Pub Date : 2021-07-01DOI: 10.22116/JIEMS.2020.226032.1352
Armin Cheraghalipour, E. Roghanian
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 e-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.
{"title":"Determining the optimal portfolio for healthcare processes management using a hybrid decision-making approach","authors":"Armin Cheraghalipour, E. Roghanian","doi":"10.22116/JIEMS.2020.226032.1352","DOIUrl":"https://doi.org/10.22116/JIEMS.2020.226032.1352","url":null,"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 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 e-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.","PeriodicalId":45245,"journal":{"name":"Industrial Engineering and Management Systems","volume":"11 1","pages":"218-239"},"PeriodicalIF":0.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73167415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22116/JIEMS.2020.215206.1330
Behrooz Khorshidvand, H. Soleimani, M. S. Esfahani, S. Sibdari
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.
{"title":"Sustainable closed-loop supply chain network: Mathematical modeling and Lagrangian relaxation","authors":"Behrooz Khorshidvand, H. Soleimani, M. S. Esfahani, S. Sibdari","doi":"10.22116/JIEMS.2020.215206.1330","DOIUrl":"https://doi.org/10.22116/JIEMS.2020.215206.1330","url":null,"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, 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.","PeriodicalId":45245,"journal":{"name":"Industrial Engineering and Management Systems","volume":"13 1","pages":"240-260"},"PeriodicalIF":0.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82112149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22116/JIEMS.2020.227003.1355
A. Oke, J. Abafi, Banji Zacheous Adewole
Equipment breakdown adds to the cost of production and considerably affect the overall equipment efficiency in automated lines due to unplanned downtime. Preventive maintenance with appropriate actions has been considered to enhance products quality, equipment reliability and minimize the probability of system brake down or failure. To this end, this study conducted a reliability status of nine packaging facilities, from the perspective of existing failure data of production system in the Nigerian multinational bottling plant. Failure data of the production system were stratified and analyzed to achieve the failure interval of each of the facilities and the sub-systems. Stratification of failure data resulted to an established input format that fitted the Pareto chart analysis, Weibull Distributions and Reliability/Failure Time analysis. The results showed that the facility with minimum value of reliability was filler machine. A standby filler system was therefore recommended in order to prevent unnecessary idleness of the other facilities especially when the production target is high. The study concluded that, analysis of downtime in a production/manufacturing system assisted in predicting the likely failure interval and hence a preventive maintenance scheduled was proposed.
{"title":"Failure data analysis for preventive maintenance scheduling of a bottling company production system","authors":"A. Oke, J. Abafi, Banji Zacheous Adewole","doi":"10.22116/JIEMS.2020.227003.1355","DOIUrl":"https://doi.org/10.22116/JIEMS.2020.227003.1355","url":null,"abstract":"Equipment breakdown adds to the cost of production and considerably affect the overall equipment efficiency in automated lines due to unplanned downtime. Preventive maintenance with appropriate actions has been considered to enhance products quality, equipment reliability and minimize the probability of system brake down or failure. To this end, this study conducted a reliability status of nine packaging facilities, from the perspective of existing failure data of production system in the Nigerian multinational bottling plant. Failure data of the production system were stratified and analyzed to achieve the failure interval of each of the facilities and the sub-systems. Stratification of failure data resulted to an established input format that fitted the Pareto chart analysis, Weibull Distributions and Reliability/Failure Time analysis. The results showed that the facility with minimum value of reliability was filler machine. A standby filler system was therefore recommended in order to prevent unnecessary idleness of the other facilities especially when the production target is high. The study concluded that, analysis of downtime in a production/manufacturing system assisted in predicting the likely failure interval and hence a preventive maintenance scheduled was proposed.","PeriodicalId":45245,"journal":{"name":"Industrial Engineering and Management Systems","volume":"116 1","pages":"32-44"},"PeriodicalIF":0.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73407544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22116/JIEMS.2020.218468.1368
A. Shahabi, S. Raissi, K. Khalili-Damghani, M. Rafei
Avoiding the passengers extra waiting time is a vital task for rail planners. The current research focused on minimizing the passenger waiting time on the presence of real frequently random occurred disturbances. Details of the proposed model are on the 1st line of Tehran underground rail rapid transit. All fitness functions are validated using the analysis of variance (ANOVA) by applying the hypothesis testing method. Also, a validated discrete-event computer simulation model is applied to examine the average waiting time per passenger as the key performance measure under different scenarios generated using full factorial design of experiments. The validity of the obtained optimal solution, i.e., train headway times is confirmed at a 95% level of reliability. Also, simulation outcomes indicated that the proposed response surface meta-model could efficiently provide a more reliable train operation plan to ensure a desirable level of system resiliency on the presence of random disturbances. The numerical results indicated that wait time could be reduced by 14.8% for passengers as compared with the baseline train headway plan.
{"title":"Optimizing resiliency of train operations in an underground metro: A hybrid discrete-event simulation and response surface methodology","authors":"A. Shahabi, S. Raissi, K. Khalili-Damghani, M. Rafei","doi":"10.22116/JIEMS.2020.218468.1368","DOIUrl":"https://doi.org/10.22116/JIEMS.2020.218468.1368","url":null,"abstract":"Avoiding the passengers extra waiting time is a vital task for rail planners. The current research focused on minimizing the passenger waiting time on the presence of real frequently random occurred disturbances. Details of the proposed model are on the 1st line of Tehran underground rail rapid transit. All fitness functions are validated using the analysis of variance (ANOVA) by applying the hypothesis testing method. Also, a validated discrete-event computer simulation model is applied to examine the average waiting time per passenger as the key performance measure under different scenarios generated using full factorial design of experiments. The validity of the obtained optimal solution, i.e., train headway times is confirmed at a 95% level of reliability. Also, simulation outcomes indicated that the proposed response surface meta-model could efficiently provide a more reliable train operation plan to ensure a desirable level of system resiliency on the presence of random disturbances. The numerical results indicated that wait time could be reduced by 14.8% for passengers as compared with the baseline train headway plan.","PeriodicalId":45245,"journal":{"name":"Industrial Engineering and Management Systems","volume":"56 1","pages":"135-151"},"PeriodicalIF":0.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73934275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22116/JIEMS.2020.226584.1353
Seyed Mohammad Hadian, H. Farughi, H. Rasay
In this paper, a mathematical model is presented for the integrated planning of maintenance, quality control and production control in deteriorating production systems. The simultaneous consideration of these three factors improves the efficiency of the production process and leads to high-quality products. In this study, a single machine produces a product with a known and constant production rate per time unit and the production process has two operational states, i.e. in-control state and out-of-control state, and the probability of the state transition follows a general distribution. To monitor the process, sampling inspection is conducted during a production cycle and a proper control chart is applied. In the developed model, there is no restriction on the type of the control chart. Therefore, different control charts can be applied in practice for quality control. The lot size produced in each production cycle is determined with respect to the production rate of the machine and the proportion of conforming and non-conforming items produced in each cycle. In this study, preventive maintenance and corrective maintenance as perfect maintenance actions and minimal maintenance as imperfect maintenance action are applied to maintain the process in a proper condition. The objective of the integrated model is to plan the maintenance actions, determine the optimal values of the control chart parameters and optimize the production level to minimize the expected total cost of the process per time unit. To evaluate the performance of this model, a numerical study is solved and a sensitivity analysis is conducted on the critical parameters and the obtained results are analyzed.
{"title":"Coordination of the decisions associated with maintenance, quality control and production in imperfect deteriorating production systems","authors":"Seyed Mohammad Hadian, H. Farughi, H. Rasay","doi":"10.22116/JIEMS.2020.226584.1353","DOIUrl":"https://doi.org/10.22116/JIEMS.2020.226584.1353","url":null,"abstract":"In this paper, a mathematical model is presented for the integrated planning of maintenance, quality control and production control in deteriorating production systems. The simultaneous consideration of these three factors improves the efficiency of the production process and leads to high-quality products. In this study, a single machine produces a product with a known and constant production rate per time unit and the production process has two operational states, i.e. in-control state and out-of-control state, and the probability of the state transition follows a general distribution. To monitor the process, sampling inspection is conducted during a production cycle and a proper control chart is applied. In the developed model, there is no restriction on the type of the control chart. Therefore, different control charts can be applied in practice for quality control. The lot size produced in each production cycle is determined with respect to the production rate of the machine and the proportion of conforming and non-conforming items produced in each cycle. In this study, preventive maintenance and corrective maintenance as perfect maintenance actions and minimal maintenance as imperfect maintenance action are applied to maintain the process in a proper condition. The objective of the integrated model is to plan the maintenance actions, determine the optimal values of the control chart parameters and optimize the production level to minimize the expected total cost of the process per time unit. To evaluate the performance of this model, a numerical study is solved and a sensitivity analysis is conducted on the critical parameters and the obtained results are analyzed.","PeriodicalId":45245,"journal":{"name":"Industrial Engineering and Management Systems","volume":"9 1","pages":"89-113"},"PeriodicalIF":0.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77597169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22116/JIEMS.2021.252150.1395
I. Seyedi, M. Hamedi, Reza Tavakkoli-Moghadaam
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.
{"title":"Developing a mathematical model for a multi-door cross-dock scheduling problem with human factors: A modified imperialist competitive algorithm","authors":"I. Seyedi, M. Hamedi, Reza Tavakkoli-Moghadaam","doi":"10.22116/JIEMS.2021.252150.1395","DOIUrl":"https://doi.org/10.22116/JIEMS.2021.252150.1395","url":null,"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 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.","PeriodicalId":45245,"journal":{"name":"Industrial Engineering and Management Systems","volume":"126 1","pages":"180-201"},"PeriodicalIF":0.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78733864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-30DOI: 10.7232/iems.2021.20.2.258
A. Suychinov, M. Rebezov, L. Tretyak, V. Zhenzhebir, N. Maksimiuk, R. Pavlov, A. Ostapenko, Yu.I. Zubtsova, G. Abdilova
{"title":"Random Optimization of the Green Closed Chain Supply Chain of Perishable Products","authors":"A. Suychinov, M. Rebezov, L. Tretyak, V. Zhenzhebir, N. Maksimiuk, R. Pavlov, A. Ostapenko, Yu.I. Zubtsova, G. Abdilova","doi":"10.7232/iems.2021.20.2.258","DOIUrl":"https://doi.org/10.7232/iems.2021.20.2.258","url":null,"abstract":"","PeriodicalId":45245,"journal":{"name":"Industrial Engineering and Management Systems","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48212112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-30DOI: 10.7232/iems.2021.20.2.192
R. Setiawan
In today's competitive environment, production efficiency is a very important and key issue in success in the market. However, all decisions of the production unit are interdependent and it is necessary to use an integrated form which leads to finding a better approach for the management. Accordingly, in this research, the integration of three important fields in manufacturing companies has been addressed. These fields include production planning, maintenance, and labor scheduling. In this regard, a novel mathematical model with the aim of optimal use of labor and increasing production volume is presented. In this model of workers’ experience, machine utilization rate and machine failure rate are expressed using fuzzy numbers. To optimize this model, the ant colony optimization algorithm has been used. Numerical results obtained from the implementation of the mathematical model and solution method show that the used algorithm can provide solutions with the least possible error in a reasonable time. Moreover, the sensitivity analysis shows that the failure rate of the machine before and after maintenance has a great impact on the objective function of the mathematical model.
{"title":"Mathematical Model Developed Using Meta-Initiative Optimization Algorithm for Production and Labor Planning","authors":"R. Setiawan","doi":"10.7232/iems.2021.20.2.192","DOIUrl":"https://doi.org/10.7232/iems.2021.20.2.192","url":null,"abstract":"In today's competitive environment, production efficiency is a very important and key issue in success in the market. However, all decisions of the production unit are interdependent and it is necessary to use an integrated form which leads to finding a better approach for the management. Accordingly, in this research, the integration of three important fields in manufacturing companies has been addressed. These fields include production planning, maintenance, and labor scheduling. In this regard, a novel mathematical model with the aim of optimal use of labor and increasing production volume is presented. In this model of workers’ experience, machine utilization rate and machine failure rate are expressed using fuzzy numbers. To optimize this model, the ant colony optimization algorithm has been used. Numerical results obtained from the implementation of the mathematical model and solution method show that the used algorithm can provide solutions with the least possible error in a reasonable time. Moreover, the sensitivity analysis shows that the failure rate of the machine before and after maintenance has a great impact on the objective function of the mathematical model.","PeriodicalId":45245,"journal":{"name":"Industrial Engineering and Management Systems","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42077209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-30DOI: 10.7232/iems.2021.20.2.304
Zh. Abylkassimova, Yermek Abilmazhinov, M. Rebezov, N. Maksimiuk, Yury Obolonskiy, R. Zalilov, Svetlana Shamina, Konstantin Kolyazov, Yu. A. Dudko
{"title":"Integrated Production and Distribution Scheduling in the Dual-Purpose Supply Chain with Environmental Aspects and Delays","authors":"Zh. Abylkassimova, Yermek Abilmazhinov, M. Rebezov, N. Maksimiuk, Yury Obolonskiy, R. Zalilov, Svetlana Shamina, Konstantin Kolyazov, Yu. A. Dudko","doi":"10.7232/iems.2021.20.2.304","DOIUrl":"https://doi.org/10.7232/iems.2021.20.2.304","url":null,"abstract":"","PeriodicalId":45245,"journal":{"name":"Industrial Engineering and Management Systems","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47002067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-30DOI: 10.7232/iems.2021.20.2.130
B. Almansour, M. M. Alshater, A. Almansour
{"title":"Performance of ARCH and GARCH Models in Forecasting Cryptocurrency Market Volatility","authors":"B. Almansour, M. M. Alshater, A. Almansour","doi":"10.7232/iems.2021.20.2.130","DOIUrl":"https://doi.org/10.7232/iems.2021.20.2.130","url":null,"abstract":"","PeriodicalId":45245,"journal":{"name":"Industrial Engineering and Management Systems","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44213491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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