places, distance from parks, distance from the river, distance from medical centers (hospitals and clinics), distance or proximity from rural areas, proximity to the main road, distance from residential areas, distance from schools and educational centers, height, land slope, and slope direction. The innovations used in this research include the use of multi-criteria spatial modeling, including the best-worst technique and its integration with geographic information system techniques for locating mobile phone towers and investigating the optimal location for their deployment. along with the application of new criteria of sustainable development, which was done for the first time in Urmia city. The weights of the criteria were obtained by the best-worst technique, and the data required for weighting were collected by interviewing experts who were experts of the telecommunications company. The data related to each quality was entered into the ArcGis10.8 software, and various processing and fuzzy overlay operations were performed to prepare the optimal final map. The general results obtained showed that it is not possible to use the masts in a wide area from the west of the county, and the best points go towards the eastern areas near Lake Urmia, which can be due to the selection of the criteria.https://sjie.journals.sharif.edu/article_23748_2c2436e80a7d032391da485d7d3783c6.pdfSharif University of TechnologySharif Journal of Industrial Engineering & Management2676-474141120250922Investigating Big Data Investment in a Three-Level Green Supply Chain: A Game Theoretic ApproachInvestigating Big Data Investment in a Three-Level Green Supply Chain: A Game Theoretic Approach39562364810.24200/j65.2024.63579.2380FAZahraEsmaeeliDepartment of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, IranNaserMollaverdiDepartment of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, IranSoroushSafarzadehDepartment of Industrial Engineering, Faculty of Engineering Science, Quchan University of Technology, Quchan, IranJournal Article20240120In recent decades, the rapid and remarkable advancements in technology have sparked significant interest in the potential of big data and the extraction of valuable insights and information from it. This has led to a surge in the utilization of big data analytics across various industries, including supply chain management. In this paper, our focus is on evaluating the dynamics of a three-level supply chain, which encompasses a retailer, manufacturer, and supplier, within different power structures. Notably, the members of this supply chain have made substantial investments in big data analytics and are reaping the benefits of these investments accordingly.<br /> <br />In our quest to gain a deeper understanding of the feasibility conditions and factors that impact big data investment within different power structures, we have meticulously examined the problem model in two distinct cases: one with big data investment and another without it. Our findings reveal that the direct impact of investment efficiency and cost improvement coefficient plays a significant role in determining the feasible limits of big data investment for all members of the supply chain. Furthermore, our research demonstrates that big data investment has a positive and far-reaching effect on the equilibrium profit of members. This leads to an increase in the equilibrium price of products, as well as an enhancement in the equilibrium level of green innovation and product quality across all power structures.In recent decades, the rapid and remarkable advancements in technology have sparked significant interest in the potential of big data and the extraction of valuable insights and information from it. This has led to a surge in the utilization of big data analytics across various industries, including supply chain management. In this paper, our focus is on evaluating the dynamics of a three-level supply chain, which encompasses a retailer, manufacturer, and supplier, within different power structures. Notably, the members of this supply chain have made substantial investments in big data analytics and are reaping the benefits of these investments accordingly.<br /> <br />In our quest to gain a deeper understanding of the feasibility conditions and factors that impact big data investment within different power structures, we have meticulously examined the problem model in two distinct cases: one with big data investment and another without it. Our findings reveal that the direct impact of investment efficiency and cost improvement coefficient plays a significant role in determining the feasible limits of big data investment for all members of the supply chain. Furthermore, our research demonstrates that big data investment has a positive and far-reaching effect on the equilibrium profit of members. This leads to an increase in the equilibrium price of products, as well as an enhancement in the equilibrium level of green innovation and product quality across all power structures.https://sjie.journals.sharif.edu/article_23648_946cc4aa522b10822c0fb76bcc477d5c.pdfSharif University of TechnologySharif Journal of Industrial Engineering & Management2676-474141120250922Analysis of Customers' Lifetime Value with a Clustering Approach Based on Som Artificial Neural Networks and Markov Chain (Case Study: Pasargad Digital Bank)Analysis of Customers' Lifetime Value with a Clustering Approach Based on Som Artificial Neural Networks and Markov Chain (Case Study: Pasargad Digital Bank)57722378310.24200/j65.2024.63417.2379FAMaralMirzaei MoradiFaculty of Industrial Management, University of Tehran, Kish International Campus, IranSeyed HossRazavi HDepartment of Management, Faculty of Management and Financial Sciences, Khatam University, Tehran, IranHannanAmoozad MahdirajiDepartment of Technology and Innovation Management, Faculty of Industrial and Technology Management, University of Tehran, Tehran, IranAliAlikhaniDepartment of Industrial Management, Faculty of Management and Social Sciences, Islamic Azad University, North Tehran Branch, Tehran, IranJournal Article20240127Clustering of customers and analysis of their lifetime value is one of the basic strategies of policy making in production and marketing. Since the advertising strategies of many businesses are implemented regardless of the financial behavior clusters of customers and the expected income resulting from the dynamic financial behavior of customers, the advertising policy based on the customers’ lifetime value in the dynamic clusters of their financial behavior can save a lot of advertising and marketing costs to manage the costs imposed on businesses. This is despite the fact that the studies conducted in the field of customer lifetime value analysis have not paid attention to advertising strategies based on lifetime value. The existing methods for determining the lifetime value of customers rely on static clustering of customers' status, while customer clustering based on their financial behavior is a dynamic and time-varying phenomenon. Therefore, in this research, the lifetime value analysis of customers and the optimization of advertising strategy in the dynamic clusters of their financial behavior using a self-organizing neural network and dynamic stochastic programming have been discussed. For this purpose, based on the status of each customer belonging to a cluster during consecutive weeks, a Markov chain is formed from the clusters containing customers, and the lifetime value of customers is based on the transition probability matrices of customers' status from one cluster to another, and it is also estimated based on the expected income of customers in each cluster. The results showed that customer clustering with the self-organizing neural network method can explain at least 91% of the changes in the data. Also, the findings showed that the optimal advertising strategy for each cluster of customers with a certain expected income level will lead to different lifetime value for customers, which shows the importance of dynamic clustering of customers and determining the optimal level of advertising cost in each cluster to gain more customer lifetime value.Clustering of customers and analysis of their lifetime value is one of the basic strategies of policy making in production and marketing. Since the advertising strategies of many businesses are implemented regardless of the financial behavior clusters of customers and the expected income resulting from the dynamic financial behavior of customers, the advertising policy based on the customers’ lifetime value in the dynamic clusters of their financial behavior can save a lot of advertising and marketing costs to manage the costs imposed on businesses. This is despite the fact that the studies conducted in the field of customer lifetime value analysis have not paid attention to advertising strategies based on lifetime value. The existing methods for determining the lifetime value of customers rely on static clustering of customers' status, while customer clustering based on their financial behavior is a dynamic and time-varying phenomenon. Therefore, in this research, the lifetime value analysis of customers and the optimization of advertising strategy in the dynamic clusters of their financial behavior using a self-organizing neural network and dynamic stochastic programming have been discussed. For this purpose, based on the status of each customer belonging to a cluster during consecutive weeks, a Markov chain is formed from the clusters containing customers, and the lifetime value of customers is based on the transition probability matrices of customers' status from one cluster to another, and it is also estimated based on the expected income of customers in each cluster. The results showed that customer clustering with the self-organizing neural network method can explain at least 91% of the changes in the data. Also, the findings showed that the optimal advertising strategy for each cluster of customers with a certain expected income level will lead to different lifetime value for customers, which shows the importance of dynamic clustering of customers and determining the optimal level of advertising cost in each cluster to gain more customer lifetime value.https://sjie.journals.sharif.edu/article_23783_05f5f9d86129c53d6c92c2090b18dab4.pdfSharif University of TechnologySharif Journal of Industrial Engineering & Management2676-474141120250922A Novel Method for Designing Glaze Formulation Using Mathematical ProgrammingA Novel Method for Designing Glaze Formulation Using Mathematical Programming73852374910.24200/j65.2024.63797.2387FAHosseinShams ShemiraniIndustrial Engineering Group, Golpayegan College of Engineering, Isfahan University of Technology, Isfahan, IranHamid RezaZahedi NeiestaniIndustrial Engineering Group, Golpayegan College of Engineering, Isfahan University of Technology, Isfahan, IranJournal Article20230827Glaze is one of the fundamental components of ceramic products such as tiles, porcelain dishes, and sanitary ceramics. The formulation of glaze is a critical and complex process, representing one of the most significant and challenging aspects within the ceramic industry. Establishing the optimal formulation for glaze production is crucial for enhancing product quality and minimizing production costs. In this study, grounded in practical experiences from a factory setting, the issue of glaze formulation is examined and analyzed using an operations research approach for the first time globally. The outcomes of the developed model were tested and successfully implemented on the production line. To ensure the practical implementation of this research within the industry and to enhance user accessibility, a user-friendly software was developed. It is crucial to recognize that different factories employ a variety of raw materials in their glaze production processes. This variation arises from factors such as the unavailability of certain raw materials, the elevated costs associated with some materials, and the expenses related to transportation. The chemical composition of raw materials varies significantly, with the percentage of oxides differing based on the specific mine and the section of the mine from which they are sourced. Consequently, determining the optimal proportion of available raw materials for producing a glaze with a specific Seger formula necessitates repeated analysis. This iterative process is essential to identify the appropriate formula, which can then be implemented in the production line. In this study, conducted with real-world data and a thorough understanding of the problem through the production process, as well as an extensive review of relevant literature, the issue of designing a glaze formula is analyzed using operations research methodology. This paper presents a novel approach to glaze formulation, which can serve as a foundation for future research in this area.Glaze is one of the fundamental components of ceramic products such as tiles, porcelain dishes, and sanitary ceramics. The formulation of glaze is a critical and complex process, representing one of the most significant and challenging aspects within the ceramic industry. Establishing the optimal formulation for glaze production is crucial for enhancing product quality and minimizing production costs. In this study, grounded in practical experiences from a factory setting, the issue of glaze formulation is examined and analyzed using an operations research approach for the first time globally. The outcomes of the developed model were tested and successfully implemented on the production line. To ensure the practical implementation of this research within the industry and to enhance user accessibility, a user-friendly software was developed. It is crucial to recognize that different factories employ a variety of raw materials in their glaze production processes. This variation arises from factors such as the unavailability of certain raw materials, the elevated costs associated with some materials, and the expenses related to transportation. The chemical composition of raw materials varies significantly, with the percentage of oxides differing based on the specific mine and the section of the mine from which they are sourced. Consequently, determining the optimal proportion of available raw materials for producing a glaze with a specific Seger formula necessitates repeated analysis. This iterative process is essential to identify the appropriate formula, which can then be implemented in the production line. In this study, conducted with real-world data and a thorough understanding of the problem through the production process, as well as an extensive review of relevant literature, the issue of designing a glaze formula is analyzed using operations research methodology. This paper presents a novel approach to glaze formulation, which can serve as a foundation for future research in this area.https://sjie.journals.sharif.edu/article_23749_89905804bbbf8da5f432697daefa682a.pdfSharif University of TechnologySharif Journal of Industrial Engineering & Management2676-474141120250922Investigating the Performance of Two Stock Price Forecasting Models, Based on a Long Short-Term Memory Neural Network and with Two Different Approaches of Feature Selection and Time Series AnalysisInvestigating the Performance of Two Stock Price Forecasting Models, Based on a Long Short-Term Memory Neural Network and with Two Different Approaches of Feature Selection and Time Series Analysis86972375010.24200/j65.2024.63935.2392FAKeyvanHaghighi NaeiniDepartment of Systems and Productivity Management, Faculty of Industrial Engineering, Tarbiat Modares University, Tehran, Iran.Mohammad AliRastegarDepartment of Systems and Productivity Management, Faculty of Industrial Engineering, Tarbiat Modares University, Tehran, Iran.Journal Article20240429In the financial literature, the capital market plays an essential role in economic growth through the financing of enterprises, the optimal allocation of resources, the improvement of liquidity of assets, the improvement of company management, and the increase of transparency in the economy. One of the most important challenges that shareholders always face in the market is to make the right decision and be in the right position with buying and selling stocks. Stock forecasting that predicts future stock movements and benefits shareholders has been an attractive research area for financial studies and researchers for the past. In this research, we present two models of neural networks that receive inputs and predict price and movement trends, and finally, the performance of these two models is compared. The data studied in this research includes the price data of the 5 largest shares of the New York market during the years 2000 to 2020, in which 80 percent of the initial samples are used as training data and the remaining 20 percent are used as test data.<br /><br />In the proposed VMD-LSTM model, first, the stock price time series is decomposed using the variational mode decomposition algorithm (VMD) into the intrinsic mode functions (IMF), and then each of these IMFs is predicted by the LSTM model and After interpreting the results. In the second proposed method, available features, including price and some of the most important technical indicators, are used to predict stock prices. In the GA-LSTM model, the genetic algorithm is first used to select the best features from the entire set of features. Then, the time series of the stock closing price was predicted by an LSTM network using the selected features. The results of the research showed that because both models are very good in price prediction, the proposed GA-LSTM model, which is developed based on feature selection, has less error and more accurate performance.In the financial literature, the capital market plays an essential role in economic growth through the financing of enterprises, the optimal allocation of resources, the improvement of liquidity of assets, the improvement of company management, and the increase of transparency in the economy. One of the most important challenges that shareholders always face in the market is to make the right decision and be in the right position with buying and selling stocks. Stock forecasting that predicts future stock movements and benefits shareholders has been an attractive research area for financial studies and researchers for the past. In this research, we present two models of neural networks that receive inputs and predict price and movement trends, and finally, the performance of these two models is compared. The data studied in this research includes the price data of the 5 largest shares of the New York market during the years 2000 to 2020, in which 80 percent of the initial samples are used as training data and the remaining 20 percent are used as test data.<br /><br />In the proposed VMD-LSTM model, first, the stock price time series is decomposed using the variational mode decomposition algorithm (VMD) into the intrinsic mode functions (IMF), and then each of these IMFs is predicted by the LSTM model and After interpreting the results. In the second proposed method, available features, including price and some of the most important technical indicators, are used to predict stock prices. In the GA-LSTM model, the genetic algorithm is first used to select the best features from the entire set of features. Then, the time series of the stock closing price was predicted by an LSTM network using the selected features. The results of the research showed that because both models are very good in price prediction, the proposed GA-LSTM model, which is developed based on feature selection, has less error and more accurate performance.https://sjie.journals.sharif.edu/article_23750_201c3d403026d6ccee1f72d02ab20c1b.pdfSharif University of TechnologySharif Journal of Industrial Engineering & Management2676-474141120250823Developing and Assessing New Models for the Free Patrol Vehicle Routing ProblemDeveloping and Assessing New Models for the Free Patrol Vehicle Routing Problem981082375310.24200/j65.2024.64520.2405FAHamedManshadianSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranMohsenSadegh AmalnickSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranSeyed AliTorabiSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranJournal Article20240512Vehicle routing problems are a category of complex mathematical problems in the field of transportation and supply chain management, and a wide variety of these problems have been introduced. One category within this field involves the collaboration of two different types of fleets in a network, where in many cases, one of the fleets must patrol the graph as a support fleet and is not subject to the same constraints as the other fleet, such as only visiting a vertex or an edge once. Examples include refueling fleets or trucks providing services to drones, where these fleets patrol the network so that the other fleet can use their services as needed.
In this paper, the problem of the free movement of such fleets on a graph is examined as a free patrol problem, which can be used as a sub-problem in the modeling and solving of many routing problems. To this end, the problem is modeled as a mixed-integer linear programming (MILP) problem in both discrete and continuous time spaces. Generally, it is expected that the model in continuous time space will exhibit lower computational complexity, but since modeling in continuous time requires changes to the graph, leading to an increase in the number of vertices and edges, its behavior needs to be more thoroughly investigated. On the other hand, another challenge in analyzing this problem is that the objective of the free patrol sub-problem depends on the main problem, as without the main problem, the free patrol problem may lose its objective, with a fleet patrolling the graph within a designated time. Therefore, to decouple and make the free patrol independent of the main problem, five different objective functions have been introduced for each model, covering a wider range of comparisons, leading to ten models in total. Subsequently, all ten models were solved exactly using randomly generated data for ten different graphs. Finally, the behavior of the models was evaluated and compared from various perspectives. The numerical results demonstrate the superior performance of the continuous time model over the discrete time model.Vehicle routing problems are a category of complex mathematical problems in the field of transportation and supply chain management, and a wide variety of these problems have been introduced. One category within this field involves the collaboration of two different types of fleets in a network, where in many cases, one of the fleets must patrol the graph as a support fleet and is not subject to the same constraints as the other fleet, such as only visiting a vertex or an edge once. Examples include refueling fleets or trucks providing services to drones, where these fleets patrol the network so that the other fleet can use their services as needed.
In this paper, the problem of the free movement of such fleets on a graph is examined as a free patrol problem, which can be used as a sub-problem in the modeling and solving of many routing problems. To this end, the problem is modeled as a mixed-integer linear programming (MILP) problem in both discrete and continuous time spaces. Generally, it is expected that the model in continuous time space will exhibit lower computational complexity, but since modeling in continuous time requires changes to the graph, leading to an increase in the number of vertices and edges, its behavior needs to be more thoroughly investigated. On the other hand, another challenge in analyzing this problem is that the objective of the free patrol sub-problem depends on the main problem, as without the main problem, the free patrol problem may lose its objective, with a fleet patrolling the graph within a designated time. Therefore, to decouple and make the free patrol independent of the main problem, five different objective functions have been introduced for each model, covering a wider range of comparisons, leading to ten models in total. Subsequently, all ten models were solved exactly using randomly generated data for ten different graphs. Finally, the behavior of the models was evaluated and compared from various perspectives. The numerical results demonstrate the superior performance of the continuous time model over the discrete time model.https://sjie.journals.sharif.edu/article_23753_82898f5776d3d691fafd95a423fe3c4f.pdfSharif University of TechnologySharif Journal of Industrial Engineering & Management2676-474141120250922Game-Theoretic Approach for Pricing Cloud Computing and Determining the Security Level of Security Provider CompaniesGame-Theoretic Approach for Pricing Cloud Computing and Determining the Security Level of Security Provider Companies1091212369410.24200/j65.2024.64530.2406FAMahdieSadeghianDepartment of Industrial and Systems Engineering, Isfahan University of Technology, IsfahanMortezaRasti-BarzokiDepartment of Industrial and Systems Engineering, Isfahan University of Technology, IsfahanHosseinKhosroshahiDepartment of Industrial and Systems Engineering, Isfahan University of Technology, IsfahanJournal Article20240615Since hardware may experience sudden failure and software solutions are often costly, users need an environment to perform computational and data storage tasks without expensive hardware and software. Cloud computing can provide this capability, but the presence of cyber hackers and their attacks raises user concerns about their data security. As information is precious, losing it can result in significant costs for the information owner. To address this problem, companies have emerged to ensure the safety of cloud computing services, and cloud users can entrust their information security to them. This article aims to examine the competition between security provider companies and cyber hackers using game theory and determine the strategies of each player to determine the game structure. These structures are based on the leader's decision to determine the security level initially or after an attack has occurred. The company decides what price to offer the user based on the value of the information, the amount of effort needed to return the information after a successful attack, the security level it needs to maintain, and the power structure.<strong> </strong>Similarly, the hacker decides how much effort to put in based on the value of the information. The results show that the price decreases linearly based on the information value when the company is the leader. In addition to the results obtained about the company's profit, it shows that in general, the company's profit, when it is a leader, is more than when it is a follower, and in particular, the company's profit based on the percentage of returned information in the leader's position is much higher than in the position of the follower. The level of security provided is also different according to the position of the company, and when the company is the leader, it is much higher than when the company is the follower, based on the hacker's credibility and the value of the returned information.Since hardware may experience sudden failure and software solutions are often costly, users need an environment to perform computational and data storage tasks without expensive hardware and software. Cloud computing can provide this capability, but the presence of cyber hackers and their attacks raises user concerns about their data security. As information is precious, losing it can result in significant costs for the information owner. To address this problem, companies have emerged to ensure the safety of cloud computing services, and cloud users can entrust their information security to them. This article aims to examine the competition between security provider companies and cyber hackers using game theory and determine the strategies of each player to determine the game structure. These structures are based on the leader's decision to determine the security level initially or after an attack has occurred. The company decides what price to offer the user based on the value of the information, the amount of effort needed to return the information after a successful attack, the security level it needs to maintain, and the power structure.<strong> </strong>Similarly, the hacker decides how much effort to put in based on the value of the information. The results show that the price decreases linearly based on the information value when the company is the leader. In addition to the results obtained about the company's profit, it shows that in general, the company's profit, when it is a leader, is more than when it is a follower, and in particular, the company's profit based on the percentage of returned information in the leader's position is much higher than in the position of the follower. The level of security provided is also different according to the position of the company, and when the company is the leader, it is much higher than when the company is the follower, based on the hacker's credibility and the value of the returned information.https://sjie.journals.sharif.edu/article_23694_e4300cd795148dda7a27397e072ba0c0.pdfSharif University of TechnologySharif Journal of Industrial Engineering & Management2676-474141120250922Hub Location with the Backup Approach by Considering the Capacity Constraint in Critical SituationsHub Location with the Backup Approach by Considering the Capacity Constraint in Critical Situations1221372367810.24200/j65.2024.62721.2365FAHasanZiaratiDepartment of Industrial &Systems Engineering, Isfahan University of Tech, Isfahan, Iran.AliShahandeh NookabadiDepartment of Industrial &Systems Engineering, Isfahan University of Tech, Isfahan, Iran.MohammadReisi-NafchiDepartment of Industrial &Systems Engineering, Isfahan University of Tech, Isfahan, IranJournal Article20230929The efficient transportation of goods and passengers from origin to destination is a crucial aspect of supply chain management. The design of transportation systems plays a key role in determining system costs and customer satisfaction. In cases where direct communication between all points is not feasible, the hub and spoke system can be utilized. The design of the hub network is a strategic decision that faces uncertainties such as demand, costs, and system reliability. Natural and unnatural events can impact the efficiency of hub facilities, leading to additional costs for the system. Capacity limitations in hub facilities may necessitate crisis management strategies, such as transferring flows to backup hubs during emergencies. This study explores the use of single backup and multiple backup approaches to address hub unavailability and meet demand requirements. A mathematical model is presented to investigate and solve the single and multiple backup strategies. Due to the complexity of the problem, a genetic algorithm approach is employed for optimization. The performance of the algorithms is evaluated using the CAB dataset, demonstrating the effectiveness of the proposed solutions. Comparing the results of single backup and multiple backup strategies reveals that the latter is more advantageous in terms of system costs and congestion in hub nodes. This study highlights the importance of strategic planning in transportation systems and the benefits of implementing backup solutions to ensure efficient operations. This research underscores the critical role that transportation systems play in the overall success of supply chains and the significant impact that effective logistics management can have on customer satisfaction and operational costs. The strategic decisions made in designing transportation networks can have far-reaching implications on the overall efficiency and reliability of the system. By exploring different backup strategies and utilizing a mathematical model, organizations can better prepare for disruptions and ensure continuity of operations. The use of genetic algorithms and data analysis tools can provide valuable insights into the performance of transportation systems and help identify opportunities for improvement. Overall, this study emphasizes the importance of proactive planning and the adoption of innovative solutions in the field of logistics and transportation management.The efficient transportation of goods and passengers from origin to destination is a crucial aspect of supply chain management. The design of transportation systems plays a key role in determining system costs and customer satisfaction. In cases where direct communication between all points is not feasible, the hub and spoke system can be utilized. The design of the hub network is a strategic decision that faces uncertainties such as demand, costs, and system reliability. Natural and unnatural events can impact the efficiency of hub facilities, leading to additional costs for the system. Capacity limitations in hub facilities may necessitate crisis management strategies, such as transferring flows to backup hubs during emergencies. This study explores the use of single backup and multiple backup approaches to address hub unavailability and meet demand requirements. A mathematical model is presented to investigate and solve the single and multiple backup strategies. Due to the complexity of the problem, a genetic algorithm approach is employed for optimization. The performance of the algorithms is evaluated using the CAB dataset, demonstrating the effectiveness of the proposed solutions. Comparing the results of single backup and multiple backup strategies reveals that the latter is more advantageous in terms of system costs and congestion in hub nodes. This study highlights the importance of strategic planning in transportation systems and the benefits of implementing backup solutions to ensure efficient operations. This research underscores the critical role that transportation systems play in the overall success of supply chains and the significant impact that effective logistics management can have on customer satisfaction and operational costs. The strategic decisions made in designing transportation networks can have far-reaching implications on the overall efficiency and reliability of the system. By exploring different backup strategies and utilizing a mathematical model, organizations can better prepare for disruptions and ensure continuity of operations. The use of genetic algorithms and data analysis tools can provide valuable insights into the performance of transportation systems and help identify opportunities for improvement. Overall, this study emphasizes the importance of proactive planning and the adoption of innovative solutions in the field of logistics and transportation management.https://sjie.journals.sharif.edu/article_23678_4cae10f5379af877eaf9bf2e55044635.pdfSharif University of TechnologySharif Journal of Industrial Engineering & Management2676-474141120250823Optimization of Hospital Bed Allocation by a Hybrid Simulation-MCDM ApproachOptimization of Hospital Bed Allocation by a Hybrid Simulation-MCDM Approach1381512378010.24200/j65.2024.64305.2402FASaraMotevali HaghighiDepartment of Industrial Engineering, Faculty of Electrical and Computer Engineering, Esfarayen University of Technology, Esfarayen, IranAliGhorbanianDepartment of Industrial Engineering, Faculty of Electrical and Computer Engineering, Esfarayen University of Technology, Esfarayen, IranJournal Article20240505Hospitals are one of the key parts of the health system that are responsible for providing services to patients. The optimal allocation of hospital beds is one of the important issues that plays a significant role in the financial and clinical performance of hospitals. Therefore, in this article, an algorithm in six main steps, including data collection, simulation, scenario definition, simulation model execution, calculation of the importance degree of output variables, and ranking of scenarios, has been developed using a computer simulation approach and considering the bed-sharing policy among different hospital departments. The main criteria considered in this study include patient rejection (PR), the percentage of resource utilization (RU), and the length of the queue (LQ). Due to the nature of uncertainty in the problem, a fuzzy DEMATEL method has been used for ranking scenarios. Finally, the best scenarios have been identified from the total scenarios considered, which can be taken into account by hospital managers in decision-making to improve the overall performance of their medical unit using optimal scenario assumptions. The presented algorithm has been investigated on a case study, and its results have been analyzed and reviewed. The considered case study hospital has two inpatient departments. The first department is designated for triage patients and first-type clinic patients, while the second department is for second-type clinic patients. In the conducted case study, the weights of each decision criterion are 0.33, 0.09, 0.27, and 0.30<strong>, </strong>respectively. A total of 36<strong> </strong>scenarios have been defined for the case study. In the end, based on the developed model, scenario number 20 has been chosen as the best scenario. In this scenario, the percentage of patient rejection for triage is 9/5%, the resource utilization percentage is 70%, and the average number of people waiting in line for first and second-type clinic patients is 19.73<strong> </strong>and 1.27, respectively<strong>.</strong>Hospitals are one of the key parts of the health system that are responsible for providing services to patients. The optimal allocation of hospital beds is one of the important issues that plays a significant role in the financial and clinical performance of hospitals. Therefore, in this article, an algorithm in six main steps, including data collection, simulation, scenario definition, simulation model execution, calculation of the importance degree of output variables, and ranking of scenarios, has been developed using a computer simulation approach and considering the bed-sharing policy among different hospital departments. The main criteria considered in this study include patient rejection (PR), the percentage of resource utilization (RU), and the length of the queue (LQ). Due to the nature of uncertainty in the problem, a fuzzy DEMATEL method has been used for ranking scenarios. Finally, the best scenarios have been identified from the total scenarios considered, which can be taken into account by hospital managers in decision-making to improve the overall performance of their medical unit using optimal scenario assumptions. The presented algorithm has been investigated on a case study, and its results have been analyzed and reviewed. The considered case study hospital has two inpatient departments. The first department is designated for triage patients and first-type clinic patients, while the second department is for second-type clinic patients. In the conducted case study, the weights of each decision criterion are 0.33, 0.09, 0.27, and 0.30<strong>, </strong>respectively. A total of 36<strong> </strong>scenarios have been defined for the case study. In the end, based on the developed model, scenario number 20 has been chosen as the best scenario. In this scenario, the percentage of patient rejection for triage is 9/5%, the resource utilization percentage is 70%, and the average number of people waiting in line for first and second-type clinic patients is 19.73<strong> </strong>and 1.27, respectively<strong>.</strong>https://sjie.journals.sharif.edu/article_23780_cf83d8ab387752811dd09f4781ae6bb0.pdf