Implementing a hybrid simulation model for a Kanban-based material handling system

Robotics and Computer-Integrated Manufacturing

Volumn 24, Issue 5, 2008, Pages 635-646

  Hao, Qi ^a   Shen, Weiming ^a  

^a NATIONAL RESEARCH COUNCIL CANADA   (Canada)

Author keywords

Agent based systems; Discrete event simulation; JIT; Kanban; Material handling

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; AEROSPACE APPLICATIONS; COMPUTER NETWORKS; CURVE FITTING; FINANCIAL DATA PROCESSING; FORCE CONTROL; INDUSTRIAL APPLICATIONS; MATERIALS HANDLING EQUIPMENT; TECHNOLOGY;

AGENT-BASED TECHNOLOGIES; AND REAL TIME; APPLIED (CO); ASSEMBLY LINES; ASSEMBLY PLANTS; DISCRETE EVENTS; ELSEVIER (CO); HYBRID MODEL (HM); HYBRID SIMULATIONS; MANUFACTURING ENVIRONMENTS; MATERIAL HANDLING; MATERIAL HANDLING SYSTEM (MHS); MULTI PARADIGM MODELING; PLANT FLOORS; PROTOTYPE SYSTEMS; PROTOTYPING; SYSTEM OPTIMIZATIONS; SYSTEM PERFORMANCES;

MATERIALS HANDLING;

EID: 46749096878     PISSN: 07365845     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rcim.2007.09.012     Document Type: Article

References (38)

1. Lin GY, Solberg JJ. An agent-based flexible routing manufacturing control simulation system. In: Proceedings of the winter simulation conference, 1994, Lake Buena Vista, FL.
2. Martins K., and Lewandrowski U. Inventory safety stocks of Kanban control systems. Prod Planning Control 10 6 (1999) 520-529
3. Shen W., and Norrie D.H. Agent-based systems for intelligent manufacturing: a state-of-the-art survey. Knowl Inf Syst 1 2 (1999) 129-156
4. Shen W., Lang S., and Wang L. iShopFloor: an Internet-enabled agent-based intelligent shop floor. IEEE Trans. Syst Man Cybern C 35 3 (2005) 371-381
5. Wang L., Wong B., Shen W., and Lang S. Java3D enabled cyber workspace. Commun ACM 45 11 (2002) 45-49
6. Hao Q., Shen W., Zhang Z., Park S.W., and Lee J.K. Agent-based collaborative product development engineering: an industrial case study. Comput Ind 57 1 (2006) 26-38
7. Hao Q, Shen W, Wang S. Agent-oriented manufacturing scheduling services for enterprise collaboration. In: Proceedings of the SAE world congress, 2005, Detroit, MI.
8. Shen W., Li Y., Hao Q., Wang S., and Ghenniwa H. A service oriented integration framework for collaborative intelligent manufacturing. Robot Comput Integr Manuf 23 3 (2007) 315-325
9. Shen W., Hao Q., Yoon H.J., and Norrie D.H. Applications of agent systems in intelligent manufacturing: an updated review. Int J Adv Eng Inform 20 4 (2006) 415-431
10. XJ Techonologies. AnyLogic™, 〈http://www.xjtek.com/anylogic/〉.
11. Krishnamurthy A., Suri R., and Vernon M. Re-examining the performance of MRP and Kanban material control strategies for multi-product flexible manufacturing systems. Int J Flex Manuf Syst 16 2 (2004) 123-150
12. Köchel P., and Nieländer U. Kanban optimization by simulation and evolution. Produc Planning Control 13 8 (2002) 725-734
13. Gunasekaran A., Goyal S.K., Martinkainen T., and Yli-Olli P. Modeling and analysis of Just-In-Time manufacturing systems. Int J Produc Econ 32 (1993) 23-37
14. Berkley B.J. A review of Kanban production control research literature. Produc Oper Manage 1 4 (1992) 393-411
15. Gupta S.M., and Al-Turki Y.A.Y. The effect of sudden material handling system breakdown on the performance of a JIT system. Int J Produc Res 36 7 (1998) 1935-1960
16. Köchel P, Nieländer U, Sturm M. KASIMIR-object-oriented KAnban SIMulation Imaging Reality. Research report, Chemnitzer Informatik-Berichte CSR-01-03. Chemnitz, Germany: Chemnitz University of Technology; 2001.
17. Andijani A.A. A multi-criterion approach for Kanban allocations. Omega Int J Manage Sci 26 (1998) 483-493
18. Askin H., Mitwasi M.G., and Goldberg J.B. Determining the number of Kanbans in multi-item Just-in-Time systems. IIE Trans Des Manuf 25 (1993) 89-98
19. Bard J.F., and Golany B. Determining the number of Kanbans in a multiproduct, multistage production system. Int J Produc Res 29 (1991) 881-895
20. Berkley B.J., and Kiran A.S. A simulation study of sequencing rules in a Kanban-controlled flow shop. Decis Sci 22 (1991) 559-582
21. Hum S.H., and Lee C.K. JIT scheduling rules: a simulation evaluation. Omega Int J Manage Sci 26 (1998) 381-395
22. Berkley B.J. Effect of buffer capacity and sequencing rules on single-card Kanban system performance. Int J Produc Res 31 (1993) 2875-2893
23. Savsar M. Effects of Kanban withdrawal policies and other factors on the performance of JIT systems: a simulation study. Int J Produc Res 34 (1996) 2879-2899
24. Savsar M. Simulation analysis of maintenance policies in Just-in-Time production systems. Int J Oper Produc Manage 17 (1997) 256-266
25. Karaesmen F., and Dallery Y. A performance comparison of pull type control mechanisms for multi-stage production control. Int J Produc Econ 68 (2000) 59-71
26. Yavuz I.H., and Satir A. A Kanban-based simulation study of a mixed model Just-in-Time manufacturing line. Int J Produc Res 33 (1995) 1027-1048
27. Bonney M.C., Zhang Z., Head M.A., Tien C.C., and Barson R.J. Are push and pull systems really so different?. Int J Produc Econ 59 (1999) 53-64
28. Savsar M., and Choueiki H. A neural network procedure for Kanban allocation in JIT production control systems. Int J Produc Res 38 (2000) 3247-3265
29. Viharos Z.J., and Monostori L. Optimization of process chains and production plants by using a hybrid-, AI-, and simulation-based approach. Lect Notes Artif Intell 2070 (2001) 827-835
30. Nieländer U. Simulation optimization of Kanban systems using a non-standard genetic algorithm. In: Proceedings of the fourth ISIR research summer school on inventory modeling, School of Mathematics Sciences, 2000. p. 137-46.
31. Autug H., and Dogan C.A. A framework and a simulation generator for Kanban-controlled manufacturing systems. Comput Ind Eng 34 (1998) 337-350
32. KanbanSIM™, PMC. 〈http://www.pmcorp.com/kanbanSIM.sh™〉.
33. Modeling approaches, XJ Technologies. 〈http://www.xjtek.com/anylogic/modeling-approaches/〉.
34. Rizzoli AE. A collection of modeling and simulation resources on the Internet. December 2005 (last update). 〈http://www.idsia.ch/~andrea/simtools.h™l〉.
35. Law AM, McComas MG. How to select simulation software for manufacturing applications. Ind Eng 1992;24(7):29-32, 34-5.
36. Venkateswaran J., and Son Y.J. Hybrid system dynamic-discrete event simulation-based architecture for hierarchical production planning. Int J Produc Res 43 20 (2005) 4397-4429
37. Lee Y.H., Cho M.K., and Kim Y.B. A discrete-continuous combined modeling approach for supply chain simulation. Simulation 78 (2002) 321-329
38. Maione G., and Naso D. Modelling adaptive multi-agent manufacturing control with discrete event system formalism. Int J Syst Sci 35 10 (2004) 591-614