Optimization of material transportation system for factory logistic

Material transportation system (MTS) often being used to move materials inside a factory, warehouse, or other facility. The five main types of equipment are industrial trucks, automated guided (AGV) vehicles, rail-guided vehicles, conveyors, and hoist and cranes. This report focused on AGV where the...

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Main Author:	Mohamad, Nor Rashidah
Format:	Thesis
Language:	English English
Published:	2015
Subjects:	H Social Sciences (General) HE Transportation and Communications
Online Access:	http://eprints.utem.edu.my/id/eprint/15866/1/Nor%20Rashidah%20Bte%20Mohamad.pdf http://eprints.utem.edu.my/id/eprint/15866/2/Optimization%20of%20material%20transportation%20system%20for%20factory%20logistic.pdf
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id	my-utem-ep.15866
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Md Fauadi, Muhammad Hafidz Fazli
topic	H Social Sciences (General) HE Transportation and Communications
spellingShingle	H Social Sciences (General) HE Transportation and Communications Mohamad, Nor Rashidah Optimization of material transportation system for factory logistic
description	Material transportation system (MTS) often being used to move materials inside a factory, warehouse, or other facility. The five main types of equipment are industrial trucks, automated guided (AGV) vehicles, rail-guided vehicles, conveyors, and hoist and cranes. This report focused on AGV where the optimization of MTS is further studied. Applying an AGVs in logistic factory may help in improving the efficiency in material flow and distribution among workstation at right time and right place. The main objective of this project is to study transportation requirement in a factory which consist of dynamic factors. The used of dynamic system in modelling gives an advantages in term of flexibility for changes of orders, unexpected machine or equipment failure, production delays, and other decisions then feedback to alter inventories and backlogs. This report present the method to organize and analyze the movement of AGV in warehouse area to obtain the optimum number of AGVs required in the warehouse to fulfill all the task given by simulation software. Anylogic software is being used to build a simulation model and analyzed the system performance. The obtained results was the optimization of MTS for factory logistic which produce effective material handling system and creates the systematic handling system in warehouse. By manipulating number of AGV, system throughput and cycle time being observed. Data obtained from simulation being compared as number of AGV had change. Minitab 17 software are used to create statistical graph in 2D and 3D surface in order to analyze and evaluate the results.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohamad, Nor Rashidah
author_facet	Mohamad, Nor Rashidah
author_sort	Mohamad, Nor Rashidah
title	Optimization of material transportation system for factory logistic
title_short	Optimization of material transportation system for factory logistic
title_full	Optimization of material transportation system for factory logistic
title_fullStr	Optimization of material transportation system for factory logistic
title_full_unstemmed	Optimization of material transportation system for factory logistic
title_sort	optimization of material transportation system for factory logistic
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Manufacturing Engineering
publishDate	2015
url	http://eprints.utem.edu.my/id/eprint/15866/1/Nor%20Rashidah%20Bte%20Mohamad.pdf http://eprints.utem.edu.my/id/eprint/15866/2/Optimization%20of%20material%20transportation%20system%20for%20factory%20logistic.pdf
_version_	1747833878047883264
spelling	my-utem-ep.158662022-06-13T15:07:00Z Optimization of material transportation system for factory logistic 2015 Mohamad, Nor Rashidah H Social Sciences (General) HE Transportation and Communications Material transportation system (MTS) often being used to move materials inside a factory, warehouse, or other facility. The five main types of equipment are industrial trucks, automated guided (AGV) vehicles, rail-guided vehicles, conveyors, and hoist and cranes. This report focused on AGV where the optimization of MTS is further studied. Applying an AGVs in logistic factory may help in improving the efficiency in material flow and distribution among workstation at right time and right place. The main objective of this project is to study transportation requirement in a factory which consist of dynamic factors. The used of dynamic system in modelling gives an advantages in term of flexibility for changes of orders, unexpected machine or equipment failure, production delays, and other decisions then feedback to alter inventories and backlogs. This report present the method to organize and analyze the movement of AGV in warehouse area to obtain the optimum number of AGVs required in the warehouse to fulfill all the task given by simulation software. Anylogic software is being used to build a simulation model and analyzed the system performance. The obtained results was the optimization of MTS for factory logistic which produce effective material handling system and creates the systematic handling system in warehouse. By manipulating number of AGV, system throughput and cycle time being observed. Data obtained from simulation being compared as number of AGV had change. Minitab 17 software are used to create statistical graph in 2D and 3D surface in order to analyze and evaluate the results. 2015 Thesis http://eprints.utem.edu.my/id/eprint/15866/ http://eprints.utem.edu.my/id/eprint/15866/1/Nor%20Rashidah%20Bte%20Mohamad.pdf text en public http://eprints.utem.edu.my/id/eprint/15866/2/Optimization%20of%20material%20transportation%20system%20for%20factory%20logistic.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=96079 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Manufacturing Engineering Md Fauadi, Muhammad Hafidz Fazli 1. Alain, C. & Kang, H., (2004). Research on Dynamic Dispatching Rule for Semiconductor Assembly Production Line.i : Al-. , 47. 2. Alden, J.M. et al., (2006). General Motors Increases Its Production Throughput. , 36(1), pp.6–25. 3. Baines, T. S., & Harrison, D. K. (1999). An opportunity for system dynamics in manufacturing system modelling. Production Planning & Control, 10(6), 542–552. doi:10.1080/095372899232830 4. Benincasa, a. X., Morandin, O. J., & Kato, E. R. R. (2003). Reactive fuzzy dispatching rule for automated guided vehicles. SMC’03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483), 5, 4375–4380. doi:10.1109/ICSMC.2003.1245673 5. Chen, H. Z., Chang, L., Yuan, R., & Jun, Z. (2012). Research on dynamic dispatching rule for semiconductor assembly production line. IEEE International Conference on Industrial Engineering and Engineering Management, 1(2011), 493–497. doi:10.1109/IEEM.2012.6837788 6. Chincholkar, M.M. et al., (2004). Estimating Manufacturing Cycle Time and Throughput in Flow Shops with Process Drift and Inspection. 7. Groover, M., (2008). Material Transport System. In: Automation, Production Systems and Computer Intergrated Manufacturing. 3rd ed. New Jersey: Pearson Education Inc,, pp. 8. Leonardo das Dores Cardoso, Joao Jose de Assis Rangel, and Patrick Junior Teixeira Bastos, (2013), (1994), Discrete Event Simulation for integrated 9. Heilala, J., (1999). Use of Simulation in Manufacturing and Logistics system planning. Finland, s.n. 10. Lane, (2011) Profiles in Operations Research.pdf.[online] Available at: http://jsterman.scripts.mit.edu/docs/. 298-304. 11. Hoshino, S., & Ota, J. (2004). Comparison of an AGV Transportation System by Using the Queuing Network Theory, 3785–3790. 12. Ikkai, Y., Ohkawa, T., & Komoda, N. (1993). Dispatching rule exchangeable optimization planning system. Proceedings of IEEE 2nd International Workshop on Emerging Technologies and Factory Automation (ETFA ’93). doi:10.1109/ETFA.1993.396419 13. Liu, S. (2012). Optimization and scheduling of AGV in automated warehouse system based on immune algorithm, 1492–1495. doi:10.1049/cp.2012.1264 14. Lane, D. C., & Sterman, J. D. (2011). Profiles in Operations Research: Jay Wright Forrester. Profiles in Operations Research: Pioneers and Innovators, 363–386. Retrieved from 15. Mehdi, K. & Venkatesh, K., (1993). Flexible manufacturing Systems: An Overview. IJOPM, p. 26. 16. Nishi, T., & Maeno, R. (2010). Petri net decomposition approach to optimization of route planning problems for AGV systems. IEEE Transactions on Automation Science and Engineering, 7(3), 523–537. doi:10.1109/TASE.2010.2043096 17. Pang, Y. & Lodewijks, G., (2012). Agent based intelligent monitoring in large scale continuous material transport.. Netherlands, IEEE, pp. 1-6. 18. Schulze, L., & Wullner, A. (2006). The Approach of Automated Guided Vehicle Systems. 2006 IEEE International Conference on Service Operations and Logistics, and Informatics, 522–527. doi:10.1109/SOLI.2006.328941 19. Sai-nan, L. (2013). optimization problem for avg in automated warehouse system" - Cerca con Google, (2), 1640–1642. 20. Steve, M. K. & Thom, H. J., (1985). Developing Control Rules for an AGV using Markov Decision Processes. Ft Lauderdale, FL, pp. 1817-1821. 21. Scholz-Reiter, B., Heger, J., & Hildebrandt, T. (2010). Gaussian processes for dispatching rule selection in production scheduling: Comparison of learning techniques. Proceedings – IEEE International Conference on Data Mining, ICDM, 631–638. doi:10.1109/ICDMW.2010.19 22. Shu Chu Liu. (1998). Moves in a Flexible Manufacturing System. 23. Tompkins, J. et al., (1996). Facilities Planning. 2nd ed. New York: John Wiley & Sons 24. Warangal, M. S. (2011). The Role of Transportation in Logistic Chain, 1–10. 25. Yifei, T. Y. T., Junruo, C. J. C., Meihong, L. M. L., Xianxi, L. X. L., & Yali, F. Y. F. (2010). An estimate and simulation approach to determining the Automated Guided Vehicle fleet size in FMS. Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on, 9, 432–435. doi:10.1109/ICCSIT.2010.5565147 26. Yoshida, T., & Touzaki, H. (1999). A study on association among dispatching rules in manufacturing scheduling problems. 1999 7th IEEE International Conference on Emerging Technologies and Factory Automation. Proceedings ETFA ’99 (Cat. No.99TH8467), 2(2), 1355–1360. doi:10.1109/ETFA.1999.813146 27. Zhan, Y. D. (2011). Intelligent Coordination Steering Control of Automated Guided Vehicle.

Optimization of material transportation system for factory logistic

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