A generic hierarchical clustering approach for detecting bottlenecks in manufacturing

Journal of Manufacturing Systems - Tập 55 - Trang 143-158 - 2020
Mukund Subramaniyan1, Anders Skoogh1, Azam Sheikh Muhammad2, Jon Bokrantz1, Björn Johansson1, Christoph Roser3
1Department of Industrial and Materials Science, Chalmers University of Technology, Gothenburg 41296, Sweden
2Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 41296, Sweden
3Department of Management Science and Engineering, Karlsruhe University of Applied Sciences, Karlsruhe 76133, Germany

Tài liệu tham khảo

Miller, 2018, AI: Augmentation, more so than automation, Asian Manag Insights, 5, 1 Wilson, 2018, Collaborative intelligence: humans and AI are joining forces humans, Harv Bus Rev, 96, 115 Aghabozorgi, 2014, Stock market co-movement assessment using a three-phase clustering method, Expert Syst Appl, 41, 1301, 10.1016/j.eswa.2013.08.028 De Prado, 2016, Building diversified portfolios that outperform out-of-sample, J Portf Manag Emerson, 2019, Trends and applications of machine learning in quantitative finance, 8th Int. Conf. Econ. Financ. Res. (ICEFR 2019) Tao, 2018, Data-driven smart manufactuirng, J Manuf Syst, 48, 157, 10.1016/j.jmsy.2018.01.006 Xia, 2018, Recent advances in prognostics and health management for advanced manufacturing paradigms, Reliab Eng Syst Saf, 10.1016/j.ress.2018.06.021 Carvalho, 2019, A systematic literature review of machine learning methods applied to predictive maintenance, Comput Ind Eng, 10.1016/j.cie.2019.106024 Wuest, 2016, Machine learning in manufacturing : advantages, challenges, and applications, Prod Manuf Res, 4, 1 Sharp, 2018, A survey of the advancing use and development of machine learning in smart manufacturing, J Manuf Syst, 48, 170, 10.1016/j.jmsy.2018.02.004 Bokrantz, 2019, Smart maintenance: a research agenda for industrial maintenance management, Int J Prod Econ Cimini, 2020, A human-in-the-loop manufacturing control architecture for the next generation of production systems, J Manuf Syst, 10.1016/j.jmsy.2020.01.002 Li, 2009, Short-term decision support system for maintenance task prioritization, Int J Prod Econ, 121, 195, 10.1016/j.ijpe.2009.05.006 Wu, 2016, Variability and the fundamental properties of production lines, Comput Ind Eng, 99, 364, 10.1016/j.cie.2016.04.014 Alavian, 2019, Smart production systems : automating decision- making in manufacturing environment, Int J Prod Res, 1 Roser, 2001, A practical bottleneck detection method, 949 Goldrat, 1990 Roser, 2002, Shifting bottleneck detection, vol. 2 Li, 2009, Data driven bottleneck detection of manufacturing systems, Int J Prod Res, 47, 5019, 10.1080/00207540701881860 Betterton, 2012, Detecting bottlenecks in serial production lines – a focus on interdeparture time variance, Int J Prod Res, 50, 4158, 10.1080/00207543.2011.596847 Tang, 2019, A new method of bottleneck analysis for manufacturing systems, Manuf Lett, 10.1016/j.mfglet.2019.01.003 Subramaniyan, 2018, Data-driven algorithm for throughput bottleneck analysis of production systems, Prod Manuf Res, 6, 225 Li, 2018, A systematic-theoretic analysis of data-driven throughput bottleneck detection of production systems, J Manuf Syst, 47, 43, 10.1016/j.jmsy.2018.03.001 Pehrsson, 2016, Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis, J Manuf Syst, 39, 24, 10.1016/j.jmsy.2016.02.001 Yu, 2016, Data-driven bottleneck detection in manufacturing systems: a statistical approach, Int J Prod Res, 54, 6317, 10.1080/00207543.2015.1126681 Amrhein, 2019, Inferential statistics as descriptive statistics: there is No replication crisis if we don’t expect replication, Am Stat, 73, 262, 10.1080/00031305.2018.1543137 Roser, 2003, Confidence interval from a single simulation using delta method, JSME Int J Ser C Mech Syst Mach Elem Manuf, 46, 67, 10.1299/jsmec.46.67 Subramaniyan, 2018, A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines, Comput Ind Eng, 125, 533, 10.1016/j.cie.2018.04.024 Li, 2011, Throughput bottleneck prediction of manufacturing systems using time series analysis, J Manuf Sci Eng, 133, 1, 10.1115/1.4003786 Hastie, 2001 Keogh, 2005, Clustering of time-series subsequences is meaningless : implications for previous and future research, Knowl Inf Syst, 8, 154, 10.1007/s10115-004-0172-7 Balcan, 2014, Robust hierarchical clustering, J Mach Learn Res, 15, 4011 Davidson, 2005, Agglomerative hierarchical clustering with constraints: theoretical and empirical results, 59 Jain, 1999, Data clustering: a review, ACM Comput Surv, 31, 264, 10.1145/331499.331504 Serr, 2014, An empirical evaluation of similarity measures for time series classification, Knowledge-Based Syst, 67, 305, 10.1016/j.knosys.2014.04.035 Mueen, 2016, Extracting optimal performance from dynamic time warping, 2129 Hirano, 2005, Empirical comparison of clustering methods for Long time-series databases, 275 Zambelli, 2017, A data-driven approach to estimating the number of clusters in hierarchical clustering, F1000Research, 5, 1 Thorndike, 1953, Who belongs in the family?, Psychometrika, 18, 267, 10.1007/BF02289263 Baheti, 2014, Trend analysis of time series data using data mining techniques, IEEE Int. Congr. Big Data, IEEE, 430 Baheti, 2014, Finding representative time sequence for trend analysis, CSI Trans ICT, 2, 181, 10.1007/s40012-014-0056-2 Kumar, 2016, A novel framework to analyze road accident time series data, J Big Data, 3, 1, 10.1186/s40537-016-0044-5 Wirth, 2000, CRISP-DM: towards a standard process model for data mining, Proc. Fourth Int. Conf. Pract. Appl. Knowl. Discov. Data Min., 29 Huber, 2019, DMME: data mining methodology for engineering applications - a holistic extension to the CRISP-DM model, Procedia CIRP, 79, 403, 10.1016/j.procir.2019.02.106 Lei, 2017, Identification approach for bottleneck clusters in a job shop based on theory of constraints and sensitivity analysis, Proc Inst Mech Eng Part B J Eng Manuf, 231, 1091, 10.1177/0954405415583884 Sakoe, 1978, Dynamic programming algorithm optimization for spoken word recognition, IEEE Trans Acoust, 26, 43, 10.1109/TASSP.1978.1163055