대한산업공학회지 (Journal of Korean Institute of Industrial Engineers)

  • 제39권6호
  • /
  • Pages.577-586
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  • 2013
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  • 1225-0988(pISSN)
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  • 2234-6457(eISSN)
대한산업공학회 (Korean Institute of Industrial Engineers)
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조선 산업에서 프로세스 마이닝을 이용한 블록 이동 프로세스 분석 프레임워크 개발

Analysis Framework using Process Mining for Block Movement Process in Shipyards

  • 이동하 (대우조선해양(주) 중앙연구소) ;
  • 배혜림 (부산대학교 산업공학과)
  • Lee, Dongha (Central R&D Institute, Daewoo Shipbuilding and Marine Engineering Co., Ltd.) ;
  • Bae, Hyerim (Department of Industrial Engineering, Pusan National University)
  • 투고 : 2013.02.04
  • 심사 : 2013.05.14
  • 발행 : 2013.12.15
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초록

In a shipyard, it is hard to predict block movement due to the uncertainty caused during the long period of shipbuilding operations. For this reason, block movement is rarely scheduled, while main operations such as assembly, outfitting and painting are scheduled properly. Nonetheless, the high operating costs of block movement compel task managers to attempt its management. To resolve this dilemma, this paper proposes a new block movement analysis framework consisting of the following operations: understanding the entire process, log clustering to obtain manageable processes, discovering the process model and detecting exceptional processes. The proposed framework applies fuzzy mining and trace clustering among the process mining technologies to find main process and define process models easily. We also propose additional methodologies including adjustment of the semantic expression level for process instances to obtain an interpretable process model, definition of each cluster's process model, detection of exceptional processes, and others. The effectiveness of the proposed framework was verified in a case study using real-world event logs generated from the Block Process Monitoring System (BPMS).

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참고문헌

  1. van der Aalst, W. M. P. and Basten, T. (2002), Inheritance of workflows : An approach to tackling problems related to change, Theoretical Computer Science, 270(1), 125-203. https://doi.org/10.1016/S0304-3975(00)00321-2
  2. van der Aalst, W. M. P. (2005), Business alignment: using process mining as a tool for Delta analysis and conformance testing, Requirement Engineering, 10(3), 198-211. https://doi.org/10.1007/s00766-005-0001-x
  3. van der Aalst, W. M. P., Reijers, H. A., and Song, M. (2005), Discovering social networks from event logs, Computer Supported Cooperative work, 14(6), 549-593. https://doi.org/10.1007/s10606-005-9005-9
  4. van der Aalst, W. M. P., van Dongen B. F., Gunther, C. W., Rozinat, A., Verbeek, E., and Weijters, T. (2009), ProM : the process mining toolkit, BPM Demonstration, CEUR Workshop Proceedings, 489, 1-4.
  5. Berry, M. J. A. and Linoff, G. (1997), Data Mining Techniques, John Wiley and Sons, Inc.
  6. Gunther, C. W. and van der Aalst, W. M. P. (2007), Fuzzy mining-Adaptive process simplification based on multi-perspective metrics, BPM Workshops, Lecture Notes in Computer Science, 4714, 328-343.
  7. Jansen-Vullers, M. H., van der Aalst, W. M. P., and Rosemann, M. (2006), Mining configurable enterprise information systems, Data and Knowledge Engineering, 56(3), 195-244. https://doi.org/10.1016/j.datak.2005.03.007
  8. Lee, S., Kim, J., and Moon, I. (2011), Deployment planning of blocks from storage yards using a tabu search algorithm, Journal of the korean institute of industrial engineers, 37(3), 198-208. https://doi.org/10.7232/JKIIE.2011.37.3.198
  9. Lee, S., Kim, B., Huh, M., Cho, S., Park, S., and Lee, D. (2013), Mining transportation logs for understanding the after-assembly block manufacturing process in the shipbuilding industry, Expert Systems with Applications, 40(1), 83-95. https://doi.org/10.1016/j.eswa.2012.07.033
  10. de Medeiros, A. K. Alves, Weijters, A. J. M. M., and van der Aalst, W. M. P. (2007), Genetic Process Mining : An Experimental Evaluation, Data Mining and Knowledge Discovery, 14(2), 245-304. https://doi.org/10.1007/s10618-006-0061-7
  11. Park, C. and Seo, J. (2012), A GRASP apporach to transporter scheduling and routing at a shipyard, Computer and Industrial Engineering, 63, 390-399. https://doi.org/10.1016/j.cie.2012.04.010
  12. Rozinat, A. and van der Aalst, W. M. P. (2006), Decision Mining in ProM, Proc. 4th Int. Conf. on Business Process Management, 420-425.
  13. Song, M., Gunther C. W., and van der Aalst, W. M. P. (2009), Trace clustering in process mining, BPM 2008 Workshops, Lecture Notes in Business Information Processing, 17, 109-120.
  14. Veiga, G. M. and Ferreira, D. R. (2010), Understanding Spaghetti Models with Sequence Clustering for ProM, BPM 2010 Workshops, Lecture Notes in Business Information Processing, 43, 92-103.
  15. de Weerdt, J., de Backer, M., Vanthienen, J., and Baesens, B. (2012), A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs, Information Systems, 37, 654-676. https://doi.org/10.1016/j.is.2012.02.004
  16. Yahya, B. N., Park, J. H., Bae, H. and Mo, J. K. (2011), Similarity measurement using ontology in vessel clearance process, Journal of the korean institute of industrial engineers, 37(2), 153-162. https://doi.org/10.7232/JKIIE.2011.37.2.153
  17. Yu, Y.-W., Kim, S., and Bae, H. (2012), Business process modeling using process structure constraints and social relations, IE Interfaces, 25(3), 300-308. https://doi.org/10.7232/IEIF.2012.25.3.300

피인용 문헌

  1. Quay Wall Scheduling of Ships Using Assignment Method and Tabu Search Algorithm vol.41, pp.1, 2015, https://doi.org/10.7232/JKIIE.2015.41.1.001