Pub Date : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978897
Amelia Castillo-Revelo, Liseth Mañuico-Salas, Fernando Maradiegue-Tuesta, J. Alvarez-Merino
This study sets out to increase the overall efficiency of equipment in the automotive battery assembly line by reducing the number of non-compliant products. To this end, the root cause analysis was performed, and it determined that disconformities are caused by the lack of standardization of processes, paucity of working methods, and inadequate maintenance plans. The proposal of this study involves the use of total productive maintenance (TPM) tools as the modal analysis of failures and effects, self-maintenance, and planned maintenance based on reliability-centric maintenance (RCM). The joint use of these tools leads to an increase in the quality of automotive batteries. The application of TPM and RCM is subject to internal and external factors affecting a company. The proposed methodology can be applied to small and medium-sized manufacturing industries with different production lines. As a result of its application, the number of non-compliant batteries was reduced by 30%, machine efficiency increased by 3.00%, and the mean time between failures was reduced by 19.96%.
{"title":"Application of TPM Tools in an Automotive Battery Assembly Line","authors":"Amelia Castillo-Revelo, Liseth Mañuico-Salas, Fernando Maradiegue-Tuesta, J. Alvarez-Merino","doi":"10.1109/IEEM44572.2019.8978897","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978897","url":null,"abstract":"This study sets out to increase the overall efficiency of equipment in the automotive battery assembly line by reducing the number of non-compliant products. To this end, the root cause analysis was performed, and it determined that disconformities are caused by the lack of standardization of processes, paucity of working methods, and inadequate maintenance plans. The proposal of this study involves the use of total productive maintenance (TPM) tools as the modal analysis of failures and effects, self-maintenance, and planned maintenance based on reliability-centric maintenance (RCM). The joint use of these tools leads to an increase in the quality of automotive batteries. The application of TPM and RCM is subject to internal and external factors affecting a company. The proposed methodology can be applied to small and medium-sized manufacturing industries with different production lines. As a result of its application, the number of non-compliant batteries was reduced by 30%, machine efficiency increased by 3.00%, and the mean time between failures was reduced by 19.96%.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120810432","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 : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978616
Haibing Liu, Lei Yang, Qingrui Xu
The purpose of this paper is to analyze how the strategy of latecomer firms leads innovation and promotes the accumulation and improvement of capability in its development with an analytical framework of “strategy-innovation paradigm-capability” and tries to sum up the internal evolution logic of strategy. Based on this, it makes a longitudinal case study of Haier Group from 1984 to 2018. Through the research, it constructs the L.S.I.C model, which reveals the key role of the strategy in the innovation and capability in the catch-up process of latecomer firms: strategy leads innovation. Innovation is the basis of forming capability. Different innovation paradigm forms different enterprise capability. At the same time, the mechanism of strategic leading innovation is supported by leadership mechanism, learning mechanism and coordination mechanism. The contributions of this paper are as follows: it enriches the theoretical basis of strategic evolution, makes a pioneering analysis of the relationship between strategy, innovation and capability, and probes into the national technology policy.
{"title":"Research on Strategic Leading Mechanism of Latecomer Firms","authors":"Haibing Liu, Lei Yang, Qingrui Xu","doi":"10.1109/IEEM44572.2019.8978616","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978616","url":null,"abstract":"The purpose of this paper is to analyze how the strategy of latecomer firms leads innovation and promotes the accumulation and improvement of capability in its development with an analytical framework of “strategy-innovation paradigm-capability” and tries to sum up the internal evolution logic of strategy. Based on this, it makes a longitudinal case study of Haier Group from 1984 to 2018. Through the research, it constructs the L.S.I.C model, which reveals the key role of the strategy in the innovation and capability in the catch-up process of latecomer firms: strategy leads innovation. Innovation is the basis of forming capability. Different innovation paradigm forms different enterprise capability. At the same time, the mechanism of strategic leading innovation is supported by leadership mechanism, learning mechanism and coordination mechanism. The contributions of this paper are as follows: it enriches the theoretical basis of strategic evolution, makes a pioneering analysis of the relationship between strategy, innovation and capability, and probes into the national technology policy.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125817918","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 : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978720
Weidong Lin, M. Low, Y. T. Chong, C. L. Teo
Smart Industry Readiness Index (SIRI) is one of the Industry 4.0 maturity models that help industrial companies to identify the opportunities moving towards Industry 4.0. SIRI is strongly aligned with other global manufacturing initiatives, and has the potential to be one of the global standards for the future of manufacturing Industry 4.0. However there is little literature found on applications of SIRI due to it is a relatively new model released in 2017. This paper discussed the methodology and processes of applying SIRI to help companies to identify the gaps and opportunities moving towards industry 4.0. For illustration purpose, four pillars, i.e. Operations, Automation, Connectivity and Intelligence, are selected to illustrate the methodology and processes for assessing the respective dimensions. The designed rubrics with questionnaire to facilitate the processes and methodology are demonstrated with examples.
{"title":"Application of SIRI for Industry 4.0 Maturity Assessment and Analysis","authors":"Weidong Lin, M. Low, Y. T. Chong, C. L. Teo","doi":"10.1109/IEEM44572.2019.8978720","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978720","url":null,"abstract":"Smart Industry Readiness Index (SIRI) is one of the Industry 4.0 maturity models that help industrial companies to identify the opportunities moving towards Industry 4.0. SIRI is strongly aligned with other global manufacturing initiatives, and has the potential to be one of the global standards for the future of manufacturing Industry 4.0. However there is little literature found on applications of SIRI due to it is a relatively new model released in 2017. This paper discussed the methodology and processes of applying SIRI to help companies to identify the gaps and opportunities moving towards industry 4.0. For illustration purpose, four pillars, i.e. Operations, Automation, Connectivity and Intelligence, are selected to illustrate the methodology and processes for assessing the respective dimensions. The designed rubrics with questionnaire to facilitate the processes and methodology are demonstrated with examples.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127027204","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 : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978591
Zhiqiang Chen, Xiaoyang Zhu
In industry, the working conditions of systems are complicated. It may contain some adverse/favorable factors that can damage/improve components' reliabilities compared to that in the designed working condition. The same product/system may also work in different sites whose conditions exist significant differences to each other. Then the reliability of the system is not only determined by system structure, components' designed reliabilities, etc. It can be also influenced by its working condition. In previous researches in importance measures, the environmental influence on system reliability has been paid little attention. In this paper, two working-condition importance measures for multi-component systems are introduced.
{"title":"Working-Condition Importance Measures for Multi-Component Systems","authors":"Zhiqiang Chen, Xiaoyang Zhu","doi":"10.1109/IEEM44572.2019.8978591","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978591","url":null,"abstract":"In industry, the working conditions of systems are complicated. It may contain some adverse/favorable factors that can damage/improve components' reliabilities compared to that in the designed working condition. The same product/system may also work in different sites whose conditions exist significant differences to each other. Then the reliability of the system is not only determined by system structure, components' designed reliabilities, etc. It can be also influenced by its working condition. In previous researches in importance measures, the environmental influence on system reliability has been paid little attention. In this paper, two working-condition importance measures for multi-component systems are introduced.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127246999","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 : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978890
V. Isoherranen, R. Ratnayake
To succeed in product development starting from new product concepts is a challenging task. The lean startup methodology (LSM) enables on agile testing and learning cycle to validate hypotheses at the product idea/concept generation level when traditional stage-gate product development process is too resource consuming and is lacking speed to market. Lean startups are significantly challenged by subsequent financial and related other resource restrictions and subsequently it leads to minimal viable products (MVPs). An MVP provides minimum sufficient product features to satisfy early consumers in which provide feedback and revenue for future product development i.e. product market fit. When there are many previously developed MVPs in the market. It is not clear how to pull them to be the products with minimum viable product features (MVPFs) as the consumer demands rise over the time. This manuscript first briefly discusses current challenges in lean startups and concept of lean/pull product development. Then, it presents a framework to demonstrate pull product development process. Finally, with the support of a case study, it demonstrates how to use multi-criteria analysis methodology to recognize MVPFs. An illustrative analysis and results are presented to demonstrate the use of the suggested approach for recognizing MVPFs.
{"title":"Use of Pull Product Development for Enhancing Lean Startups","authors":"V. Isoherranen, R. Ratnayake","doi":"10.1109/IEEM44572.2019.8978890","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978890","url":null,"abstract":"To succeed in product development starting from new product concepts is a challenging task. The lean startup methodology (LSM) enables on agile testing and learning cycle to validate hypotheses at the product idea/concept generation level when traditional stage-gate product development process is too resource consuming and is lacking speed to market. Lean startups are significantly challenged by subsequent financial and related other resource restrictions and subsequently it leads to minimal viable products (MVPs). An MVP provides minimum sufficient product features to satisfy early consumers in which provide feedback and revenue for future product development i.e. product market fit. When there are many previously developed MVPs in the market. It is not clear how to pull them to be the products with minimum viable product features (MVPFs) as the consumer demands rise over the time. This manuscript first briefly discusses current challenges in lean startups and concept of lean/pull product development. Then, it presents a framework to demonstrate pull product development process. Finally, with the support of a case study, it demonstrates how to use multi-criteria analysis methodology to recognize MVPFs. An illustrative analysis and results are presented to demonstrate the use of the suggested approach for recognizing MVPFs.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127485142","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 : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978907
B. Mark, Luca Gualtieri, E. Rauch, Rafael A. Rojas, Dollaya Buakum, D. Matt
The fourth industrial revolution aims to digitize the production chain. The goal is to optimize production processes while minimizing error rates and contemporaneously increasing the productivity. Not only the production lines are considered, but also the employee himself, for whom the working environment should be designed to be more comfortable, ergonomic and adapted to the individual needs. To get closer to this goal, assistance systems can be used, which accompany the worker through appropriate support during the daily work routine. The following paper discusses various assistance systems both identified in scientific literature and on the market, as well as categorizes diverse user groups in the present production 4.0. As a result, the paper provides a proposal for the matching of assistance systems with the identified user groups.
{"title":"Analysis of User Groups for Assistance Systems in Production 4.0","authors":"B. Mark, Luca Gualtieri, E. Rauch, Rafael A. Rojas, Dollaya Buakum, D. Matt","doi":"10.1109/IEEM44572.2019.8978907","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978907","url":null,"abstract":"The fourth industrial revolution aims to digitize the production chain. The goal is to optimize production processes while minimizing error rates and contemporaneously increasing the productivity. Not only the production lines are considered, but also the employee himself, for whom the working environment should be designed to be more comfortable, ergonomic and adapted to the individual needs. To get closer to this goal, assistance systems can be used, which accompany the worker through appropriate support during the daily work routine. The following paper discusses various assistance systems both identified in scientific literature and on the market, as well as categorizes diverse user groups in the present production 4.0. As a result, the paper provides a proposal for the matching of assistance systems with the identified user groups.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128934344","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 : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978660
Daria Biskupska, R. Ratnayake
Engineering contractors (ECs) need to focus on minimizing performance waste, in order to maintain their competitiveness, as the global petroleum industry experiences lack of investments in new projects, due to low oil and gas prices. This requires ECs to put great emphasis on: improving timeliness of current equipment deliveries; cost-effective and timely implementation of documentation; increased efficiency of engineering tools; and development of new products for future orders. Lean Daily Management (LDM) elements, such as visual control boards, daily accountability, and leader standard work, etc., with identified key performance indicators, support leaders to continuously improve processes and eliminate waste. This manuscript demonstrates the basics of LDM, then presents the findings regarding design project delivery and performance in a case study EC company. It also demonstrates the potential of using LDM to solve the existing challenges pertaining to design projects' delivery in the case study company. Finally, it presents a methodology and guidelines for implementing LDM to minimize backlog of design projects and waste of design projects' delivery process in the case study EC company, to avoid over-budget circumstances.
{"title":"On the Need for Effective Lean Daily Management in Engineering Design Projects: Development of a Framework","authors":"Daria Biskupska, R. Ratnayake","doi":"10.1109/IEEM44572.2019.8978660","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978660","url":null,"abstract":"Engineering contractors (ECs) need to focus on minimizing performance waste, in order to maintain their competitiveness, as the global petroleum industry experiences lack of investments in new projects, due to low oil and gas prices. This requires ECs to put great emphasis on: improving timeliness of current equipment deliveries; cost-effective and timely implementation of documentation; increased efficiency of engineering tools; and development of new products for future orders. Lean Daily Management (LDM) elements, such as visual control boards, daily accountability, and leader standard work, etc., with identified key performance indicators, support leaders to continuously improve processes and eliminate waste. This manuscript demonstrates the basics of LDM, then presents the findings regarding design project delivery and performance in a case study EC company. It also demonstrates the potential of using LDM to solve the existing challenges pertaining to design projects' delivery in the case study company. Finally, it presents a methodology and guidelines for implementing LDM to minimize backlog of design projects and waste of design projects' delivery process in the case study EC company, to avoid over-budget circumstances.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133013920","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 : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978895
Zhihong Xu, Yufeng Sun, Guangyan Zhao
Repairable system is generally dealt by Markov modeling method. But the state space will exponentially explode with increasing number of components. Thus, a newly emerging concept, survival signature, is utilized in this paper to help reduce state space in Markov modeling. On the basis of that, mathematical expressions of availability for repairable system consisting of homogenous components as well as system of heterogeneous components are derived. The final cases demonstrate the application of this method and the method is proved to be efficiency.
{"title":"Using Survival Signature to Analyze Availability of Repairable System","authors":"Zhihong Xu, Yufeng Sun, Guangyan Zhao","doi":"10.1109/IEEM44572.2019.8978895","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978895","url":null,"abstract":"Repairable system is generally dealt by Markov modeling method. But the state space will exponentially explode with increasing number of components. Thus, a newly emerging concept, survival signature, is utilized in this paper to help reduce state space in Markov modeling. On the basis of that, mathematical expressions of availability for repairable system consisting of homogenous components as well as system of heterogeneous components are derived. The final cases demonstrate the application of this method and the method is proved to be efficiency.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132038283","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 : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978912
Yiping Gao, Liang Gao, Xinyu Li
As one of the breakthroughs in modern manufacturing, deep learning (DL) performs large-scale network architectures and achieves some outstanding performances in vision-based defect recognition. However, most of these large-scale networks require a large sample for training, and a small sample might cause the networks overfitting and collapse. Since the defect often occurs with a low probability, it is costly to collect large-scale samples. To overcome this problem, a hierarchical feature fusion-based method is introduced for defect recognition with a small sample. The proposed method divides a pretrained VGG16 network into different blocks, and learns the hierarchical features from the low- and high- level blocks. The results are better than the other methods. This result manifests the proposed method suits problem, and the defect recognition could be deployed earlier with the proposed method.
{"title":"A Hierarchical Feature Fusion-based Method for Defect Recognition with a Small Sample","authors":"Yiping Gao, Liang Gao, Xinyu Li","doi":"10.1109/IEEM44572.2019.8978912","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978912","url":null,"abstract":"As one of the breakthroughs in modern manufacturing, deep learning (DL) performs large-scale network architectures and achieves some outstanding performances in vision-based defect recognition. However, most of these large-scale networks require a large sample for training, and a small sample might cause the networks overfitting and collapse. Since the defect often occurs with a low probability, it is costly to collect large-scale samples. To overcome this problem, a hierarchical feature fusion-based method is introduced for defect recognition with a small sample. The proposed method divides a pretrained VGG16 network into different blocks, and learns the hierarchical features from the low- and high- level blocks. The results are better than the other methods. This result manifests the proposed method suits problem, and the defect recognition could be deployed earlier with the proposed method.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130832488","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 : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978767
Haiyan Xu, Vasundhara Jayaraman, Xiuju FU, N. Othman, Wanbing Zhang, Xiaofeng Yin, Deqing Zhai, R. Goh
The large scale deployment of sensor, Global Positioning System (GPS) and other mobile devices generates large volumes of spatiotemporal data, which facilitates the understandings of objects' movement trajectories and activities. However, it is very challenging to store, transfer and load such a large volume of data into system memory for processing and analysis. In this study, we look into a study case that processes the large scale of Automatic Identification System (AIS) data in the maritime sector, and propose a computational framework to efficiently compress, transfer and acquire necessary information for further data analysis. The framework is composed of two parts: The first is a lossless compression algorithm that compresses the AIS data into binary form for efficient storage, speedy loading and easy transfer across networks and systems within the organization; the second is an aggregation algorithm which derives movement and activity information of vessels grouped by grid and/or time window from the compressed binary files, therefore improves data accessibility and reduces storage demand. The proposed framework has been applied to extract vessel movement information within Singapore port with high compression rate and fast access speed, and it can be extensively applied for other data processing applications.
{"title":"Efficient Compression and Preprocessing for Facilitating Large Scale Spatiotemporal Data Mining - A Case Study based on Automatic Identification System Data","authors":"Haiyan Xu, Vasundhara Jayaraman, Xiuju FU, N. Othman, Wanbing Zhang, Xiaofeng Yin, Deqing Zhai, R. Goh","doi":"10.1109/IEEM44572.2019.8978767","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978767","url":null,"abstract":"The large scale deployment of sensor, Global Positioning System (GPS) and other mobile devices generates large volumes of spatiotemporal data, which facilitates the understandings of objects' movement trajectories and activities. However, it is very challenging to store, transfer and load such a large volume of data into system memory for processing and analysis. In this study, we look into a study case that processes the large scale of Automatic Identification System (AIS) data in the maritime sector, and propose a computational framework to efficiently compress, transfer and acquire necessary information for further data analysis. The framework is composed of two parts: The first is a lossless compression algorithm that compresses the AIS data into binary form for efficient storage, speedy loading and easy transfer across networks and systems within the organization; the second is an aggregation algorithm which derives movement and activity information of vessels grouped by grid and/or time window from the compressed binary files, therefore improves data accessibility and reduces storage demand. The proposed framework has been applied to extract vessel movement information within Singapore port with high compression rate and fast access speed, and it can be extensively applied for other data processing applications.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130453607","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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