An Integrated Stochastic Optimization and Simulation Approach to SERU vs. Assembly Line Manufacturing Systems
DOI:
https://doi.org/10.33423/jabe.v26i5.7351Keywords:
business, economics, cellular manufacturing, assembly lines, skilled workers, optimization, discrete event simulation, production systemsAbstract
This research compares SERU manufacturing systems to traditional assembly lines, focusing on the impact of uncertainty in task processing time on production output. The study considers worker skill levels and team identity, using a stochastic mixed integer linear programming approach to model uncertainty and optimize workforce allocation. Discrete event simulation is then integrated to evaluate performance using five key performance indicators (KPIs). Results show that SERU systems outperform traditional lines in terms of throughput when uncertainty is considered. The integrated approach provides more reliable performance data than deterministic optimization alone. The study also highlights the advantages of SERU systems when worker skill levels and team identity are factored in. This research fills a gap in the literature by proposing a stochastic optimization approach that considers uncertainty and worker skill levels, and by integrating stochastic optimization with simulation for comprehensive analysis. This approach provides valuable guidance for production managers in optimizing production systems.
References
Abdullah, M. (2018). Impact of skill: SERU vs classical assembly line. Ohio University. Retrieved from https://rave.ohiolink.edu/etdc/view?acc_num=ohiou1515681463200986
Abdullah, M., & Süer, G.A. (2019). Consideration of skills in assembly lines and SERU production systems. Asian Journal of Management Science and Applications, 4(2), 99. https://doi.org/10.1504/AJMSA.2019.110377
Aboelfotoh, A., Süer, G.A., & Abdullah, M. (2018). Selection of assembly systems: Assembly lines vs. SERU systems. Procedia Computer Science, 140, 351–358. https://doi.org/10.1016/j.procs.2018.10.304
Alhawari, O.I., Süer, G.A., & Bhutta, M.K.S. (2021). Operations performance considering demand coverage scenarios for individual products and product families in supply chains. International Journal of Production Economics, 233, 108012. https://doi.org/10.1016/j.ijpe.2020.108012
Ayough, A., Hosseinzadeh, M., & Motameni, A. (2020). Job rotation scheduling in the SERU system: Shake enforced invasive weed optimization approach. Assembly Automation, 40(3), 461–474. https://doi.org/10.1108/AA-07-2019-0126/full/xml
Deepak, A., Srivatsan, R., & Transactions, V.S. (2017). A case study on implementation of walking worker assembly line to improve productivity and utilization of resources in a heavy duty manufacturing industry. Facta Universitatis, Series: Mechanical Engineering, 15(4), 496–507. https://doi.org/10.5937/fmet1704496d
Egilmez, G., & Süer, G.A. (2014). The impact of risk on the integrated cellular design and control. International Journal of Production Research, 52(5), 1455–1478. https://doi.org/10.1080/00207543.2013.844375
Egilmez, G., Erenay, B., & Süer, G.A. (2019). Hybrid cellular manufacturing system design with cellularisation ratio: An integrated mixed-integer nonlinear programming and discrete event simulation approach. International Journal of Services and Operations Management, 32(1), 1–24. https://doi.org/10.1504/IJSOM.2019.097036
Egilmez, G., Süer, G.A., & Huang, J. (2012). Stochastic cellular manufacturing system design subject to maximum acceptable risk level. Computers & Industrial Engineering, 63(4), 842–854. https://doi.org/10.1016/j.cie.2012.05.006
Fujita, Y., Izui, K., Nishiwaki, S., Zhang, Z., & Yin, Y. (2022). Production planning method for SERU production systems under demand uncertainty. Computers & Industrial Engineering, 163, 107856. https://doi.org/10.1016/j.cie.2021.107856
Gai, Y., Yin, Y., Tang, J., & Liu, S. (2022). Minimizing makespan of a production batch within concurrent systems: SERU production perspective. Journal of Management Science and Engineering, 7(1), 1–18. https://doi.org/10.1016/j.jmse.2020.10.002
Jiang, Y., Zhang, Z., Gong, X., & Yin, Y. (2021). An exact solution method for solving SERU scheduling problems with past-sequence-dependent setup time and learning effect. Computers & Industrial Engineering, 158, 107354. https://doi.org/10.1016/j.cie.2021.107354
Khalafallah, S., & Egilmez, G. (2021). A stochastic mixed integer linear programming approach to skill-based workforce allocation in SERUs. IIE Annual Conference Proceedings, pp. 1070–1075. Retrieved from https://www.proquest.com/openview/f557f1422b923f348921ada145b3caac/1?cbl=51908&pq-origsite=gscholar
Lian, J., Liu, C.G., Li, W.J., & Yin, Y. (2018). A multi-skilled worker assignment problem in SERU production systems considering the worker heterogeneity. Computers & Industrial Engineering, 118, 366–382. https://doi.org/10.1016/j.cie.2018.02.035
Liu, C., Stecke, K.E., Lian, J., & Yin, Y. (2014). An implementation framework for SERU production. International Transactions in Operational Research, 21(1), 1–19. https://doi.org/10.1111/itor.12014
Liu, C., Yang, N., Li, W., Lian, J., Evans, S., & Yin, Y. (2013). Training and assignment of multi-skilled workers for implementing SERU production systems. International Journal of Advanced Manufacturing Technology, 69(5–8), 937–959. https://doi.org/10.1007/s00170-013-5027-5/metrics
Liu, F., Niu, B., Xing, M., Wu, L., & Feng, Y. (2021). Optimal cross-trained worker assignment for a hybrid SERU production system to minimize makespan and workload imbalance. Computers & Industrial Engineering, 160, 107552. https://doi.org/10.1016/j.cie.2021.107552
Mosadegh, H., Fatemi Ghomi, S.M.T., & Süer, G.A. (2020). Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics. European Journal of Operational Research, 282(2), 530–544. https://doi.org/10.1016/j.ejor.2019.09.021
Shan, H. (2022). Assembly line-SERU conversion in the C2M enterprise: An empirical study in China. Assembly Automation, 42(4), 506–520. https://doi.org/10.1108/AA-04-2022-0087/full/xml
Süer, G., Ulutas, B., Kaku, I., & Yin, Y. (2019). Considering product life cycle stages and worker skill level in SERU production systems. Procedia Manufacturing, 39, 1097–1103. https://doi.org/10.1016/j.promfg.2020.01.361
Sun, W., Wu, Y., Lou, Q., & Yu, Y. (2019). A cooperative coevolution algorithm for the SERU production with minimizing makespan. IEEE Access, 7, 5662–5670. https://doi.org/10.1109/access.2018.2889372
Wu, L., Chan, F.T.S., Niu, B., & Li, L. (2018). Cross-trained worker assignment and comparative analysis on throughput of divisional and rotating SERU. Industrial Management and Data Systems, 118(5), 1114–1136. https://doi.org/10.1108/IMDS-07-2017-0303/full/xml
Yılmaz, Ö.F. (2019). Operational strategies for SERU production system: A bi-objective optimisation model and solution methods. International Journal of Production Research, 58(11), 3195–3219. https://doi.org/10.1080/00207543.2019.1669841
Yılmaz, Ö.F. (2020). Attaining flexibility in SERU production system by means of Shojinka: An optimization model and solution approaches. Computers & Operations Research, 119, 104917. https://doi.org/10.1016/j.cor.2020.104917
Yin, Y., Stecke, K.E., Swink, M., & Kaku, I. (2017). Lessons from SERU production on manufacturing competitively in a high cost environment. Journal of Operations Management, pp. 49–51, 67–76. https://doi.org/10.1016/j.jom.2017.01.003
Ying, K.C., & Tsai, Y.J. (2017). Minimising total cost for training and assigning multiskilled workers in SERU production systems. International Journal of Production Research, 55(10), 2978–2989. https://doi.org/10.1080/00207543.2016.1277594
Yu, Y., & Tang, J. (2019). Review of SERU production. Frontiers of Engineering Management, 6(2), 183–192. https://doi.org/10.1007/s42524-019-0028-1
Yu, Y., Sun, W., Tang, J., Kaku, I., & Wang, J. (2017). Line-SERU conversion towards reducing worker(s) without increasing makespan: Models, exact and meta-heuristic solutions. International Journal of Production Research, 55(10), 2990–3007. https://doi.org/10.1080/00207543.2017.1284359
Zeng, S., Wu, Y., & Yu, Y. (2022). Multi-skilled worker assignment in SERU production system for the trade-off between production efficiency and workload fairness. Kybernetes, ahead-of-print(ahead-of-print). https://doi.org/10.1108/K-01-2022-0054/full/xml
Zhang, X., Liu, C., Li, W., Evans, S., & Yin, Y. (2017). Effects of key enabling technologies for SERU production on sustainable performance. Omega. Retrieved February 21, 2023, from https://www.sciencedirect.com/science/article/pii/S0305048316000189
Zhang, X.L., Liu, C.G., Li, W.J., Evans, S., & Yin, Y. (2017). Effects of key enabling technologies for SERU production on sustainable performance. Omega, 66, 290–307. https://doi.org/10.1016/j.omega.2016.01.013
Zhang, Z., Shen, L., Gong, X., Zhong, X., & Yin, Y. (2023). A genetic-simulated annealing algorithm for stochastic SERU scheduling problem with deterioration and learning effect. Journal of Industrial and Production Engineering. https://doi.org/10.1080/21681015.2023.2167875
Zhang, Z., Song, X., Huang, H., Yin, Y., & Lev, B. (2022). Scheduling problem in SERU production system considering DeJong’s learning effect and job splitting. Annals of Operations Research, 312(2), 1119–1141. https://doi.org/10.1007/s10479-021-04515-0/figures/8
Zhang, Z., Wang, L., Song, X., Huang, H., & Yin, Y. (2022). Improved genetic-simulated annealing algorithm for SERU loading problem with downward substitution under stochastic environment. Journal of the Operational Research Society, 73(8), 1800–1811. https://doi.org/10.1080/01605682.2021.1939172
Zwierzyński, P., & Ahmad, H. (2018). SERU production as an alternative to a traditional assembly line. Engineering Management in Production and Services, 10(3), 62–69. https://doi.org/10.2478/emj-2018-0017