Research article Special Issues

Research on flexibility of production system based on hybrid modeling and simulation

  • Received: 13 September 2020 Accepted: 02 December 2020 Published: 04 January 2021
  • In order to analyze the influence of the personnel flexibility on the flexibility of the production system and optimize the organization and configuration of the production system, this paper puts forward a flexible simulation study of production system personnel based on mixed modeling. Firstly, the flexible evaluation index of production system is constructed from four dimensions: machine utilization ratio, personnel utilization ratio, production and personnel production efficiency. The concrete calculation method is given accordingly. Secondly, the simulation model of discrete event-multibody mixed modeling in production system is established, and the construction principle of the model is given. Thirdly, based on the flexible evaluation index and mixed modeling method, the flexible personnel skills of a motorcycle engine key parts production system are simulated and analyzed, and the comparative analysis of multiple schemes is realized. The simulation results show that multi-energy and personnel cooperation have an important influence on the flexibility of production system. In the medium and large fluctuation production environment, compared with the professional production team, the flexibility of the four dimensions of machine utilization, personnel utilization, production and personnel production efficiency of the multi-energy production team has been significantly improved. The case study also shows that the hybrid modeling simulation model can realize the dynamic configuration and operation evaluation of the flexible personnel elements, and provide a dynamic and quantitative research method for the organizational optimization of the production system.

    Citation: Dawei Ren, Xiaodong Zhang, Shaojuan Lei, Zehua Bi. Research on flexibility of production system based on hybrid modeling and simulation[J]. Mathematical Biosciences and Engineering, 2021, 18(1): 933-949. doi: 10.3934/mbe.2021049

    Related Papers:

  • In order to analyze the influence of the personnel flexibility on the flexibility of the production system and optimize the organization and configuration of the production system, this paper puts forward a flexible simulation study of production system personnel based on mixed modeling. Firstly, the flexible evaluation index of production system is constructed from four dimensions: machine utilization ratio, personnel utilization ratio, production and personnel production efficiency. The concrete calculation method is given accordingly. Secondly, the simulation model of discrete event-multibody mixed modeling in production system is established, and the construction principle of the model is given. Thirdly, based on the flexible evaluation index and mixed modeling method, the flexible personnel skills of a motorcycle engine key parts production system are simulated and analyzed, and the comparative analysis of multiple schemes is realized. The simulation results show that multi-energy and personnel cooperation have an important influence on the flexibility of production system. In the medium and large fluctuation production environment, compared with the professional production team, the flexibility of the four dimensions of machine utilization, personnel utilization, production and personnel production efficiency of the multi-energy production team has been significantly improved. The case study also shows that the hybrid modeling simulation model can realize the dynamic configuration and operation evaluation of the flexible personnel elements, and provide a dynamic and quantitative research method for the organizational optimization of the production system.


    加载中


    [1] R. Laughery, Modeling human performance in systems, in Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Sage Publications Ltd., 1 (2000), 2254–2254.
    [2] R. Qin, D. A. Nembhard, W. L. Barnes II, Workforce flexibility in operations management, Surv. Oper. Res. Manage. Sci., 20 (2015), 19–33.
    [3] Y. Yang, J. Tang, W. Sun, Y. Yin, I. Kaku, Reducing worker(s) by converting assembly line into a pure cell system, Int. J. Prod. Econ., 145 (2013), 799–806. doi: 10.1016/j.ijpe.2013.06.009
    [4] G. A. Süer, K. Kamat, E. Mese, J. Huang, Minimizing total tardiness subject to manpower restriction in labor–intensive manufacturing cells, Math. Comput. Modell., 57 (2013), 741–753. doi: 10.1016/j.mcm.2012.08.013
    [5] G. Egilmez, B. Erenay, G. A. Süer, Stochastic skill-based manpower allocation in a cellular manufacturing system, J. Manuf. Syst., 33 (2014), 578–588.
    [6] J. S. Evans, Strategic flexibility for high technology manoeuvres: a conceptual framework, J. Manage. Stud., 28 (1991), 69–89. doi: 10.1111/j.1467-6486.1991.tb00271.x
    [7] R. Xu, Q. Qin, Impact of Entrepreneurial Orientation and Organizational Flexibility on Technological Innovation, in 2010 3rd International Conference on Information Management, Innovation Management and Industrial Engineering, 2 (2010), 421–425.
    [8] M. Dağdeviren, A hybrid multi-criteria decision-making model for personnel selection in manufacturing systems, J. Intell. Manuf., 21 (2010), 451–460. doi: 10.1007/s10845-008-0200-7
    [9] O. Oleghe, K. Salonitis, Hybrid simulation modelling of the human-production process interface in lean manufacturing systems, Int. J. Lean Six Sigma, 10 (2019), 665–690. doi: 10.1108/IJLSS-01-2018-0004
    [10] M. K. Malhotra, T. D. Fry, H. V. Kher, J. M. Donohue, The impact of learning and labor attrition on worker flexibility in dual resource constrained job shops, Decis. Sci., 24 (1993), 641–664. doi: 10.1111/j.1540-5915.1993.tb01296.x
    [11] B. Denkena, M. A. Dittrich, F. Winter, C. Wagener, Simulation-based planning and evaluation of personnel scheduling in knowledge-intensive production systems, Prod. Eng., 10 (2016), 489–496. doi: 10.1007/s11740-016-0693-4
    [12] D. J. Davis, H. V. Kher, B. J. Wagner, Influence of workload imbalances on the need for worker flexibility, Comput. Ind. Eng., 57 (2009), 319–329. doi: 10.1016/j.cie.2008.11.029
    [13] C. Pach, T. Berger, Y. Sallez, T. Bonte, E. Adam, D. Trentesaux, Reactive and energy-aware scheduling of flexible manufacturing systems using potential fields, Comput. Ind., 65 (2014), 434–448. doi: 10.1016/j.compind.2013.11.008
    [14] A. Ferjani, A. Ammar, H. Pierreval, S. Elkosantini, A simulation-optimization based heuristic for the online assignment of multi–skilled workers subjected to fatigue in manufacturing systems, Comput. Ind. Eng., 112 (2017), 663–674. doi: 10.1016/j.cie.2017.02.008
    [15] F. Phillips, S. D. Tuladhar, Measuring organizational flexibility: an exploration and general model, Technol. Forecasting Soc. Change, 64 (2000), 23–38. doi: 10.1016/S0040-1625(99)00077-3
    [16] R. V. Rao, A decision-making framework model for evaluating flexible manufacturing systems using digraph and matrix methods, Int. J. Adv. Manuf. Technol., 30 (2006), 1101–1110. doi: 10.1007/s00170-005-0150-6
    [17] H. Yue, J. Slomp, E. Molleman, D. J. Van Der Zee, Worker flexibility in a parallel dual resource constrained job shop, Int. J. Prod. Res., 46 (2008), 451–467.
    [18] D. Nembhard, K. Prichanont, Cross training in serial production with process characteristics and operational factors, IEEE Trans. Eng. Manage., 54 (2007), 565–575. doi: 10.1109/TEM.2007.900793
    [19] K. Georgoulias, N. Papakostas, G. Chryssolouris, S. Stanev, H. Krappe, J. Ovtcharova, Evaluation of flexibility for the effective change management of manufacturing organizations, Rob. Comput. Integr. Manuf., 25 (2009), 888–893. doi: 10.1016/j.rcim.2009.04.010
    [20] S. K. Singh, M. K. Singh, Evaluation of productivity, quality and flexibility of an advanced manufacturing system, J. Inst. Eng. (India): Ser. C, 93 (2012), 93–101. doi: 10.1007/s40032-011-0002-0
    [21] M. Naderi, E. Ares, G. Peláez, D. Prieto, A. Fernández, L. P. Ferreira, The sustainable evaluation of manufacturing systems based on simulation using an economic index function: A case study, Procedia Manuf., 13 (2017), 1043–1050. doi: 10.1016/j.promfg.2017.09.128
    [22] A. Yadav, S. C. Jayswal, Evaluation of batching and layout on the performance of flexible manufacturing system, Int. J. Adv. Manuf. Technol., 101 (2019), 1435–1449. doi: 10.1007/s00170-018-2999-1
    [23] N. V. Hop, Approach to measure the mix response flexibility of manufacturing systems, In. J. Prod. Res., 42 (2004), 1407–1418. doi: 10.1080/00207540310001638064
    [24] M. I. M. Wahab, D. Wu, C. Lee, A generic approach to measuring the machine flexibility of manufacturing systems, Eur. J. Oper. Res., 186 (2008), 137–149. doi: 10.1016/j.ejor.2007.01.052
    [25] S. Kemmoe, P. Pernot, N. Tchernev, Model for flexibility evaluation in manufacturing network strategic planning, Int. J. Prod. Res., 52 (2014), 4396–4411. doi: 10.1080/00207543.2013.845703
    [26] R. Mishra, A comparative evaluation of manufacturing flexibility adoption in SMEs and large firms in India, J. Manuf. Technol. Manage., 27 (2016), 730–762. doi: 10.1108/JMTM-11-2015-0105
    [27] F. Long, P. Zeiler, B. Bertsche, Modelling the flexibility of production systems in Industry 4.0 for analysing their productivity and availability with high-level Petri nets, IFAC-PapersOnLine, 50 (2017), 5680–5687.
    [28] R. Mishra, A. K. Pundir, L. Ganapathy, Evaluation and prioritisation of manufacturing flexibility alternatives using integrated AHP and TOPSIS method, Benchmarking: Int. J., 24 (2017), 1437–1465. doi: 10.1108/BIJ-07-2015-0077
    [29] M. Mohun, Modelling of fuzzy discrete event dynamic systems, in Proceedings of 2005 International Conference on Intelligent Sensing and Information Processing, (2005), 278–283.
    [30] R. Kia, A. Baboli, N. Javadian, R. Tavakkoli-Moghaddam, M. Kazemi, J. Khorrami, Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing, Comput. Oper. Res., 39 (2012), 2642–2658. doi: 10.1016/j.cor.2012.01.012
    [31] B. Lennartson, O. Wigstrom, M. Fabian, F. Basile, Unified model for synthesis and optimization of discrete event and hybrid systems, IFAC Proc. Vol., 47 (2014), 86–92.
    [32] D. Antonelli, P. Litwin, D. Stadnicka, Multiple system dynamics and discrete event simulation for manufacturing system performance evaluation, Procedia CIRP, 78 (2018), 178–183. doi: 10.1016/j.procir.2018.08.312
    [33] S. E. H. Petroodi, A. B. D. Eynaud, N. Klement, R. Tavakkoli-Moghaddam, Simulation–based optimization approach with scenario–based product sequence in a reconfigurable manufacturing system (RMS): A case study, IFAC-PapersOnLine, 52 (2019), 2638–2643. doi: 10.1016/j.ifacol.2019.11.605
    [34] D. Xu, C. Lu, W. Zhou, S. Liu, Hybrid model of multi-agent and DEDS for steelmaking-continuous casting-hot rolling manufacturing process simulation, in The 26th Chinese Control and Decision Conference (2014 CCDC), (2014), 1936–1940.
    [35] Z. Lin, A. Matta, J. G. Shanthikumar, Combining simulation experiments and analytical models with area–based accuracy for performance evaluation of manufacturing systems, IISE Trans., 51 (2019), 266–283. doi: 10.1080/24725854.2018.1490046
    [36] P. Pawlewski, M. Anholcer, Relational database template in the simulation modeling of manufacturing systems, IFAC PapersOnLine, 52 (2019), 1744–1748. doi: 10.1016/j.ifacol.2019.11.453
    [37] A. V. Barenji, R. V. Barenji, Improving multi-agent manufacturing control system by indirect communication based on ant agents, Proc. Inst. Mech. Eng., Part I: J. Syst. Control Eng., 231 (2017), 447–458.
    [38] X. Zhang, S. Tang, X. Liu, R. Malekian, Z. Li, A novel multi-agent-based collaborative virtual manufacturing environment integrated with edge computing technique, Energies, 12 (2019), 2815. doi: 10.3390/en12142815
    [39] X. Zhang, J. Qiu, D. Zhao, C. M. Schlick, A Human-Oriented Simulation Approach for Labor Assignment Flexibility in Changeover Processes of Manufacturing Cells, Hum. Factors Ergon. Manuf. Serv. Ind., 25 (2015), 740–757. doi: 10.1002/hfm.20589
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2335) PDF downloads(217) Cited by(1)

Article outline

Figures and Tables

Figures(3)  /  Tables(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog