Mohammad Mirabi; Mohammad Taghi Fatemi Ghomi; Fariborz Jolai
Abstract
The design of control chart has economic consequences that pure statistical viewpoint does not consider them. The economic-statistical design of control chart, attends not only statistical properties such as average time to signal (ATS) but also economic consequences like hourly expected total cost. ...
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The design of control chart has economic consequences that pure statistical viewpoint does not consider them. The economic-statistical design of control chart, attends not only statistical properties such as average time to signal (ATS) but also economic consequences like hourly expected total cost. The x-bar control chart dominates others if the quality is measured by continuous scale. This paper has considered the economic-statistical design of variable sample size and sampling interval (VSSI) x-bar control chart with multiple assignable causes. Using three sample sizes and three sampling intervals to construct the VSSI x-bar control chart and considering possible combination of design parameters as a decision-making unit, are part of novelty of this research. The problem is formulated as multiple objective decision making (MODM). Also, one capable hybrid meta-heuristic based on genetic algorithm is developed in this research and it was compared with some approaches extracted from the literature and it is found that it can be competitive based on economic and statistics factors.
Ehsan Vaezi
Abstract
Classic Data Envelopment Analysis methods ignore the internal interactions and consider the systems as a ‘black box’. Most of the network analysis models are nonlinear and it is feasible that these point may cause a considerable amount of modification to occur in the efficiency results. Amidst ...
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Classic Data Envelopment Analysis methods ignore the internal interactions and consider the systems as a ‘black box’. Most of the network analysis models are nonlinear and it is feasible that these point may cause a considerable amount of modification to occur in the efficiency results. Amidst which, models, such as, the Wang Model, takes the intermediate variables into account, but in order to prevent intricacies in resolving models, it has an inconclusive approach, between two outlooks, of the black box and the network. Hence, in this paper, we consider a three-stage network with additional, desirable and undesirable inputs and outputs and the three abovementioned approaches are analyzed by contemplating on the optimistic and pessimistic views. The goals of this paper are to put together the results of the three mentioned approaches in order to attain the final conclusions. We generalize and use of Wang’s approach for the three levels, with additional inputs and outputs, as well as a heuristic solution to solve the network’s view. Finally, this paper considers a genuine world example, in the form of a dynamic network, for model application and analyzes it from three perspectives.
Nasser Safaie; Ali Nazeri; Anita Mottakiani
Abstract
In this study, the relationship between supply chain management functions, supply chain performance, competitive advantage, and organizational performance in the hospitality were investigated. The objective of this study was to assess the performance of supply chains and management in the hospitality. ...
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In this study, the relationship between supply chain management functions, supply chain performance, competitive advantage, and organizational performance in the hospitality were investigated. The objective of this study was to assess the performance of supply chains and management in the hospitality. For this purpose, a model consisting of 4 variables and 5 hypotheses was created. The statistical population of this study included all administrative staff of the 4 and 5-star hotels in Tehran. Sampling among senior and middle managers was done randomly. A total of 199 samples were collected and the designed model was tested using a structural equation approach. All study hypotheses were approved. The results indicated that using the dimensions of the supply chain management function, we can observe the positive and comprehensive impact of these factors on organizational performance, supply chain performance, and competitive advantage. In addition, supply chain performance and competitive advantage were found to have a positive and significant effect on organizational performance.
Taha-Hossein Hejazi; Shahin Behboodi; Fatemeh Abbaszadeh
Abstract
Generally, the safety management system (SMS) introduced in 1980 focuses on reducing the risk of potential injuries and fatalities in the construction industry. The key to considering the challenges of project safety management and risk assessment in the construction industry as a hazardous industry ...
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Generally, the safety management system (SMS) introduced in 1980 focuses on reducing the risk of potential injuries and fatalities in the construction industry. The key to considering the challenges of project safety management and risk assessment in the construction industry as a hazardous industry because of its peculiar nature is important. In line with this, this article aims at employing decision-making techniques to ensure the safety requirements of construction projects. Additionally, a questionnaire under fuzzy environments for identifying the candidate locations and strategies associated with each specific location was conducted. Also, the Empirical Bayesian (EB) approach has been considered to estimate the expected frequency of accidents. The objective of the novel proposed approach is to find the optimal safety project selection with respect to the economic indicators and time value of money under uncertain circumstances. For this purpose, a mathematical optimization model is proposed, and its efficiency is demonstrated by a numerical case study. The results of optimizing the mathematical model indicate that by modifying two factors, namely the safety level and uncertainty coefficient, several scenarios can be explored for cost reduction and a decrease in the number of construction projects. By maintaining a constant safety level of 1.37 (as determined by industry experts) and increasing the uncertainty coefficient from 0 to 0.2, costs decrease by a factor of 1.7, accompanied by a decrease in the number of construction projects by one unit. Furthermore, when the uncertainty coefficient is held constant at 0.2, costs can be reduced up to four times by reducing the safety level from 1.37 to 1. This decision-making framework can significantly contribute to minimizing building accidents and enhancing safety in construction projects.
Mojtaba Hajian Heidary
Abstract
During the last decade, many researchers have been attracted to study the role of uncertainties in their supply chain designs. Two important uncertainties of a supply chain are demand uncertainty and supply disruption. The basic concept of the proposed model of this paper is based on the newsvendor problem. ...
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During the last decade, many researchers have been attracted to study the role of uncertainties in their supply chain designs. Two important uncertainties of a supply chain are demand uncertainty and supply disruption. The basic concept of the proposed model of this paper is based on the newsvendor problem. The model consists of many retailers and many suppliers as two types of autonomous agents that interact with each other considering demand and supply uncertainties. To cope with the uncertainties, retailers have three choices: a forward contract, an option contract, and purchasing from the spot market. Retailers maybe risk sensitive or risk neutral. A new simulation optimization approach is developed to find the best behavior of a risk sensitive retailer in contrast with the other risk neutral retailers during the multiple contract periods. In this model two objectives are defined to find the best behavior of the risk sensitive retailer: the maximization of the profit and the service level. In order to optimize the agent based simulation, an NSGA-II approach is used. The proposed simulation based NSGA-II is further developed in two directions: the one is different realization numbers of the uncertain parameters, and the other is preference points. Under the different preference points and different number of realizations, Pareto optimal solutions are discovered by the collaboration of the agents. Results of the numerical studies showed that adopting more risk averse policies during the contract periods will result in a larger service level and smaller profit rather than adopting more risk taking policies.
Iman Seyedi; Maryam Hamedi; Reza Tavakkoli-Moghadaam
Abstract
This paper deals with optimizing the multi-door cross-docking scheduling problem for incoming and outgoing trucks. Contrary to previous studies, it first considers the simultaneous effects of learning and deteriorating on loading and unloading the jobs. A mixed-integer linear programming (MILP) model ...
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This paper deals with optimizing the multi-door cross-docking scheduling problem for incoming and outgoing trucks. Contrary to previous studies, it first considers the simultaneous effects of learning and deteriorating on loading and unloading the jobs. A mixed-integer linear programming (MILP) model is developed for this problem, in which the basic truck scheduling problem in a cross-docking system is strongly considered as NP-hardness. Thus, in this paper, meta-heuristic algorithms namely genetic algorithm, imperialist competitive algorithm, and a new hybrid meta-heuristic algorithm, resulted from the principal component analysis (PCA) and an imperialist competitive algorithm (ICA) called PCICA are proposed and used. Finally, the numerical results obtained from meta-heuristic algorithms are examined using the relative percentage deviation and time criteria. Results show that the hybrid PCICA algorithm performs better than the other algorithms in terms of the solution quality. Computational results indicate when the learning rate increases, its decreasing effect on processing time will growth and the objective function value is improved. Finally, the sensitivity analysis also indicates when the deterioration rate is reduced, its incremental effect is decreased over time.
Zahra Hajirahimi; Mehdi Khashei
Abstract
With the increasing importance of forecasting with the utmost degree of accuracy, utilizing hybrid frameworks become a must for obtaining more accurate and more reliable forecasting results. Series hybrid methodology is one of the most widely-used hybrid approaches that has encountered a great amount ...
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With the increasing importance of forecasting with the utmost degree of accuracy, utilizing hybrid frameworks become a must for obtaining more accurate and more reliable forecasting results. Series hybrid methodology is one of the most widely-used hybrid approaches that has encountered a great amount of popularity in the literature of time series forecasting and has been applied successfully in a wide variety of domains. In such hybrid methods is assumed that there is an additive relationship among different components of time series. Thus, based on this assumption, various individual models can apply separately on decomposed components, and the final forecast can be obtained. However, developed series hybrid models in the literature are constructed based on the decomposing time series into linear and nonlinear parts and generating linear-nonlinear modeling order for decomposed parts. Another assumption considered in the traditional series model is assigning equal weights to each model used for modeling linear and nonlinear components. Thus, contrary to traditional series hybrid models, to improve the performance of series hybrid models, these two basic assumptions have been violated in this paper. This study aims to propose a novel weighted MLP-ARIMA model filling the gap of series hybrid models by changing the order of sequence modeling and assigning weight for each component. Firstly, the modeling order is changed to nonlinear-linear, and then Multi-Layer Perceptron Neural Network (MLPNN) -Auto-Regressive Integrated Moving Average(ARIMA) models are employed to model and process nonlinear and linear components respectively. Secondly, each model's weights are computed by the Ordinary Least Square (OLS) weighting algorithm. Thus, in this paper, a novel improved weighted MLP-ARIMA series hybrid model is proposed for time series forecasting. The real-world benchmark data sets, including Wolf's sunspot data, the Canadian lynx data, and the British pound/US dollar exchange rate data, are elected to verify the effectiveness of the proposed weighted MLP-ARIMA series hybrid model. The simulation results revealed that the weighted MLP-ARIMA model could obtain superior performance compared to ARIMA-MLP, MLP-ARIMA, as well as the ARIMA and MLPNN individual models. The proposed hybrid model can be an effective alternative to improve forecasting accuracy obtained by traditional series hybrid methods.
Mohammad Alipour-Vaezi; Reza Tavakkoli-Moghadaam; Mina Samieinasab
Abstract
Since human societies have endured massive financial disruptions and life losses after the outbreak of the COVID-19 pandemic, it is critical to eliminate this disease as soon as possible. Today, the invention of the COVID-19 vaccine made this objective more reachable. But unfortunately, the suppliant ...
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Since human societies have endured massive financial disruptions and life losses after the outbreak of the COVID-19 pandemic, it is critical to eliminate this disease as soon as possible. Today, the invention of the COVID-19 vaccine made this objective more reachable. But unfortunately, the suppliant of the vaccines is limited. Hence, to prevent further lethal harms, it seems rational to use a scientific method for vaccine allocation. This study proposes a method for prioritizing the patients based on their level of life-threatening danger according to the proven risk factors (e.g., age, sex, pregnancy, and underlying diseases) of the COVID-19. That is a new data-driven decision-making method for patients’ classification based on their health condition information using several machine learning algorithms. In this method, vaccine applicants are classified into four classes. The scheduling of vaccine distribution would be conducted based on the results of this classification. Furthermore, a real-life case study is also investigated through the proposed method for better illumination in this paper. The vaccine distribution schedule of the real-case study has been performed with 94% accuracy. It should be mentioned that the main achievement of this research is to design a new efficient method for a vaccine distribution schedule.
Mohammad Bagher Fakhrzad; Mohammad Reza Firozpour; Hasan Hosseini Nasab; Ahmad Sadegheih
Abstract
For reducing risk effects in a supply chain, the appropriate risk assessment and ranking by the use of multi-criteria decision-making methods (MCDM) is important. Failure to properly assess and rank the risks makes the supply chain less efficient and competitive. Given the existence of both qualitative ...
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For reducing risk effects in a supply chain, the appropriate risk assessment and ranking by the use of multi-criteria decision-making methods (MCDM) is important. Failure to properly assess and rank the risks makes the supply chain less efficient and competitive. Given the existence of both qualitative and quantitative criteria in a supply chain, the use of verbal preferences, given by authorities for determining the priority of qualitative factors, has higher reliability than that of the Crisp numbers. Fuzzy concept plays an important role in solving the problem of complexity of assigning quantitative fixed numbers to the values of verbal preferences. In the proposed method of this study, a comparison was made among the decision-making methods in the fuzzy environment for selecting a suitable method. To validate the proposed method, we compared it to some case studies from the literature. The results show that the proposed method has high validity and reliability in assessing the risks of a supply chain.
Mohammad Hossein Sadat Hosseini Khajouei; Nazanin Pilevari
Abstract
Nowadays, environmental deterioration is one of the most noticeable issues in logistics, so that the organizations are required to control the triggers of environmental contaminations generation. One of the most effective steps in addressing this term is to design transportation network considering CO2 ...
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Nowadays, environmental deterioration is one of the most noticeable issues in logistics, so that the organizations are required to control the triggers of environmental contaminations generation. One of the most effective steps in addressing this term is to design transportation network considering CO2 emission limitation. In this paper, a vehicle routing problem with simultaneous pickup and delivery with heterogeneous fleet and environmental measurement consideration is proposed. Introduced two objectives mathematical modeling, with the help of the weighted LP metric method has become to a combined dimensionless objective. The formulated optimization problem is solved in small dimensions using General Algebraic Modelling System (GAMS) approach and specifically BARON solver respect to the nature of the mathematical equations. The results obtained from simulations are discussed to confirm the effectiveness of the proposed method in dealing with the desired example. Because of NP-hardness, Discrete Invasive Weed Optimization (DIWO) meta-heuristic algorithm is applied.
Mohammad Saleh Owlia; Kosar Roshani; Mohammad Hossein Abooei
Abstract
In the age of a knowledge-based economy, identifying, measuring, and managing the intellectual capital (IC) of organizations has become very significant. These depend on identifying the main components of intellectual capital and their relationships. So far, however, no study has been conducted to clarify ...
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In the age of a knowledge-based economy, identifying, measuring, and managing the intellectual capital (IC) of organizations has become very significant. These depend on identifying the main components of intellectual capital and their relationships. So far, however, no study has been conducted to clarify the interactions among those components or to develop a model for laying out a hierarchy of IC components. There is, indeed, an urgent need to analyze the behavior of IC components so that the corresponding policies may be successfully implemented. This paper aims to prioritize the IC components based on the identified relationships among the IC components with a focus on the banking industry. A literature review was used to identify the 16 most important IC components. At the first stage, the Interpretive Structural Modeling technique was practiced to determine the interrelationships among these components, based on the data gathered from the Export Development Bank of Iran. The interconnections between the components were clarified. At the second stage, the application of Analytic Network Process for the prioritizing of IC components has been demonstrated. MICMAC analysis and classifying them into four categories including the autonomous, driver, dependent, and linkage components regarding their driving and dependence power is a new effort in the field of IC. A hierarchical structure was proposed through leveling of the components. And finally, the importance and priorities of the components are calculated with the help of the fuzzy analytic network process. The adoption of such an ISM-ANP model of IC components in the banking industry would provide insights for managers, decision-makers and policymakers for a better understanding of these components and to focus on the major components while managing their IC in their organizations.
Armin Cheraghalipour; Emad Roghanian
Abstract
Due to the increasing progress in various industries, paying attention to the internal processes of the organizations is more visible to stay on the competitive scene. Therefore, many organizations attempt to simplify and evaluate their internal processes using re-engineering. By reviewing the conducted ...
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Due to the increasing progress in various industries, paying attention to the internal processes of the organizations is more visible to stay on the competitive scene. Therefore, many organizations attempt to simplify and evaluate their internal processes using re-engineering. By reviewing the conducted studies, it can be stated that one of the existing problems in the implementation of re-engineering projects is the selection of the optimal portfolio of processes. Hence, this study aims to provide a bi-objective mathematical model for selecting processes in the re-engineering project by considering two key assumptions include improvement in achieving organizational goals and staff resistance. To this end, first, the impact of processes on organizational goals is specified by experts and then the goals’ weights are obtained using a fuzzy Best Worst Method. Finally, the proposed model is solved by an augmented ε-constraint method and the optimal portfolio of processes is selected. Also, a public Hospital of Sari as a real-world case study is employed to set the values of model parameters. Finally, the obtained results are reported and using a sensitivity analysis, several directions are provided. The results show that changes in the staff resistance directly affects the second objective function, while changes in the improvement created by each process affect the first objective function. Also, changes in costs have little effect on either objective functions.
Mahdi Nakhaeinejad
Abstract
The parallel machine scheduling problem (PMSP) is one of the most difficult classes of problem. Due to the complexity of the problem, obtaining optimal solution for the problems with large size is very time consuming and sometimes, computationally infeasible. So, heuristic algorithms that provide near-optimal ...
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The parallel machine scheduling problem (PMSP) is one of the most difficult classes of problem. Due to the complexity of the problem, obtaining optimal solution for the problems with large size is very time consuming and sometimes, computationally infeasible. So, heuristic algorithms that provide near-optimal solutions are more practical and useful. The present study aims to propose a hybrid metaheuristic approach for solving the problem of unrelated parallel machine scheduling, in which, the machine and the job sequence dependent setup times are considered. A Mixed-Integer Programming (MIP) model is formulated for the unrelated PMSP with sequence dependent setup times. The solution approach is robust, fast, and simply structured. The hybridization of Genetic Algorithm (GA) with Ant Colony Optimization (ACO) algorithm is the key innovative aspect of the approach. This hybridization is made in order to accelerate the search process to near-optimal solution. After computational and statistical analysis, the two proposed algorithms are used to compare with the proposed hybrid algorithm to highlight its advantages in terms of generality and quality for short and large instances. The results show that the proposed hybrid algorithm has a very good performance as regards the instance size and provides the acceptable results.
Maryam Aghapoor Alishahi; GholamReza Rahimi; Mojtaba Ramazani; Nader Bohlooli
Abstract
Since one of the main dimensions of any organization in terms of survival and development is strategy, so the main purpose of this study is to provide a model of alignment of organizational strategies and human resource strategies for this purpose, both qualitative and quantitative methods have been ...
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Since one of the main dimensions of any organization in terms of survival and development is strategy, so the main purpose of this study is to provide a model of alignment of organizational strategies and human resource strategies for this purpose, both qualitative and quantitative methods have been used. The quality section has two sections. In the first part, the quality method of articles from 2014 to 2020 related to the subject were reviewed and evaluated, and the main criteria of strategy, organizational strategy, human resources strategy, strategic alignment and organizational structure were identified. The second part of the qualitative method was based on interviews with experts until the theoretical saturation in the field of the present study was done and coding and analysis was done using MAXQDA software. The main criteria identified based on interviews with experts include organizational communication, employee empowerment, employee attitude evaluation, organizational strategies, human resource strategies, organizational development, social capital, intra-organizational factors, external organizational factors, organizational responsibility and goal setting. It is based on environmental factors that the highest percentage of frequency is related to organizational development and is equal to 20.94% and the lowest value is related to improving the ability of employees equal to 2.6%. In quantitative evaluation, F.DEMATEL, F.AHP and DEA methods have been used. Using F.DEMATEL method, effective and efficient dimensions were identified that the effective factors include organizational communication, employee attitude evaluation, organizational strategies, organizational development, social capital, external environmental factors and organizational responsibility and effective factors include improving employee ability, resource strategies Human, internal factors and goal setting are based on environmental conditions. Then, using F.AHP, the identified variables were prioritized. The first rank among the sub-criteria belongs to the factor of exposure to critical factors with a normal weight of 0.785, and the first rank among the main criteria includes social capital with a normal weight of 0.134. The results obtained from DEA show that exposure to critical factors has the highest efficiency score with a maximum score equal to 1, and finally, based on the results, practical suggestions are presented.
Samar Shetaban; Mir Mehdi Seyyed Esfahani; Abbas Saghaei; Abbas Ahmadi
Abstract
Todays, human health is in the best situation ever. The progress of vaccines, development of hospitals, new medicines, advanced medical equipment, and new treatments preventing death will place the health system indicators at its best state in all ages and centuries. In addition, healthcare is one of ...
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Todays, human health is in the best situation ever. The progress of vaccines, development of hospitals, new medicines, advanced medical equipment, and new treatments preventing death will place the health system indicators at its best state in all ages and centuries. In addition, healthcare is one of the biggest industries in developed and developing countries and is a service-oriented industry that is significant, high-quality, and safe in medical services. Healthcare has become one of the biggest sectors in terms of income and employment. Health care involves hospitals, medical devices, clinical trials, outsourcing, telemedicine, medical tourism, health insurance, and medical equipment. Nowadays, the application of operations research in various fields, including health, is on increase. Although many issues face operations research in healthcare, such issues are not analytically different from the issues in other industries. Operations research, as a quantitative systemic method, can considerably solve the problems related to the health system. The present study aimed to evaluate the application of operations research models based on the research process in health systems including Markov decision-making processes (MDPs) and partially observable Markov decision process (POMDP), etc., and compare these methods with each other. The basis of this study was evaluating the publication of scientific studies on operations research models in the health systems.
Behrooz Khorshidvand; Hamed Soleimani; Mir Mehdi Seyyed Esfahani; Soheil Sibdari
Abstract
This paper addresses a novel two-stage model for a Sustainable Closed-Loop Supply Chain (SCLSC). This model, as a contribution, provides a balance among economic aims, environmental concerns, and social responsibilities based on price, green quality, and advertising level. Therefore, in the first stage, ...
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This paper addresses a novel two-stage model for a Sustainable Closed-Loop Supply Chain (SCLSC). This model, as a contribution, provides a balance among economic aims, environmental concerns, and social responsibilities based on price, green quality, and advertising level. Therefore, in the first stage, the optimal values of price are derived by considering the optimal level of advertising and greening. After that, in the second stage, multi-objective Mixed-Integer Linear Programming (MOMILP) is extended to calculate Pareto solutions. The objectives are include maximizing the profit of the whole chain, minimizing the environmental impacts due to CO2 emissions, and maximizing employee safety. Besides, a Lagrangian relaxation algorithm is developed based on the weighted-sum method to solve the MOMILP model. The findings demonstrate that the proposed two-stage model can simultaneously cope with coordination decisions and sustainable objectives. The results show that the optimal price of the recovered product equals 75% of the new product price which considerably encourages customers to buy it. Moreover, to solve the MOMILP model, the proposed algorithm can reach to exact bound with an efficiency gap of 0.17% compared to the optimal solution. Due to the use of this algorithm, the solution time of large-scale instances is reduced and simplified by an average of 49% in comparison with the GUROBI solver.
Golnar Adabi; Ali Hajiha; Farhad Hosseinzadeh Lotfi
Abstract
Studies in the field of the tourism industry are often carried out using classical statistics. While the use of spatial statistics in tourism helps a lot in identifying existing models and trends in the industry and discovering them. Due to the interaction between economic, political, environmental and ...
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Studies in the field of the tourism industry are often carried out using classical statistics. While the use of spatial statistics in tourism helps a lot in identifying existing models and trends in the industry and discovering them. Due to the interaction between economic, political, environmental and social elements in tourism activities, analysis methods of spatial statistics can be used by identifying information between samples and using large volumes of information by not indicating the independence of the data, can obtain suitable tourism clusters and help identify the appropriate tourism model in Iran. This study aims at to design a model of the tourism industry in Iran with the approach of the spatial correlation structure. The research method was qualitative and quantitative. To identify the variables affecting the tourism industry, the qualitative meta-analysis method, and to collect the required data in spatial statistics, the data of the Cultural Heritage and Tourism Organization in the summer of 2008-2018 have been used. To determine the model of tourism clusters Moran statistics and to study tourism clusters in all provinces of the country, the best interpolation method of tourism has been determined. ArcGIS software was used to analyze the research data. The results of data analysis showed that tourism data has a spatial autocorrelation and a cluster and regular model in the statistical period of summer 2008 to 2018. The most cluster model of tourism using the Moran spatial autocorrelation index is related to the summer of 2008 with 0.991 and the lowest cluster model of tourism is related to the summer of 2014 with the amount of 0.976. Also, the results of the study of the distribution of tourism direction in the provinces of the country in this statistical period showed that the predominant direction of tourism is with a slight change from northwest to southeast.