Shahid Beheshti University Journal of Industrial Management Perspective 2251-9874 11 2 2021 06 22 Multi-Objective Random Model to Determine the Type, Capacity and Installation Location of Distributed Products in the New Supply Chain of the Electricity Industry Multi-Objective Random Model to Determine the Type, Capacity and Installation Location of Distributed Products in the New Supply Chain of the Electricity Industry 9 39 100873 10.52547/jimp.11.2.9 FA Ahmad Ghorbankhani Ph.D student, Yazd University. Ali Morvati Sharifabadi Associate Professor, Yazd University. Seyed Habibollah Mir Ghafouri Associate Professor, Yazd University. Seyed Heydar Mirfakhrodini Associate Professor, Yazd University. Journal Article 2020 05 26 In recent years, with the high cost of building large and centralized power plants and the problems of long power transmission lines, the electricity industry has shifted to the use of small and distributed generation near the location of customers. On the other hand, due to environmental problems, some of these distributed products are based on renewable energy, which has a random behavior. Determining the location and capacity of these products at the distribution network level has a great impact on managing financial resources and improving supply chain parameters. In this research, a comprehensive multi-objective and probabilistic model is proposed to determine the installation location, type, and optimal capacity of distributed products at the level of the new electricity supply chain. The ultimate goal of this model is to minimize energy losses, investment and operation costs, unsupplied energy, and environmental pollutants. The proposed model is applied on a 33-region network by MATLAB software and solved in a multi-objective way by a genetic meta-heuristic algorithm with faulty sorting. The final results show the effectiveness of the proposed method in various economic, environmental, and social dimensions of the electricity supply chain. In recent years, with the high cost of building large and centralized power plants and the problems of long power transmission lines, the electricity industry has shifted to the use of small and distributed generation near the location of customers. On the other hand, due to environmental problems, some of these distributed products are based on renewable energy, which has a random behavior. Determining the location and capacity of these products at the distribution network level has a great impact on managing financial resources and improving supply chain parameters. In this research, a comprehensive multi-objective and probabilistic model is proposed to determine the installation location, type, and optimal capacity of distributed products at the level of the new electricity supply chain. The ultimate goal of this model is to minimize energy losses, investment and operation costs, unsupplied energy, and environmental pollutants. The proposed model is applied on a 33-region network by MATLAB software and solved in a multi-objective way by a genetic meta-heuristic algorithm with faulty sorting. The final results show the effectiveness of the proposed method in various economic, environmental, and social dimensions of the electricity supply chain. Distributed supply chain Distributed products Renewable Uncertainty Genetic algorithm https://jimp.sbu.ac.ir/article_100873_2e7a4da9deb3dabcff74d5014eee36b6.pdf
Shahid Beheshti University Journal of Industrial Management Perspective 2251-9874 11 2 2021 06 22 Modeling and Solving the Cross-Docking Centers Location and Vehicle Scheduling Problem in a Multi-Product Supply Chain with Discrete Pick-up and Delivery Modeling and Solving the Cross-Docking Centers Location and Vehicle Scheduling Problem in a Multi-Product Supply Chain with Discrete Pick-up and Delivery 41 66 94173 10.52547/jimp.11.2.41 FA Soheyla Ghorbani MSc, Islamic Azad University, Qazvin Branch. Behrouz Afshar-Nadjafi Associate Professor, Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch. Journal Article 2020 03 03 This research studies cross-docking centers location and vehicles routing scheduling problems simultaneously in a three-level supply chain with discrete pick-up and delivery. The proposed problem is formulated as a mixed-integer nonlinear programming model with the aim of reducing total cost includes cross-docking centers construction cost, transportation fixed and variable costs, earliness and tardiness penalty costs. In this supply chain model, vehicles start from a cross-docking center and pick up different products from various suppliers and after classifying and preparing products at cross-docking centers, a different group of vehicles are sent to deliver products to customers. For delivering any kind of product to each customer, a soft time window is considered. Herein, three types of small, medium and large size instances have been generated randomly and solved by using the proposed simulated annealing algorithm. For small problems, the results from simulated annealing algorithm are compared with the solutions obtained by the exact methods. This research studies cross-docking centers location and vehicles routing scheduling problems simultaneously in a three-level supply chain with discrete pick-up and delivery. The proposed problem is formulated as a mixed-integer nonlinear programming model with the aim of reducing total cost includes cross-docking centers construction cost, transportation fixed and variable costs, earliness and tardiness penalty costs. In this supply chain model, vehicles start from a cross-docking center and pick up different products from various suppliers and after classifying and preparing products at cross-docking centers, a different group of vehicles are sent to deliver products to customers. For delivering any kind of product to each customer, a soft time window is considered. Herein, three types of small, medium and large size instances have been generated randomly and solved by using the proposed simulated annealing algorithm. For small problems, the results from simulated annealing algorithm are compared with the solutions obtained by the exact methods. Mathematical Programming Cross-Docking Location Vehicle Routing Discrete Pick-up and Delivery Simulated Annealing Algorithm https://jimp.sbu.ac.ir/article_94173_1fef42fa33a688b86a6fc2f3f9d58754.pdf
Shahid Beheshti University Journal of Industrial Management Perspective 2251-9874 11 2 2021 06 22 Proposing a Model for Analyzing and Improving a Service System through Queue Theory and Simulation Approach (Case: Hamedan Power Company) Proposing a Model for Analyzing and Improving a Service System through Queue Theory and Simulation Approach (Case: Hamedan Power Company) 67 100 100918 10.52547/jimp.11.2.67 FA Peyman Zandi Master’s Degree Graduated, Allameh Tabatabai University. Mohammad Rahmani Assistant Professor, Bu Ali Sina University. Parham Azimi Associate Professor, Islamic Azad University, Qazvin Branch. Journal Article 2019 11 09 It’s evident that waiting in a queue is not desirable. Nevertheless, reducing the waiting time will be costly. In order to enhance the efficiency and improve the performance of a system, there are some solutions that result in reductions in the response time and enhancement in user satisfaction. Among all, the simulation approach does not deliver a real optimal solution but provides a description of the events that take place under certain conditions in the system. Through such a model, the decision-maker can investigate system improvements via scenario analysis. This study aims to analyze a system’s behavior through queue modeling, simulation, and statistical analysis. The case under study was a service system i.e. the financial department of Hamedan Power Company. This system was modeled and analyzed via the ED software, version 8.1. Thereby, improving changes were foreseen and statistically analyzed. Findings on the proposed scenario show a significant reduction in the total waiting time of this system. Based on this scenario, it was proposed that three personnel – via a training program – serve all the three customer types (A, B, & C). In this way, the model format changed. Almost 60 seconds of each customer’s time was saved thereby. Hence the workflow can be changed through interventions such as developing some training programs. It’s evident that waiting in a queue is not desirable. Nevertheless, reducing the waiting time will be costly. In order to enhance the efficiency and improve the performance of a system, there are some solutions that result in reductions in the response time and enhancement in user satisfaction. Among all, the simulation approach does not deliver a real optimal solution but provides a description of the events that take place under certain conditions in the system. Through such a model, the decision-maker can investigate system improvements via scenario analysis. This study aims to analyze a system’s behavior through queue modeling, simulation, and statistical analysis. The case under study was a service system i.e. the financial department of Hamedan Power Company. This system was modeled and analyzed via the ED software, version 8.1. Thereby, improving changes were foreseen and statistically analyzed. Findings on the proposed scenario show a significant reduction in the total waiting time of this system. Based on this scenario, it was proposed that three personnel – via a training program – serve all the three customer types (A, B, & C). In this way, the model format changed. Almost 60 seconds of each customer’s time was saved thereby. Hence the workflow can be changed through interventions such as developing some training programs. Services Management Simulation Queue theory ED software https://jimp.sbu.ac.ir/article_100918_8875758e8305c8bb7fcad4db7baa7069.pdf
Shahid Beheshti University Journal of Industrial Management Perspective 2251-9874 11 2 2021 06 22 Designing a Hierarchical Network of Temporary Urban Medical Centers in a Disaster through a Hybrid Approach of Mathematical Model – Simulation Designing a Hierarchical Network of Temporary Urban Medical Centers in a Disaster through a Hybrid Approach of Mathematical Model – Simulation 99 124 87606 10.52547/jimp.11.2.99 FA Sogol Mousavi Ph.D Student, Industrial Engineering Department, Islamic Azad University, Science and Research Branch, Tehran, Iran. Seyed Mojtaba Sajadi Associate Professor, Department of Business, Faculty of Entrepreneurship, University of Tehran, Tehran, Iran. Akbar AlemTabriz Professor, Department of Industrial Management and Information Technology, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran. Seyed Esmaeil Najafi Assistant Professor, Industrial Engineering Department, Islamic Azad University, Science and Research Branch, Tehran, Iran. Journal Article 2020 05 24 The destructive impact of natural disasters emphasizes the importance of logistics and human resource planning in the pre- and post- disaster periods. In the event of a disaster, in order to provide immediate relief, the Health Hierarchical Network, which includes clinics and hospitals, will be activated. In this paper, using a mixed integer mathematical model and assuming the current location of hospitals and clinics, optimal locations are determined as temporary treatment emergency centers and the optimal allocation of casualties from urban areas to these centers and then clinics and hospitals are recommended in disaster. Using the simulation model, the moment of disaster and the rescue process were simulated and then the optimization approach was adopted based on simulating the optimal capacity of temporary centers and improving the capacity of current centers and hospitals. The results of the study show that the hierarchical model of location allocation of capacity optimization reduces the density of casualties, costs and treatment time in disaster. The destructive impact of natural disasters emphasizes the importance of logistics and human resource planning in the pre- and post- disaster periods. In the event of a disaster, in order to provide immediate relief, the Health Hierarchical Network, which includes clinics and hospitals, will be activated. In this paper, using a mixed integer mathematical model and assuming the current location of hospitals and clinics, optimal locations are determined as temporary treatment emergency centers and the optimal allocation of casualties from urban areas to these centers and then clinics and hospitals are recommended in disaster. Using the simulation model, the moment of disaster and the rescue process were simulated and then the optimization approach was adopted based on simulating the optimal capacity of temporary centers and improving the capacity of current centers and hospitals. The results of the study show that the hierarchical model of location allocation of capacity optimization reduces the density of casualties, costs and treatment time in disaster. Disaster Management Temporary Medical Centers Simulation-Based Optimization Hierarchical Mathematical Model Treatment Network Design https://jimp.sbu.ac.ir/article_87606_3994f92e283d1fd9f900d308f4864a7c.pdf
Shahid Beheshti University Journal of Industrial Management Perspective 2251-9874 11 2 2021 06 22 Design of Fuzzy Inference System for Green Supply Chain Evaluation of Export Manufacturing Companies Design of Fuzzy Inference System for Green Supply Chain Evaluation of Export Manufacturing Companies 125 144 100700 10.52547/jimp.11.2.125 FA Easa Narimani Ghourtoular Ph.D. Student, Islamic Azad University, UAE Branch. Naser Feg-Hi Farahmand Associate Professor, Islamic Azad University, Tabriz Branch. Nazanin Pilevari Associate Professor, Islamic Azad University, West Tehran Branch. Kamaleddin Rahmani Associate Professor, Islamic Azad University, Tabriz Branch. Mohammad Reza Motadel Assistant Professor, Islamic Azad University, Central Tehran Branch. Journal Article 2020 06 18 This paper aims to design a fuzzy inference system to evaluate the green supply chain of export manufacturing companies. This research has been applied from the point of view of purpose. The statistical population of this study included export manufacturing companies in the northwest of the country. The statistical sample is targeted, and 143 companies are determined. A research questionnaire based on the research literature was used to collect the data. In order to examine the validity of the questionnaire, while using formal validity, the validity of the structure has been used based on confirmatory factor analysis. Cronbach's alpha coefficient was also used to evaluate the reliability of the questionnaire. The research questionnaires were distributed among the statistical sample members of the research after confirming the validity and reliability. In order to evaluate the green supply chain of companies, a fuzzy inference system has been used based on triangular membership functions and Mamdani inference. The results show that the designed system is able to show how green the supply chain of companies is based on numerical values ​​and linguistic terms. This paper aims to design a fuzzy inference system to evaluate the green supply chain of export manufacturing companies. This research has been applied from the point of view of purpose. The statistical population of this study included export manufacturing companies in the northwest of the country. The statistical sample is targeted, and 143 companies are determined. A research questionnaire based on the research literature was used to collect the data. In order to examine the validity of the questionnaire, while using formal validity, the validity of the structure has been used based on confirmatory factor analysis. Cronbach's alpha coefficient was also used to evaluate the reliability of the questionnaire. The research questionnaires were distributed among the statistical sample members of the research after confirming the validity and reliability. In order to evaluate the green supply chain of companies, a fuzzy inference system has been used based on triangular membership functions and Mamdani inference. The results show that the designed system is able to show how green the supply chain of companies is based on numerical values ​​and linguistic terms. Fuzzy Inference System Green Supply Chain Export Manufacturing Companies. Input Operation Production Operations Output Operations https://jimp.sbu.ac.ir/article_100700_2cce463d64607d828ce479089c86be89.pdf
Shahid Beheshti University Journal of Industrial Management Perspective 2251-9874 11 2 2021 06 22 A Two-Stage Model for Rice Cultivation Preparation Considering Dynamic Uncertainty: A Case Study in Iran A Two-Stage Model for Rice Cultivation Preparation Considering Dynamic Uncertainty: A Case Study in Iran 145 176 101050 10.52547/jimp.11.2.145 FA Hossein Ebrahimi Mahmoudi Master student, Iran University of Science and Technology Mir Saman Pishvaei Associate Professor, Iran University of Science and Technology. Ebrahim Teymouri Associate Professor, Iran University of Science and Technology. Journal Article 2020 08 19 As one of the essential commodities in the agricultural sector, rural farmers generally cultivate rice based on experience in agricultural fields. This method of cultivation has led to a waste of natural resources. In this study, the aim is to find suitable areas and determine the pattern of rice cultivation using a two-phase methodology. In the first phase, GIS integration and the best-worst method have been used to classify suitable areas for rice cultivation in Iran. The first phase's result is considered an input to the second phase, i.e., the optimization model to determine the pattern of rice cultivation. A multi-stage stochastic optimization approach has been used in the second phase to consider the weather uncertainty in all periods. Climatic conditions in each period are modeled in three scenarios. The application of the proposed model has been investigated in a case study in Iran. As a result, it has been observed that most of the suitable areas for rice cultivation are located in the north and western parts of Iran. Also, the suitable cultivation pattern for most rice farmers is the high-yield cultivation using the transplanting method. As one of the essential commodities in the agricultural sector, rural farmers generally cultivate rice based on experience in agricultural fields. This method of cultivation has led to a waste of natural resources. In this study, the aim is to find suitable areas and determine the pattern of rice cultivation using a two-phase methodology. In the first phase, GIS integration and the best-worst method have been used to classify suitable areas for rice cultivation in Iran. The first phase's result is considered an input to the second phase, i.e., the optimization model to determine the pattern of rice cultivation. A multi-stage stochastic optimization approach has been used in the second phase to consider the weather uncertainty in all periods. Climatic conditions in each period are modeled in three scenarios. The application of the proposed model has been investigated in a case study in Iran. As a result, it has been observed that most of the suitable areas for rice cultivation are located in the north and western parts of Iran. Also, the suitable cultivation pattern for most rice farmers is the high-yield cultivation using the transplanting method. Land Preparation Rice Cultivation Pattern GIS multi-stage stochastic programming Best-Worst Method https://jimp.sbu.ac.ir/article_101050_e4e6a2ece3c458d8cf8de94b4ad7c7fd.pdf
Shahid Beheshti University Journal of Industrial Management Perspective 2251-9874 11 2 2021 06 22 Identification and Prioritizing Delay Factors and Timely Delivery Solutions Based on EFQM in the Aviation Industry Identification and Prioritizing Delay Factors and Timely Delivery Solutions Based on EFQM in the Aviation Industry 177 205 101010 10.52547/jimp.11.2.177 FA Ommolbanin Yousefi Assistant Professor, Malek-e-Ashtar University of Technology, Shahin shahr. Abdolrasool Noroozi MSc Student, Islamic Azad Univercity, Najafabad Branch. Neda Hajheidari MSc Student, Malek-e-Ashtar University of Technology, Shahin shahr. Journal Article 2020 02 09 In today's competitive world, compliance with customer requirements is a primary requisite of staying in a non-exclusive and competitive market. The late delivery of products results in the dissatisfaction of customers and imposes additional costs. Hence, one of the critical problems in the organization is the delay of the projects, which makes considering strategies for addressing them essential. This research was carried out in the industry of manufacturing airborne items and systems in 2020 to identify and prioritize the delay factors and timely delivery strategies of products. For this purpose, identifying and classifying the delay factors is done through the organization excellence model and then the dependence network relations of delay factors and solution determined by interpretive structure modeling technique and their impact on delays calculated by network analysis. Finally, by analyzing the significance-performance and the three indexes cost solution for implementing corrective amount, duration and availability for modification and feasible, acceptable solutions were identified and prioritized. The findings showed that 45 factors were root causes of delay, from among 113 detected delay factors, and after interpretive structure modeling, 14 corrective strategies and weighting and prioritizing were proposed by the experts. In today's competitive world, compliance with customer requirements is a primary requisite of staying in a non-exclusive and competitive market. The late delivery of products results in the dissatisfaction of customers and imposes additional costs. Hence, one of the critical problems in the organization is the delay of the projects, which makes considering strategies for addressing them essential. This research was carried out in the industry of manufacturing airborne items and systems in 2020 to identify and prioritize the delay factors and timely delivery strategies of products. For this purpose, identifying and classifying the delay factors is done through the organization excellence model and then the dependence network relations of delay factors and solution determined by interpretive structure modeling technique and their impact on delays calculated by network analysis. Finally, by analyzing the significance-performance and the three indexes cost solution for implementing corrective amount, duration and availability for modification and feasible, acceptable solutions were identified and prioritized. The findings showed that 45 factors were root causes of delay, from among 113 detected delay factors, and after interpretive structure modeling, 14 corrective strategies and weighting and prioritizing were proposed by the experts. Delay Organization Excellence Model Interpretative Structure Modeling Multi-criteria Decision Making Importance- Performance analysis https://jimp.sbu.ac.ir/article_101010_74b1712c3283694e64af3e80ac099eea.pdf
Shahid Beheshti University Journal of Industrial Management Perspective 2251-9874 11 2 2021 06 22 Comparative-fuzzy Analysis of National Innovation Capability Based on Results of Dynamic Network DEA Model Comparative-fuzzy Analysis of National Innovation Capability Based on Results of Dynamic Network DEA Model 207 246 101163 10.52547/jimp.11.2.207 FA Mohammad Ali Torabandeh Ph.D Student, Shahid Beheshti University. Behrouz Dorri Nokorani Professor, Shahid Beheshti University. Alireza Motameni Associate Professor, Shahid Beheshti University. Masood Rabieh Assistant Professor, Shahid Beheshti University. Journal Article 2021 03 01 In this article, by presenting the scope of national innovation capability in the context of a multi-sector system, a dynamic network model is introduced. In this system, to identify Iran's performance problem, at first by bibliometric studying and holding focus group sessions with experts, the steps and indicators of the processed model were identified and designed. Then, the dynamic network data envelopment analysis model was implemented to compare Iran's performance with other countries. The model results indicated that Iran's national innovation capability has a poor performance in the third phase of its three steps that include converting patents to high-tech products and creative goods. Then, to present the proposed policy to enhance Iran’s performance in the third step of the mentioned model, by using qualitative comparative analysis of fuzzy set (fSQCA), the combinations of institutional, human capital and research, infrastructure, market sophistication, and business sophistication dimensions were investigated. For calibration of these data, K-MEANS clustering method was used. The output of the mentioned comparative analysis indicated that the combination of the two dimensions of institutions and human capital and research in promoting the country's performance is sufficient. In this article, by presenting the scope of national innovation capability in the context of a multi-sector system, a dynamic network model is introduced. In this system, to identify Iran's performance problem, at first by bibliometric studying and holding focus group sessions with experts, the steps and indicators of the processed model were identified and designed. Then, the dynamic network data envelopment analysis model was implemented to compare Iran's performance with other countries. The model results indicated that Iran's national innovation capability has a poor performance in the third phase of its three steps that include converting patents to high-tech products and creative goods. Then, to present the proposed policy to enhance Iran’s performance in the third step of the mentioned model, by using qualitative comparative analysis of fuzzy set (fSQCA), the combinations of institutional, human capital and research, infrastructure, market sophistication, and business sophistication dimensions were investigated. For calibration of these data, K-MEANS clustering method was used. The output of the mentioned comparative analysis indicated that the combination of the two dimensions of institutions and human capital and research in promoting the country's performance is sufficient. National Innovation capability Bibliometric Analysis Dynamic Network DEA Fuzzy Set Qualitative Comparative Analysis K-MEANS Clustering Method https://jimp.sbu.ac.ir/article_101163_17435c7f74e4ddcb62d35a810adb20f6.pdf
Shahid Beheshti University Journal of Industrial Management Perspective 2251-9874 11 2 2021 06 22 The Relationship between Logistics Capability and Risk in Freight Transport Supply Chain Resilience with Canonical Correlation Analysis Approach The Relationship between Logistics Capability and Risk in Freight Transport Supply Chain Resilience with Canonical Correlation Analysis Approach 247 270 101266 10.52547/jimp.11.2.247 FA Mansour Jangizehi Department of Industrial Engineering, Payame Noor University. Ahmad Gaini Assistant Professor, Imam Hussein University. Journal Article 2020 07 18 In this study, to find supply chain resilience strategies, 14 indicators for logistics capability and 13 indicators for risk were achieved from factor analysis. The logistics capability questionnaire was filled by customers, and the risk questionnaire was answered by managers and employees of transport companies. Six factors for the logistics capability and five factors for the risk were detected through factor analysis. Indicators were analyzed by canonical correlation using SPSS software. Findings from the canonical correlation analysis show a suitable linear combination and a significant correlation between the logistics capability and risk variables. In the first canonical coefficient, the amount of variance was 81%, and in the second canonical coefficient of variance, 78% was obtained between two canonical linear combinations. The results show that risk indices can cause changes in logistics capability up to 13.2%, and logistics capability indices can change up to 16.8% in the risk variable. Moreover, insurance and safety guarantee indicators and timely delivery of goods have the greatest impact on improving logistics, and fleet delays and fuel supply problems of the transport fleet have the greatest impact on increasing risk. In this study, to find supply chain resilience strategies, 14 indicators for logistics capability and 13 indicators for risk were achieved from factor analysis. The logistics capability questionnaire was filled by customers, and the risk questionnaire was answered by managers and employees of transport companies. Six factors for the logistics capability and five factors for the risk were detected through factor analysis. Indicators were analyzed by canonical correlation using SPSS software. Findings from the canonical correlation analysis show a suitable linear combination and a significant correlation between the logistics capability and risk variables. In the first canonical coefficient, the amount of variance was 81%, and in the second canonical coefficient of variance, 78% was obtained between two canonical linear combinations. The results show that risk indices can cause changes in logistics capability up to 13.2%, and logistics capability indices can change up to 16.8% in the risk variable. Moreover, insurance and safety guarantee indicators and timely delivery of goods have the greatest impact on improving logistics, and fleet delays and fuel supply problems of the transport fleet have the greatest impact on increasing risk. logistics capability Risk Resilience Freight Transport Canonical correlation https://jimp.sbu.ac.ir/article_101266_eda86957b418e30e9d2e51d3a9392f6d.pdf
Shahid Beheshti University Journal of Industrial Management Perspective 2251-9874 11 2 2021 06 22 Mathematical Model of Location, Multi-Commodity and Multi-Period in Sustainable Closed-Loop Supply Chain Considering Risk and Demand and Quality Uncertainty (A case Study) Mathematical Model of Location, Multi-Commodity and Multi-Period in Sustainable Closed-Loop Supply Chain Considering Risk and Demand and Quality Uncertainty (A case Study) 271 304 101049 10.52547/jimp.11.2.271 FA Sina Sajedi Ph.D Student, South Tehran Branch, Islamic Azad University. Amir Homayoun Sarfaraz Assistant Professor, South Tehran Branch, Islamic Azad University. Shahrooz Bamdad Assistant Professor, South Tehran Branch, Islamic Azad University. Kaveh Khalili-Damghani Associate Professor, South Tehran Branch, Islamic Azad University. Journal Article 2020 05 24 The main objective of sustainable supply chain is to balance the economic, environmental, and social goals that companies have to use closed-loop supply chains for cost reduction and increasing the efficiency of the supply chain. According to the research literature, considering the risk in supply chains, especially the return supply chain, is one of the topics that has been little studied. Therefore, the aim of this study is to locate the components of a three-objective, sustainable closed-loop, multi-commodity, and multi-period supply chain, considering uncertainty and market scenarios with a risk approach. Location in the sustainable closed-loop supply chain, considering the risk, and also paying attention to the quality of manufactured products and different scenarios of demand are among the innovations of this research. Due to the NP-Hard nature of the problem, the model is solved by the nondominated sorting genetic algorithm II (NSGA-II). Sensitivity analysis has been performed on the parameters of the problem, and the efficiency of the studied methods has been investigated. The average Pareto points obtained from the first objective function is 56789.9, the average Pareto points for the second objective function is 1828.8 and for the third objective function is 77365.32, and also the average solution time of the model is 15.9 seconds. The main objective of sustainable supply chain is to balance the economic, environmental, and social goals that companies have to use closed-loop supply chains for cost reduction and increasing the efficiency of the supply chain. According to the research literature, considering the risk in supply chains, especially the return supply chain, is one of the topics that has been little studied. Therefore, the aim of this study is to locate the components of a three-objective, sustainable closed-loop, multi-commodity, and multi-period supply chain, considering uncertainty and market scenarios with a risk approach. Location in the sustainable closed-loop supply chain, considering the risk, and also paying attention to the quality of manufactured products and different scenarios of demand are among the innovations of this research. Due to the NP-Hard nature of the problem, the model is solved by the nondominated sorting genetic algorithm II (NSGA-II). Sensitivity analysis has been performed on the parameters of the problem, and the efficiency of the studied methods has been investigated. The average Pareto points obtained from the first objective function is 56789.9, the average Pareto points for the second objective function is 1828.8 and for the third objective function is 77365.32, and also the average solution time of the model is 15.9 seconds. Sustainable closed-loop supply chain Uncertainty Risk Assessment https://jimp.sbu.ac.ir/article_101049_33856df3a4d1e024e94c63d72bd9d982.pdf