Pub Date : 2021-09-12DOI: 10.22094/JOIE.2021.1906935.1780
V. V. Singh, P. K. Poonia
This paper describes the investigation of different reliability measures of a complex system consisting of two subsystems with controllers in a series configuration, which is a useful opportunity for specific design problems. Subsystem-1 consisting n units functioning under the policy k-out-of-n: G; policy, and subsystem-2 has m units and operating under r-out-of-m: G; policy. The system failure rates of both subsystems are constant and assumed to obey an exponential distribution; two types of distribution are allowed to repair: general distribution and Gumbel-Hougaard family copula distribution. The system's partially failed states/ completely failed states are repaired using General/ copula distribution. After repair, the units in both the subsystems are "as good as new." The controller control both subsystems and the failure of controllers brings the subsystem in the complete failed state. The operator may fail the system deliberately if not satisfied with the organization. The system is analyzed employing the supplementary variable technique, and Laplace transforms implications and traditional system reliability measures, such as the system's availability, system reliability, and profit analysis, have been computed for particular values of failure and repair parameters.
{"title":"Stochastic analysis of k-out-of-n: G type of repairable system in combination of subsystems with controllers and multi repair approach","authors":"V. V. Singh, P. K. Poonia","doi":"10.22094/JOIE.2021.1906935.1780","DOIUrl":"https://doi.org/10.22094/JOIE.2021.1906935.1780","url":null,"abstract":"This paper describes the investigation of different reliability measures of a complex system consisting of two subsystems with controllers in a series configuration, which is a useful opportunity for specific design problems. Subsystem-1 consisting n units functioning under the policy k-out-of-n: G; policy, and subsystem-2 has m units and operating under r-out-of-m: G; policy. The system failure rates of both subsystems are constant and assumed to obey an exponential distribution; two types of distribution are allowed to repair: general distribution and Gumbel-Hougaard family copula distribution. The system's partially failed states/ completely failed states are repaired using General/ copula distribution. After repair, the units in both the subsystems are \"as good as new.\" The controller control both subsystems and the failure of controllers brings the subsystem in the complete failed state. The operator may fail the system deliberately if not satisfied with the organization. The system is analyzed employing the supplementary variable technique, and Laplace transforms implications and traditional system reliability measures, such as the system's availability, system reliability, and profit analysis, have been computed for particular values of failure and repair parameters.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42956107","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 : 2021-09-12DOI: 10.22094/JOIE.2021.1873323.1670
S. A. Darestani, Mollaie Ammar, Deneise Dadd
In the scope of center location problem, transfer point location problems (TPLP) are the ones which have been studied more recently to make models more applicable in real world. The contribution of this work is to develop a model in which demand points are weighted and have a normal distribution. As an assumption, there is no transformation directly from a demand point to the service facility location. This means that the transfer point is always engaged. The contribution of work is summarized in two models. In the first model, all the points are considered in an area while in the second one the points are considered in several areas. The problem is to find out the best location for the transfer point so that the maximum expected weighted distance to all demand points through the transfer point is minimized. A mathematical solution is employed when demand points follow normal distribution, with some points of demands being in regions. Then, this model was solved by replacing real number in a real condition. We used Maple software to solve this objective function as well as MATLAB software to solve this model numerically.
{"title":"Developing a transfer point location problem considering normal demands distribution","authors":"S. A. Darestani, Mollaie Ammar, Deneise Dadd","doi":"10.22094/JOIE.2021.1873323.1670","DOIUrl":"https://doi.org/10.22094/JOIE.2021.1873323.1670","url":null,"abstract":"In the scope of center location problem, transfer point location problems (TPLP) are the ones which have been studied more recently to make models more applicable in real world. The contribution of this work is to develop a model in which demand points are weighted and have a normal distribution. As an assumption, there is no transformation directly from a demand point to the service facility location. This means that the transfer point is always engaged. The contribution of work is summarized in two models. In the first model, all the points are considered in an area while in the second one the points are considered in several areas. The problem is to find out the best location for the transfer point so that the maximum expected weighted distance to all demand points through the transfer point is minimized. A mathematical solution is employed when demand points follow normal distribution, with some points of demands being in regions. Then, this model was solved by replacing real number in a real condition. We used Maple software to solve this objective function as well as MATLAB software to solve this model numerically.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44719490","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 : 2021-09-02DOI: 10.22094/JOIE.2021.1899316.1746
H. S. Tooranloo, Salim Karimi Takalo, Fatemeh Mohyadini
The healthcare sector is one of the largest service industries with the highest potential to improve environmental performance. Hospitals as an important part of the healthcare system must act in a way that reduces their environmental consequences, which requires having a green supplier. The aim of this study was to identify the effective factors on the green supplier selection (GSS) in the hospital and to present an excellent model for analyzing the relationships between these factors. In this study, 14 concepts that effect the green supplier selection of a hospital have been extracted from in-depth literature and interviews entailing: financial capability, creativity and innovation, green technology, flexibility, organizational capability, commitment, trust on supplier, green quality, green transportation, environmental cooperation with customers, hazardous materials management, buy green, green warehouse and green packaging.In addition Intuitive fuzzy cognitive mapping approach was also used for data analysis and conclusion. The results showed that green technology index with 0.43 degree was the most influential and organizational capability index with 0.29 degree had the most influence over the other concepts. In addition, focusing on concepts like financial capability, trust on supplier and creativity and innovation process of green supplier selection of hospital. Taking these in consideration these factors should be given specific attention.
{"title":"Analysis of Causal Relationships Effective Factors on the Green Supplier Selection in Health Centers Using the Intuitionistic Fuzzy Cognitive Map (IFCM) Method","authors":"H. S. Tooranloo, Salim Karimi Takalo, Fatemeh Mohyadini","doi":"10.22094/JOIE.2021.1899316.1746","DOIUrl":"https://doi.org/10.22094/JOIE.2021.1899316.1746","url":null,"abstract":"The healthcare sector is one of the largest service industries with the highest potential to improve environmental performance. Hospitals as an important part of the healthcare system must act in a way that reduces their environmental consequences, which requires having a green supplier. The aim of this study was to identify the effective factors on the green supplier selection (GSS) in the hospital and to present an excellent model for analyzing the relationships between these factors. In this study, 14 concepts that effect the green supplier selection of a hospital have been extracted from in-depth literature and interviews entailing: financial capability, creativity and innovation, green technology, flexibility, organizational capability, commitment, trust on supplier, green quality, green transportation, environmental cooperation with customers, hazardous materials management, buy green, green warehouse and green packaging.In addition Intuitive fuzzy cognitive mapping approach was also used for data analysis and conclusion. The results showed that green technology index with 0.43 degree was the most influential and organizational capability index with 0.29 degree had the most influence over the other concepts. In addition, focusing on concepts like financial capability, trust on supplier and creativity and innovation process of green supplier selection of hospital. Taking these in consideration these factors should be given specific attention.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44144209","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 : 2021-09-02DOI: 10.22094/JOIE.2021.1864151.1634
Fatemeh Keshavarz-Ghorbani, S. Pasandideh
The main aim of this research is to find the best inventory review policy for different types of items in group B in ABC analysis through minimizing the total cost of the system and maximizing the service level. Moreover, this study has considered several operational constraints such as limitations on storage space, number of orders, and allowable shortage. To solve this problem, first, an individual optimization method is utilized to obtain optimal solutions. Then, two classic and novel multi-objective optimization methods have been used to convert the bi-objective problem to a single-objective and reach the near-optimal solutions for both objectives simultaneously. Finally, the proposed methods are compared in terms of objective function values and computational time to find the better method.
{"title":"A bi-objective non-linear approach for determining the ordering strategy for group B in ABC analysis inventory","authors":"Fatemeh Keshavarz-Ghorbani, S. Pasandideh","doi":"10.22094/JOIE.2021.1864151.1634","DOIUrl":"https://doi.org/10.22094/JOIE.2021.1864151.1634","url":null,"abstract":"The main aim of this research is to find the best inventory review policy for different types of items in group B in ABC analysis through minimizing the total cost of the system and maximizing the service level. Moreover, this study has considered several operational constraints such as limitations on storage space, number of orders, and allowable shortage. To solve this problem, first, an individual optimization method is utilized to obtain optimal solutions. Then, two classic and novel multi-objective optimization methods have been used to convert the bi-objective problem to a single-objective and reach the near-optimal solutions for both objectives simultaneously. Finally, the proposed methods are compared in terms of objective function values and computational time to find the better method.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42982086","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}
Ethiopia's industrial development strategy is characterized by manufacturing-led and expansion labor-intensive industrialization. The country expects to generate more income from the exported market. However, the case company is still known not to become productive as much as possible due to different reasons. One of the big challenges of the company has the problem with holding inappropriate inventory and with determines their optimal cost due to poor production planning. So that to solve this problem objective of the paper is to minimize total cost through the integration of seasonal forecasting and integer programming model without violating demand fulfillments. This technique improves resource utilization and enhances inventory control or stock control system. Currently, the company produces different kinds of products grouped into four common types of products (knitted garment, knitted fabric, woven garment, and woven fabric). The data survey system was both primary and secondary system and classified the products using A B C (always better classification) classification. The optimal solution was settled through the integration of seasonal forecasting and integer programming. As the Sensitivity analysis indicated the a big gap between production capacity and actual demand of the products. As the optimized solution indicated that total cost of production cost and inventory cost was minimized and the optimal production plan as well safety stock levels in each quarter was settled. Seasonal demand forecasting is a key activity for a garment which more or less controls all activities of production processes since garment products are affected by seasonal. As the result and discussion have shown that after optimized increase profit of the company through minimizing production cost and inventory costs since both costs are the big constraint of the company. Based on the optimized solution finding annually total cost needs for each A, B, and C – categories products are 57,225,920 BIRR 4,733,013 BIRR, 8,229,309 BIRR, respectively for production and inventory costs. The optimized solution indicated that if the company implemented exactly the proposed solution it will get an additional,4,219,788.8 BIRR,772,055.8 BIRR,2,119,824.2 BIRR respectively for A, B, C categories products totally around 7,111,668.8 BIRR profit per year will get. To end, it was concluded that this remarkable profit increment of the case company can certainly enhance its productivity and worldwide competitiveness. This research will create further pathways for other researchers to accomplish substantial studies on other garment sectors or other manufacturing industries based on local and international perspectives.
{"title":"Optimization of Inventory Controlling System Using Integrated Seasonal forecasting and Integer Programming","authors":"Hagazi Abrha Heniey, K. Gebrehiwot, Tsegay Berhe Desta, Leake Weleabzgi Gebrehiwot","doi":"10.22094/JOIE.2021.1895036.1732","DOIUrl":"https://doi.org/10.22094/JOIE.2021.1895036.1732","url":null,"abstract":"Ethiopia's industrial development strategy is characterized by manufacturing-led and expansion labor-intensive industrialization. The country expects to generate more income from the exported market. However, the case company is still known not to become productive as much as possible due to different reasons. One of the big challenges of the company has the problem with holding inappropriate inventory and with determines their optimal cost due to poor production planning. So that to solve this problem objective of the paper is to minimize total cost through the integration of seasonal forecasting and integer programming model without violating demand fulfillments. This technique improves resource utilization and enhances inventory control or stock control system. Currently, the company produces different kinds of products grouped into four common types of products (knitted garment, knitted fabric, woven garment, and woven fabric). The data survey system was both primary and secondary system and classified the products using A B C (always better classification) classification. The optimal solution was settled through the integration of seasonal forecasting and integer programming. As the Sensitivity analysis indicated the a big gap between production capacity and actual demand of the products. As the optimized solution indicated that total cost of production cost and inventory cost was minimized and the optimal production plan as well safety stock levels in each quarter was settled. Seasonal demand forecasting is a key activity for a garment which more or less controls all activities of production processes since garment products are affected by seasonal. As the result and discussion have shown that after optimized increase profit of the company through minimizing production cost and inventory costs since both costs are the big constraint of the company. Based on the optimized solution finding annually total cost needs for each A, B, and C – categories products are 57,225,920 BIRR 4,733,013 BIRR, 8,229,309 BIRR, respectively for production and inventory costs. The optimized solution indicated that if the company implemented exactly the proposed solution it will get an additional,4,219,788.8 BIRR,772,055.8 BIRR,2,119,824.2 BIRR respectively for A, B, C categories products totally around 7,111,668.8 BIRR profit per year will get. To end, it was concluded that this remarkable profit increment of the case company can certainly enhance its productivity and worldwide competitiveness. This research will create further pathways for other researchers to accomplish substantial studies on other garment sectors or other manufacturing industries based on local and international perspectives.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45978879","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: 10.22094/JOIE.2020.1907303.1783
Kamyar Sabri-Laghaie, R. Noorossana
Accelerated Life Testing (ALT) is very important in evaluating the reliability of highly reliable products. According to ALT procedure, products undergo higher stress levels than normal conditions to reduce the failure times. ALTs have been studied for various conditions and stresses. In addition to common stress such as temperature and humidity, random usage can also be considered as another stress that can cause failure. Design of ALT plan for products which are exposed to random usage process have not been studied in the literature. Therefore, a procedure for designing ALT plan for these products is studied in this paper. To do so, hazard rate of products is formulated based on the random usage process and other stresses. Then, the variance of the hazard rate is estimated over a predetermined time period. Optimum stress levels and the number of units at every stress level are obtained by numerically minimizing the variance of the hazard rate estimate. Numerical example and sensitivity analysis are performed to show the application and robustness of the model to parameter deviations. The results show that the proposed procedure is robust to parameter changes and can be used for ALT planning of products under random usage.
{"title":"Design of Accelerated Life Testing Plans for Products Exposed to Random Usage","authors":"Kamyar Sabri-Laghaie, R. Noorossana","doi":"10.22094/JOIE.2020.1907303.1783","DOIUrl":"https://doi.org/10.22094/JOIE.2020.1907303.1783","url":null,"abstract":"Accelerated Life Testing (ALT) is very important in evaluating the reliability of highly reliable products. According to ALT procedure, products undergo higher stress levels than normal conditions to reduce the failure times. ALTs have been studied for various conditions and stresses. In addition to common stress such as temperature and humidity, random usage can also be considered as another stress that can cause failure. Design of ALT plan for products which are exposed to random usage process have not been studied in the literature. Therefore, a procedure for designing ALT plan for these products is studied in this paper. To do so, hazard rate of products is formulated based on the random usage process and other stresses. Then, the variance of the hazard rate is estimated over a predetermined time period. Optimum stress levels and the number of units at every stress level are obtained by numerically minimizing the variance of the hazard rate estimate. Numerical example and sensitivity analysis are performed to show the application and robustness of the model to parameter deviations. The results show that the proposed procedure is robust to parameter changes and can be used for ALT planning of products under random usage.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44684105","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: 10.22094/JOIE.2020.561766.1545
A. Yemane
This study deals with line balancing of BOB T-shirt model with the help of control limit analysis and discrete event simulation of the assembly lines. In this study control limit analysis is used to measure the performance of the assembly line and used to show the bottleneck operations of the assembly line and line balancing technique improves the productivity of the sewing line of the model. BOB T-shirt model has 16 main operations and each operation’s time is analyzed as standard minute value (SMV). The main bottleneck operations are analyzed using the control limit analysis and simulation modeling. Based on the SMV of each operation, those operations which are out of lower control limit and upper control limit is called us bottlenecks of the sewing lines of the garment section and the 1st bottleneck operation is tread trimming operation. When we apply control limit analysis and discrete event simulation technique for the line balancing; the daily output has been increased from 1032 pieces to 1289 pieces. And labor productivity and machine productivity are increased from 46.9 and 54.32 to 58.59 and 71.61 respectively. And then finally, the profit that the line generated also increased from 22704 to 28358birr.
{"title":"Productivity Improvement of BOB T-shirt through Line Balancing Using Control Limit analysis and discrete event simulation (Case study: - MAA Garment and Textile Factory)","authors":"A. Yemane","doi":"10.22094/JOIE.2020.561766.1545","DOIUrl":"https://doi.org/10.22094/JOIE.2020.561766.1545","url":null,"abstract":"This study deals with line balancing of BOB T-shirt model with the help of control limit analysis and discrete event simulation of the assembly lines. In this study control limit analysis is used to measure the performance of the assembly line and used to show the bottleneck operations of the assembly line and line balancing technique improves the productivity of the sewing line of the model. BOB T-shirt model has 16 main operations and each operation’s time is analyzed as standard minute value (SMV). The main bottleneck operations are analyzed using the control limit analysis and simulation modeling. Based on the SMV of each operation, those operations which are out of lower control limit and upper control limit is called us bottlenecks of the sewing lines of the garment section and the 1st bottleneck operation is tread trimming operation. When we apply control limit analysis and discrete event simulation technique for the line balancing; the daily output has been increased from 1032 pieces to 1289 pieces. And labor productivity and machine productivity are increased from 46.9 and 54.32 to 58.59 and 71.61 respectively. And then finally, the profit that the line generated also increased from 22704 to 28358birr.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"225-238"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43556232","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: 10.22094/JOIE.2020.1877455.1685
Akbar Javadian Kootanaee, Abbas Ali Poor Aghajan, M. H. Shirvani
Financial statement fraud has increasingly become a serious problem for business, government, and investors. In fact, this threatens the reliability of capital markets, corporate heads, and even the audit profession. Auditors in particular face their apparent inability to detect large-scale fraud, and there are various ways to identify this problem. In order to identify this problem, the majority of the proposed methods are based on existing algorithms and have only attempted to identify human or simple data mining methods that have high overhead and are also costly. The data mining methods presented so far have had high computational overhead or low accuracy. The purpose of this study is to present a model in which an improved ID3 decision tree with a support vector machine is used as a hybrid approach and also to improve the performance and accuracy, genetic algorithm and multilayer perceptron neural networks are applied. More efficient feature selection has been used to reduce computational overhead. The tree proposed in the proposed method has the lowest depth possible and therefore has high velocity and low computational overhead. For this purpose, the financial statements of 151 listed companies in Tehran Stock Exchange during 2014-2015 were surveyed and 125 financial ratios were extracted using ANOVA test, 23 fraud related ratios were selected as model input data. The proposed model has a high accuracy of about 80% of prediction accuracy compared to similar models.
{"title":"A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements","authors":"Akbar Javadian Kootanaee, Abbas Ali Poor Aghajan, M. H. Shirvani","doi":"10.22094/JOIE.2020.1877455.1685","DOIUrl":"https://doi.org/10.22094/JOIE.2020.1877455.1685","url":null,"abstract":"Financial statement fraud has increasingly become a serious problem for business, government, and investors. In fact, this threatens the reliability of capital markets, corporate heads, and even the audit profession. Auditors in particular face their apparent inability to detect large-scale fraud, and there are various ways to identify this problem. In order to identify this problem, the majority of the proposed methods are based on existing algorithms and have only attempted to identify human or simple data mining methods that have high overhead and are also costly. The data mining methods presented so far have had high computational overhead or low accuracy. The purpose of this study is to present a model in which an improved ID3 decision tree with a support vector machine is used as a hybrid approach and also to improve the performance and accuracy, genetic algorithm and multilayer perceptron neural networks are applied. More efficient feature selection has been used to reduce computational overhead. The tree proposed in the proposed method has the lowest depth possible and therefore has high velocity and low computational overhead. For this purpose, the financial statements of 151 listed companies in Tehran Stock Exchange during 2014-2015 were surveyed and 125 financial ratios were extracted using ANOVA test, 23 fraud related ratios were selected as model input data. The proposed model has a high accuracy of about 80% of prediction accuracy compared to similar models.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"17 1","pages":"183-201"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41289309","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: 10.22094/JOIE.2020.575354.1585
H. Ahmadi, H. Valipour, Golamreza Jamali
The purpose of this research studies the impact of business intelligence on the financial reporting quality of listed companies in the Tehran Stock Exchange using structural equation modeling. The instruments of this research were the business Intelligence Questionnaire (Provich, 2012) and the financial statements of listed companies in The Tehran Stock Exchange to study of the financial reporting quality. For this purpose, the data of 182 listed companies in the Tehran Stock Exchange in 2018 was collected and processed. To analyze the data, Partial Least Squares Method and PLS-3 software were used. The findings of the research showed that each of the components of business intelligence including data integrity, analytical capabilities, information content quality, information access quality, use of information in business process, and Analytical decision - making culture has a positive and significant effect on the financial reporting quality
{"title":"Relationship between Business Intelligence Components and Financial Reporting Quality in Firms","authors":"H. Ahmadi, H. Valipour, Golamreza Jamali","doi":"10.22094/JOIE.2020.575354.1585","DOIUrl":"https://doi.org/10.22094/JOIE.2020.575354.1585","url":null,"abstract":"The purpose of this research studies the impact of business intelligence on the financial reporting quality of listed companies in the Tehran Stock Exchange using structural equation modeling. The instruments of this research were the business Intelligence Questionnaire (Provich, 2012) and the financial statements of listed companies in The Tehran Stock Exchange to study of the financial reporting quality. For this purpose, the data of 182 listed companies in the Tehran Stock Exchange in 2018 was collected and processed. To analyze the data, Partial Least Squares Method and PLS-3 software were used. The findings of the research showed that each of the components of business intelligence including data integrity, analytical capabilities, information content quality, information access quality, use of information in business process, and Analytical decision - making culture has a positive and significant effect on the financial reporting quality","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"171-182"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48001910","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: 10.22094/JOIE.2021.566423.1558
H. Nasseri, S. Bavandi
In this paper, we first introduce a new category of mathematical programming where the problem coefficients are interval random variables. These problems include two different kinds of ambiguity in the problem coefficients which are being interval and being random. We use Fractile method to solve these problems. In this method, using the existing method, we change the interval problem coefficients to random mode and then we solve the random problem using Fractile method. Also, a numerical example is presented to show the effectiveness of this model. Finally, we emphasize that this approach can be useful for the model with multi-objective as a generalized model in the future study.
{"title":"A Fractile Model for Stochastic Interval Linear Programming Problems","authors":"H. Nasseri, S. Bavandi","doi":"10.22094/JOIE.2021.566423.1558","DOIUrl":"https://doi.org/10.22094/JOIE.2021.566423.1558","url":null,"abstract":"In this paper, we first introduce a new category of mathematical programming where the problem coefficients are interval random variables. These problems include two different kinds of ambiguity in the problem coefficients which are being interval and being random. We use Fractile method to solve these problems. In this method, using the existing method, we change the interval problem coefficients to random mode and then we solve the random problem using Fractile method. Also, a numerical example is presented to show the effectiveness of this model. Finally, we emphasize that this approach can be useful for the model with multi-objective as a generalized model in the future study.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"321-331"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42621523","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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