Decentralized Scheduling and Dispatch Control for the Traditional Labor-Intensive Assembly System

• Autorzy: Sri Raharno , Vina Sari Yosephine , Rachmad Hartono , Ari Setiawan
• Języki publikacji: EN

Abstrakty

EN

The fourth industrial revolution has broadly transformed the manufacturing system. However, this transformation is somewhat lacking in traditional or manual production systems due to the absence of IT infrastructure. Such traditional industries need to have the advantage of real-time control and monitoring. This study has developed economic assembly planning, scheduling, and control for a traditional assembly system. We used the concept of the configurable virtual workstation as the digitalization framework. Then, we employed the decentralized scheduling concept to reduce the computational effort in scheduling the complex product. The implementation result showed that scheduling and planning have transformed the traditional assembly process into intelligent scheduling and control with low digitalization effort.(original abstract)

Słowa kluczowe

EN

Production; Implementation of systems; Production planning; Decentralization

PL

Produkcja; Wdrożenie systemów; Planowanie produkcji; Decentralizacja

• Czasopismo: Management and Production Engineering Review
• Rocznik: 2024
• Tom: 15
• Numer: nr 1
• Strony: 3--11

Twórcy

autor

Sri Raharno

• Institut Teknologi Bandung, Indonesia

autor

Vina Sari Yosephine

• Institut Teknologi Harapan Bangsa, Indonesia

autor

Rachmad Hartono

• Universitas Pasundan, Indonesia

autor

Ari Setiawan

Bibliografia

• Baheti, R., & Gill. (2011). Cyber-physical systems. ScienceDirect, 12(1), 161-166.
• Braun, A.T., Colangelo, E., & Steckel, T. (2018). Farming in the Era of Industrie 4.0. Procedia CIRP, 72, 979- 984. DOI: 10.1016/j.procir.2018.03.176.
• Charania, I., & Li, X. (2020). Smart farming: Agriculture's shift from a labor intensive to technology native industry. Internet of Things, 9, 100142. DOI: 10.1016/j.iot.2019.100142.
• ElMaraghy, H., & ElMaraghy, W. (2016). Smart Adaptable Assembly Systems. Procedia CIRP, 44, 4-13. DOI: 10.1016/j.procir.2016.04.107.
• Gökalp, E., Gökalp, M.O., & Eren, P.E. (2019). Industry 4.0 revolution in clothing and apparel sector: smart apparel factory proposal. Academic Journal of Information Technology, 10(37)
• Guide, V.D.R., Srivastava, R., & Kraus, M.E. (1997). Product structure complexity and scheduling of operations in recoverable manufacturing. International Journal of Production Research, 35(11), 3179-3200. DOI: 10.1080/002075497194345.
• Guiza, O., Mayr-Dorn, C., Weichhart, G., Mayrhofer, M., Zangi, B.B., Egyed, A., Fanta, B., & Gieler, M. (2021). Monitoring of Human-Intensive Assembly Processes Based on Incomplete and Indirect Shopfloor Observations. 2021 IEEE 19th International Conference on Industrial Informatics (INDIN), 1-8. DOI: 10.1109/INDIN45523.2021.9557551.
• Hartono, R., Raharno, S., Martawirya, Y.Y., & Arthaya, B. (2018). Development of Product Availability Monitoring System In Production Unit In Automotive Component Industry. IOP Conference Series: Materials Science and Engineering, 319, 012014. DOI: 10.1088/1757-899X/319/1/012014.
• Hartono, R., Raharno, S., Pane, M.Y., Zulfahmi, M., Yusuf, M., Yuwana, Y., & Harja, H.B. (2020). Development of production monitoring systems on laborintensive manufacturing industries. In Industry 4.0 - Shaping The Future of The Digital World. CRC Press.
• Hedman, R., & Almström, P. (2017). A state of the art system for managing time data in manual assembly. International Journal of Computer Integrated Manufacturing, 30(10), 1060-1071. DOI: 10.1080/ 0951192X.2017.1305501.
• Hu, S.J., Ko, J., Weyand, L., ElMaraghy, H.A., Lien, T.K., Koren, Y., Bley, H., Chryssolouris, G., Nasr, N., & Shpitalni, M. (2011). Assembly system design and operations for product variety. CIRP Annals, 60(2), 715-733. DOI: 10.1016/j.cirp.2011.05.004.
• Iwamura, K., & Sugimura, N. (2010). A study on realtime scheduling for autonomous distributed manufacturing systems. 2010 IEEE International Conference on Systems, Man and Cybernetics, 1352-1357. DOI: 10.1109/ICSMC.2010.5642451.
• Jung-Ug Kim & Yeong-Dae Kim. (1996). Simulated annealing and genetic algorithms for scheduling products with multi-level product structure. Computers & Operations Research, 23(9), 857-868. DOI: 10.1016/0305- 0548(95)00079-8.
• Kärcher, S., Cuk, E., Denner, T., Görzig, D., Günther, L.C., Hansmersmann, A., Riexinger, G., & Bauernhansl, T. (2018). Sensor-driven Analysis of Manual Assembly Systems. Procedia CIRP, 72, 1142-1147. DOI: 10.1016/j.procir.2018.03.241.
• Kim, J.-C., & Moon, I.-Y. (2020). A Study on Smart Factory Construction Method for Efficient Production Management in Sewing Industry. Journal of Information and Communication Convergence Engineering, 18(1), 61-68. DOI: 10.6109/JICCE.2020.18.1.61.
• Komine, K., Saito, M., Nakato, A., Sasaki, I., & Hayashi, H. (2020). A market mechanism for autonomous distributed manufacturing systems - Towards the improvement of total productivity and individual profit margins. 2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI), 393-399. DOI: 10.1109/IIAI-AAI50415.2020.00086.
• Lee, J., Bagheri, B., & Kao, H.-A. (2015). A Cyber- Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18- 23. DOI: 10.1016/j.mfglet.2014.12.001.
• Lewandowski, R., & Olszewska, J.I. (2020). Automated Task Scheduling for Automotive Industry. 2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES), 159-164. DOI: 10.1109/ INES49302.2020.9147169.
• Li, X., & Gao, L. (2016). An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. International Journal of Production Economics, 174, 93-110. DOI: 10.1016/ j.ijpe.2016.01.016.
• Pinedo, M. (2005). Planning and scheduling in manufacturing and services. Springer Link. https://link. springer.com/book/10.1007/b139030.
• Raharno, S., & Cooper, G. (2020). Jumping to Industry 4.0 through process design and managing information for smart manufacturing: Configurable virtual workstation. In Industry 4.0 - Shaping The Future of The Digital World (Vol. 1, p. 5). CRC Press.
• Ren, S.C.X., Chaw, J.K., Lim, Y.M., Lee, W.P., Ting, T.T., & Fong, C.W. (2022). Intelligent Manufacturing Planning System Using Dispatch Rules: A Case Study in Roofing Manufacturing Industry. Applied Sciences, 12 (13), 6499. DOI: 10.3390/app12136499.
• Tarallo, A., Mozzillo, R., Di Gironimo, G., & De Amicis, R. (2018). A cyber-physical system for production monitoring of manual manufacturing processes. International Journal on Interactive Design and Manufacturing (IJIDeM), 12(4), 1235-1241. DOI: 10.1007/s12008- 018-0493-5.
• Yatna, Y. M., Raharno, S., and Ruriardi, R. (2014). Real- Time Scheduling Procedures Based on Autonomous Distributed Manufacturing System (ADiMS) Concept. Applied Mechanics and Materials, 660, 1010-1014. DOI: 10.4028/www.scientific.net/AMM. 660.1010.
• Yoshihiro Yao, Toshiya Kaihara, Kentaro Sashio, & Susumu Fujii. (2007). A study on automated scheduling methodology for machining job shop. 2007 IEEE Conference on Emerging Technologies & Factory Automation (EFTA 2007), 1018-1023. DOI: 10.1109/EFTA.2007.4416895.

Identyfikatory

DOI

10.24425/mper.2024.149986