Study the Effect of Route Penalty on FMS Considering Routing Flexibility

Article Preview

Abstract:

This work is aimed to deepen a research theme focused on manufacturing system in a flexible environment. It is categorized by an emphasis on modeling a flexible manufacturing system involving design, planning and control decisions. The design decision comprises of manufacturing flexibility i.e., routing flexibility (RF0, RF1, RF2 and RF3), and buffer size (5, 10, 15 and 20) whereas the planning decision covers route penalty (0%, 5%, 10% and 15%) and system configuration (6 machines with dedicated input buffer) while control decision covers dispatching rule (MINQ), and sequencing rule (FCFS, SPT, HPT and LCFS). The study uses make-span as performance measures to study the impact of route penalty in different conditions. This study is focused on the interaction among various decisions in order to control the part flow effectively through the manufacturing system. It is observed that the performance of the system has marginally diminished with the increase in the %route penalty and it is also found that the %route penalty has an effect on the performance measures but this effect is more visible at higher levels of routing flexibilities in compare to the lower levels of routing flexibility. Other parameters have mixed effect on the performance measures that are discussed in the results.

You might also be interested in these eBooks

Info:

Periodical:

Engineering Headway (Volume 11)

Pages:

45-54

Citation:

Online since:

July 2024

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2024 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Ali, M., & Wadhwa, S. (2010). The effect of routing flexibility on a flexible system of integrated manufacturing. International Journal of Production Research, 48, 5691–5709.

DOI: 10.1080/00207540903100044

Google Scholar

[2] Amrik Singh, Jagtar Singh, and Mohammad Ali (2018) "A Simulation Study for Investigation of Routing Flexibility on Performance in FMS Environment". Indian Journal of Science and Technology Vol.29(11).

Google Scholar

[3] Chan, F. T. S., Bhagwat, R., & Wadhwa, S. (2007). Taguchi's method analysis of an FMS under review-period-based operational controls: Identification of control periodicity. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 37(2), 212–225

DOI: 10.1109/TSMCA.2006.886355

Google Scholar

[4] Chauhan, G., & Singh, T. (2011). Lean Manufacturing through Management of Labor and Machine Flexibility: A Comprehensive Review. Global Journal of Flexible Systems Management, 12, 59–80

DOI: 10.1007/BF03396599

Google Scholar

[5] Chen, J., Zhang, H., Chen, Q., Mao, N., & Wang, J. (2022). Performance analysis of flexible flow shop with parallel manufacturing cells. Computers and Industrial Engineering, 173

DOI: 10.1016/j.cie.2022.108739

Google Scholar

[6] Coito, T., Martins, M. S. E., Firme, B., Figueiredo, J., Vieira, S. M., & Sousa, J. M. C. (2022). Assessing the impact of automation in pharmaceutical quality control labs using a digital twin. Journal of Manufacturing Systems, 62, 270–285

DOI: 10.1016/j.jmsy.2021.11.014

Google Scholar

[7] Diaz C., J. L., & Ocampo-Martinez, C. (2019). Energy efficiency in discrete-manufacturing systems: Insights, trends, and control strategies. Journal of Manufacturing Systems, 52, 131–145

DOI: 10.1016/J.JMSY.2019.05.002

Google Scholar

[8] Framinan, J. M., González, P. L., & Ruiz-Usano, R. (2003). The CONWIP production control system: Review and research issues. In Production Planning and Control (Vol. 14, Issue 3, p.255–265)

DOI: 10.1080/0953728031000102595

Google Scholar

[9] Guo, H., Chen, M., Mohamed, K., Qu, T., Wang, S., & Li, J. (2021). A digital twin-based flexible cellular manufacturing for optimization of air conditioner line. Journal of Manufacturing Systems, 58, 65–78

DOI: 10.1016/J.JMSY.2020.07.012

Google Scholar

[10] Irani, Z., Sharif, A., & Mustafa, K. (2014). Visualising a knowledge mapping of information systems investment evaluation. Expert Systems with Applications: An International Journal, 41, 105–125

DOI: 10.1016/j.eswa.2013.07.015

Google Scholar

[11] Joseph, O. A., & Sridharan, R. (2012). Effects of flexibility and scheduling decisions on the performance of an FMS: Simulation modelling and analysis. International Journal of Production Research, 50(7), 2058–2078

DOI: 10.1080/00207543.2011.575091

Google Scholar

[12] Lo, J.-J., & Lin, L. (1999). An object-oriented FMS real-time and feedback control model. International Journal of Computer Integrated Manufacturing, 12(6), 483–502

DOI: 10.1080/095119299130074

Google Scholar

[13] Mallikarjuna, K., Veeranna, V., Hema, K., & Reddy, C. (2013). Optimum design of loop layout in flexible manufacturing system-An approach of metaheuristics. In International Journal of Advances in Engineering & Technology (Vol. 6).

Google Scholar

[14] Mohamed, Z. M., Youssef, M. A., & Huq, F. (2001). The impact of machine flexibility on the performance of flexible manufacturing systems. International Journal of Operations and Production Management, 21(5–6), 707–727

DOI: 10.1108/01443570110390417

Google Scholar

[15] Pérez-Pérez, M., KocabasogluHillmer, C., Serrano, A., & López-Fernández, M. (2019). Manufacturing and Supply Chain Flexibility: Building an Integrative Conceptual Model Through Systematic Literature Review and Bibliometric Analysis. Global Journal of Flexible Systems Management, 20

DOI: 10.1007/s40171-019-00221-w

Google Scholar

[16] Rachamadugu, R. A. M., &Stecke, K. E. (1994). Classification and review of FMS scheduling procedures. Production Planning & Control, 5(1), 2–20

DOI: 10.1080/09537289408919468

Google Scholar

[17] Rao, R. V. (2008). Evaluating flexible manufacturing systems using a combined multiple attribute decision making method. International Journal of Production Research, 46(7), 1975–1989

DOI: 10.1080/00207540601011519

Google Scholar

[18] Tanli, M., Jiang, Y., Wang, X., Wang, Y., & Peng, R. (2017). Research on Global Structure for Digital Quality Testing Based on Manufacturing Factory

DOI: 10.2991/EAME-17.2017.42

Google Scholar

[19] Wadhwa, S. and Bhagwat, R. (1998). Judicious increase in flexibility and decision automation in semi-computerized flexible manufacturing (SCFM) systems. Studies in Informatics and Control, 7((4)), 329–342.

Google Scholar

[20] Zhang, Z., Wang, X., Wang, X., Cui, F., & Cheng, H. (2019). A simulation-based approach for plant layout design and production planning. Journal of Ambient Intelligence and Humanized Computing, 10(3), 1217–1230

DOI: 10.1007/S12652-018-0687-5

Google Scholar