*Result*: A model to predict bottlenecks over time in a remanufacturing system under uncertainty.
Title:
A model to predict bottlenecks over time in a remanufacturing system under uncertainty.
Authors:
Source:
Environmental science and pollution research international [Environ Sci Pollut Res Int] 2021 Jul 09. Date of Electronic Publication: 2021 Jul 09.
Publication Model:
Ahead of Print
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Country of Publication: Germany NLM ID: 9441769 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1614-7499 (Electronic) Linking ISSN: 09441344 NLM ISO Abbreviation: Environ Sci Pollut Res Int Subsets: MEDLINE
Imprint Name(s):
Publication: <2013->: Berlin : Springer
Original Publication: Landsberg, Germany : Ecomed
Original Publication: Landsberg, Germany : Ecomed
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Li J, Jiao J, Tang Y (2019) An evolutionary analysis on the effect of government policies on electric vehicle diffusion in complex network. Energy Policy 129:1–12. https://doi.org/10.1016/j.enpol.2019.01.070. (PMID: 10.1016/j.enpol.2019.01.070)
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Subramaniyan M, Skoogh A, Salomonsson H, Bangalore P, Bokrantz J (2018) A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines. Comput Ind Eng 125:533–544. https://doi.org/10.1016/j.cie.2018.04.024. (PMID: 10.1016/j.cie.2018.04.024)
Subramaniyan M, Skoogh A, Sheikh Muhammad A, Bokrantz J, Turanoğlu Bekar E (2019) A prognostic algorithm to prescribe improvement measures on throughput bottlenecks. J Manuf Syst 53:271–281. https://doi.org/10.1016/j.jmsy.2019.07.004. (PMID: 10.1016/j.jmsy.2019.07.004)
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Zhengcai CAO, Jijie D, Min LIU, Yongji W (2012) Bottleneck prediction method based on improved adaptive network-based fuzzy inference system ( ANFIS ) in Semiconductor Manufacturing System *. Chin J Chem Eng 20:1081–1088. https://doi.org/10.1016/S1004-9541(12)60590-4. (PMID: 10.1016/S1004-9541(12)60590-4)
Zhong W, Dai T, Wang G, Li Q, Li D, Liang L, Sun X, Hao X, Jiang M (2018) Structure of international iron flow: based on substance flow analysis and complex network. Resour Conserv Recycl 136:345–354. https://doi.org/10.1016/j.resconrec.2018.05.006. (PMID: 10.1016/j.resconrec.2018.05.006)
Zhuang Z, Huang Z, Chen L, Qin W (2019) A heuristic rule based on complex network for open shop scheduling problem with sequence-dependent setup times and delivery times. IEEE Access 7:140946–140956. https://doi.org/10.1109/ACCESS.2019.2944296. (PMID: 10.1109/ACCESS.2019.2944296)
Betterton CE, Silver SJ (2012) Predicting bottlenecks in serial production lines - a focus on interdeparture time variance. Int J Prod Res 50:4158–4174. https://doi.org/10.1080/00207543.2011.596847. (PMID: 10.1080/00207543.2011.596847)
Beyza J, Garcia-Paricio E, Yusta JM (2019) Applying complex network theory to the vulnerability assessment of interdependent energy infrastructures. Energies 12:1–16. https://doi.org/10.3390/en12030421. (PMID: 10.3390/en12030421)
Biller S, Li J, Marin SP et al (2010) Bottlenecks in Bernoulli serial lines with rework. IEEE Trans Autom Sci Eng 7:208–217. https://doi.org/10.1109/TASE.2009.2023463. (PMID: 10.1109/TASE.2009.2023463)
Calderón-Andrade R, Hernández-Gress ES, Montufar Benítez MA (2020) Throughput improvement through reengineering and simulation: a case study in a footwear-industry. Appl Sci 10. https://doi.org/10.3390/app10165590.
Chang Q, Ni J, Bandyopadhyay P, Biller S, Xiao G (2007) Supervisory factory control based on real-time production feedback. J Manuf Sci Eng Trans ASME 129:653–660. https://doi.org/10.1115/1.2673666. (PMID: 10.1115/1.2673666)
Chao PY, Chen TT (2001) Analysis of assembly through product configuration. Comput Ind 44:189–203. https://doi.org/10.1016/S0166-3615(00)00086-5.
Chowell G, Hyman JM, Eubank S, Castillo-Chavez C (2003) Scaling laws for the movement of people between locations in a large city. Phys Rev E 68: 1855–1862. https://doi.org/10.1103/PhysRevE.68.066102.
De Montis A, Barthélemy M, Chessa A, Vespignani A (2007) The structure of interurban traffic: a weighted network analysis. Environ Plan B Plan Des 34:905–924. https://doi.org/10.1068/b32128. (PMID: 10.1068/b32128)
Eisenberg E, Levanon EY (2003) Preferential attachment in the protein network evolution. Phys Rev Lett 91. https://doi.org/10.1103/PhysRevLett.91.138701.
Fang W, Guo Y, Liao W, Huang S, Yang N, Liu J (2020) A Parallel Gated Recurrent Units (P-GRUs) network for the shifting lateness bottleneck prediction in make-to-order production system. Comput Ind Eng 140:106246. https://doi.org/10.1016/j.cie.2019.106246. (PMID: 10.1016/j.cie.2019.106246)
Felfernig A, Friedrich G, Jannach D (2001) Conceptual modeling for configuration of mass-customizable products. Artif Intell Eng 15:165–176. https://doi.org/10.1016/S0954-1810(01)00016-4. (PMID: 10.1016/S0954-1810(01)00016-4)
Gao G, Rong T, Yue W (2018) Vulnerability assessment method for manufacturing system based on complex network. Jisuanji Jicheng Zhizao Xitong/Computer Integr Manuf Syst CIMS 24:2288–2296.
Geng C, Qu S, Xiao Y et al (2018) Diffusion mechanism simulation of cloud manufacturing complex network based on cooperative game theory. J Syst Eng Electron 29:321–335. https://doi.org/10.21629/JSEE.2018.02.13. (PMID: 10.21629/JSEE.2018.02.13)
Guide VDR, Srivastava R (1997) Buffering from material recovery uncertainty in a recoverable manufacturing environment. J Oper Res Soc 48:519–529. https://doi.org/10.1057/palgrave.jors.2600402.
Huang B, Wang W, Ren S, Zhong RY, Jiang J (2019) A proactive task dispatching method based on future bottleneck prediction for the smart factory. Int J Comput Integr Manuf 32:278–293. https://doi.org/10.1080/0951192X.2019.1571241. (PMID: 10.1080/0951192X.2019.1571241)
Jackson MO, Wolinsky A (1996) A strategic model of social and economic networks. J Econ Theory 71:44–74. https://doi.org/10.1006/jeth.1996.0108. (PMID: 10.1006/jeth.1996.0108)
Leng J, Jiang P (2019) Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information. J Intell Manuf 30:979–994. https://doi.org/10.1007/s10845-017-1301-y. (PMID: 10.1007/s10845-017-1301-y)
Li L, Chang Q, Ni J (2009) Data driven bottleneck prediction of manufacturing systems. Int J Prod Res 47:5019–5036. https://doi.org/10.1080/00207540701881860. (PMID: 10.1080/00207540701881860)
Li L, Chang Q, Xiao G, Ambani S (2011) Throughput bottleneck prediction of manufacturing systems using time series analysis. J Manuf Sci Eng Trans ASME 133:1–8. https://doi.org/10.1115/1.4003786. (PMID: 10.1115/1.4003786)
Li L, Li C, Tang Y, Du Y (2017) An integrated approach of reverse engineering aided remanufacturing process for worn components. Robot Comput Integr Manuf 48:39–50. https://doi.org/10.1016/j.rcim.2017.02.004.
Li J, Jiao J, Tang Y (2019) An evolutionary analysis on the effect of government policies on electric vehicle diffusion in complex network. Energy Policy 129:1–12. https://doi.org/10.1016/j.enpol.2019.01.070. (PMID: 10.1016/j.enpol.2019.01.070)
Liao H, Shi Y, Liu X et al (2019) A non-probabilistic model of carbon footprints in remanufacture under multiple uncertainties. J Clean Prod 211:1127–1140. https://doi.org/10.1016/j.jclepro.2018.11.218.
Mojtahedi M, Fathollahi-Fard AM, Tavakkoli-Moghaddam R, Newton S (2021) Sustainable vehicle routing problem for coordinated solid waste management. J Ind Inf Integr 23:100220. https://doi.org/10.1016/j.jii.2021.100220. (PMID: 10.1016/j.jii.2021.100220)
Moosavi J, Naeni LM, Fathollahi-Fard AM, Fiore U (2021) Blockchain in supply chain management: a review, bibliometric, and network analysis. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-13094-3.
Peng S, Li T, Zhao J et al (2019) Petri net-based scheduling strategy and energy modeling for the cylinder block remanufacturing under uncertainty. Robot Comput Integr Manuf 58:208–219. https://doi.org/10.1016/j.rcim.2019.03.004.
Roser C, Nakano M, Tanaka M (2001) A practical bottleneck detection method. Winter Simulation Conference Proceedings 1:949–953.
Roser C, Lorentzen K, Lenze D et al (2017) Bottleneck prediction using the active period method in combination with buffer inventories. 1:374–381. https://doi.org/10.1007/978-3-319-66926-7.
Sengupta S, Das K, Vantil R (2008) A new method for bottleneck detection. Proc 2008 Winter Simul Conf 1922–1930. https://doi.org/10.1109/wsc.2008.4736238.
Subramaniyan M, Skoogh A, Gopalakrishnan M, Salomonsson H, Hanna A, Lämkull D (2016) An algorithm for data-driven shifting bottleneck prediction. Cogent Eng 3:1–19. https://doi.org/10.1080/23311916.2016.1239516. (PMID: 10.1080/23311916.2016.1239516)
Subramaniyan M, Skoogh A, Salomonsson H, Bangalore P, Bokrantz J (2018) A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines. Comput Ind Eng 125:533–544. https://doi.org/10.1016/j.cie.2018.04.024. (PMID: 10.1016/j.cie.2018.04.024)
Subramaniyan M, Skoogh A, Sheikh Muhammad A, Bokrantz J, Turanoğlu Bekar E (2019) A prognostic algorithm to prescribe improvement measures on throughput bottlenecks. J Manuf Syst 53:271–281. https://doi.org/10.1016/j.jmsy.2019.07.004. (PMID: 10.1016/j.jmsy.2019.07.004)
Subramaniyan M, Skoogh A, Sheikh A et al (2020) A generic hierarchical clustering approach for detecting bottlenecks in manufacturing. J Manuf Syst 55:143–158. https://doi.org/10.1016/j.jmsy.2020.02.011. (PMID: 10.1016/j.jmsy.2020.02.011)
Tang H (2019) A new method of bottleneck analysis for manufacturing systems. Manuf Lett 19:21–24. https://doi.org/10.1016/j.mfglet.2019.01.003. (PMID: 10.1016/j.mfglet.2019.01.003)
Teunter RH, Flapper SDP (2011) Optimal core acquisition and remanufacturing policies under uncertain core quality fractions. Eur J Oper Res 210:241–248. https://doi.org/10.1016/j.ejor.2010.06.015.
Tian G, Zhang H, Feng Y, Jia H (2017) Operation patterns analysis of automotive components remanufacturing industry development in China. J Clean Prod 164:1363–1375. https://doi.org/10.1016/j.jclepro.2017.07.028. (PMID: 10.1016/j.jclepro.2017.07.028)
Tian G, Zhou MC, Li P (2018) Disassembly sequence planning considering fuzzy component quality and varying operational cost. IEEE Trans Autom Sci Eng 15:748–760. https://doi.org/10.1109/TASE.2017.2690802. (PMID: 10.1109/TASE.2017.2690802)
Wang XV, Wang L (2017) A cloud-based production system for information and service integration: an internet of things case study on waste electronics. Enterp Inf Syst 11:952–968. https://doi.org/10.1080/17517575.2016.1215539. (PMID: 10.1080/17517575.2016.1215539)
Wang JQ, Chen J, Zhang Y, Huang GQ (2016) Schedule-based execution bottleneck identification in a job shop. Comput Ind Eng 98:308–322. https://doi.org/10.1016/j.cie.2016.05.039. (PMID: 10.1016/j.cie.2016.05.039)
Watts DJ, Strogatz SH (1998) Collective dynamics of small-world network. Nature 393:440–442. (PMID: 10.1038/30918)
Wu JJ, Gao ZY, Sun HJ, Huang HJ (2006) Congestion in different topologies of traffic networks. Europhys Lett 74:560–566. https://doi.org/10.1209/epl/i2005-10551-x. (PMID: 10.1209/epl/i2005-10551-x)
Zhang R, Wu C (2012) Bottleneck machine identification method based on constraint transformation for job shop scheduling with genetic algorithm. Inf Sci (Ny) 188:236–252. https://doi.org/10.1016/j.ins.2011.11.013. (PMID: 10.1016/j.ins.2011.11.013)
Zhang PP, Chen K, He Y et al (2006) Model and empirical study on some collaboration networks. Phys A Stat Mech its Appl 360:599–616. https://doi.org/10.1016/j.physa.2005.05.044. (PMID: 10.1016/j.physa.2005.05.044)
Zhengcai CAO, Jijie D, Min LIU, Yongji W (2012) Bottleneck prediction method based on improved adaptive network-based fuzzy inference system ( ANFIS ) in Semiconductor Manufacturing System *. Chin J Chem Eng 20:1081–1088. https://doi.org/10.1016/S1004-9541(12)60590-4. (PMID: 10.1016/S1004-9541(12)60590-4)
Zhong W, Dai T, Wang G, Li Q, Li D, Liang L, Sun X, Hao X, Jiang M (2018) Structure of international iron flow: based on substance flow analysis and complex network. Resour Conserv Recycl 136:345–354. https://doi.org/10.1016/j.resconrec.2018.05.006. (PMID: 10.1016/j.resconrec.2018.05.006)
Zhuang Z, Huang Z, Chen L, Qin W (2019) A heuristic rule based on complex network for open shop scheduling problem with sequence-dependent setup times and delivery times. IEEE Access 7:140946–140956. https://doi.org/10.1109/ACCESS.2019.2944296. (PMID: 10.1109/ACCESS.2019.2944296)
Contributed Indexing:
Keywords: Bottleneck shifting prediction; Complex network theory; Coupled map lattice; Remanufacturing
Entry Date(s):
Date Created: 20210710 Latest Revision: 20240220
Update Code:
20260130
DOI:
10.1007/s11356-021-15233-2
PMID:
34244948
Database:
MEDLINE
*Further Information*
*Bottleneck shifting prediction has been widely applied to the remanufacturing system for throughput improvement, and it would directly influence the general presentation of the remanufacturing system. However, predicting dynamic bottlenecks of remanufacturing systems is complicated due to the disturbed environment (e.g. various processing time and uncertain processing routes). This paper built a metamorphosis CNT conjunct with coupled map lattice (CML) algorithm to predict the bottleneck shifting phenomenon in remanufacturing for the first time. The CNT was applied to the articulation of remanufacturing process, while the CML algorithm was devoted to calculating the dynamic indicator of the bottleneck. We took the value-added connecting rod as the research object to illustrate the availability of the proposed method. As validated by Arena simulation, the approach presented in this paper put forward is feasible to make an accurate prediction for shifting bottlenecks in a remanufacturing system.
(© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)*