Modeling and Simulation of a Serial Production Line with Constant Work-In-Process
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Modeling and Simulation of a Serial Production Line with Constant Work-In-Process

Authors: Mehmet Savsar

Abstract:

This paper presents a model for an unreliable production line, which is operated according to demand with constant work-in-process (CONWIP). A simulation model is developed based on the discrete model and several case problems are analyzed using the model. The model is utilized to optimize storage space capacities at intermediate stages and the number of kanbans at the last stage, which is used to trigger the production at the first stage. Furthermore, effects of several line parameters on production rate are analyzed using design of experiments.

Keywords: Production line simulator, Push-pull system, JIT system, Constant WIP, Machine failures.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080470

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References:


[1] Chu, C. and Shih, W. "Simulation Studies in JIT Production", International Journal of Production Research, 30 (11), 1992, pp. 2573- 2586.
[2] Fukukawa, T. and Hong S.C., "The Determination of Optimal Number of Kanbans in a Just-In-Time Production System", Computers Industrial Engineering, 24 (4), 1993, pp. 551-559.
[3] Savsar, M. and Aljawini, A., "Simulation Analysis of Just-In-Time Production Systems", International Journal of Production Economics, 42, 1995, pp. 67-78.
[4] Savsar, M., "Effects of Kanban Withdrawal Policies and Other Factors on the Performance of JIT Systems: A Simulation Study", Int. Journal of Prod. Res., 34 (10), 1996, pp. 2879-2899.
[5] Savsar, M., "Simulation Analysis of a Push-Pull System for an Electronic Assembly Line", Int. Journal of Prod. Economics, 51, 1997, pp. 205-214.
[6] Savsar, M. and Choueiki, H. M., "A Neural Network Procedure for Kanban Allocation in JIT Production Control Systems", Int. Journal of Prod. Research, 38 (14), 2000, pp.3247-3265.
[7] Olhager, J. and Ostlung, B., "An Integrated Push-Pull Manufacturing Strategy" European Journal of Operational Research, 45, 1990, pp. 135- 142.
[8] Hodgson, T.J. and Wang, D., "Optimal Hybrid Push/Pull Control Strategies for Parallel Multistage System: Part II", International Journal of Production Research, 29 (7), 1991, pp. 1453-1460.
[9] Wang, H. and Xu, C., "Hybrid Push/Pull Production Control Strategy Simulation and its Applications", Production Planning and Control, 8, 1997, pp. 142-151.
[10] Beamon, B.M. and Bermund, J.M., "A hybrid push-pull Ccontrol algorithm for multi-stage, multi-line production systems", Production Planning & Control,11(4), 2000, pp. 349-356.
[11] Duri, C., Frein, Y., and Dimascolo, M., "Comparison among three pull control policies: Kanban, Base Stock and Generalized Kanban", Annals of Operations Research, 93(1), 2000, pp. 41-47.
[12] Hillier, F.S. and So, K. C., "The Effect of the Coefficient of Variation of Operation Times on the Allocation of Storage Space in Production Line System", IIE Transactions, (23), 1991, pp. 198-206.
[13] Hillier, F.S., So, K. C., and Boling, R. W., "Notes: Toward Characterizing the Optimal Allocation of Storage Space in Production Line Systems with Variable Processing Times", Management Sci. 39(1), 1993, pp. 126-133.
[14] Papadopoulos, H. T. and Heavey, C., "Queuing Theory in Manufacturing Systems Analysis and Design: A Classification of Models for Production and Transfer Lines", European Journal of Operational Research, (92), 1996, pp. 1-27.
[15] Papadopoulos, H. T., and Vouros, G. A., "A Model Management System (MMS) for the Design and Operation of Production Lines", Int. Journal of Production Research, 35(8), 1996, 2213-2236.
[16] Powel, S. G. and Pyke, D. F., "Allocation of buffers to serial production lines with bottlenecks" IIE Transactions, 28, 1996, pp.18-29.
[17] Vouros, G. A. and Papadopoulos, H.T., "Buffer Allocation in Unreliable Production Lines Using a Knowledge Based System", Computers & Operations Research, 25(12), 1996, pp. 1055-1067.
[18] Vouros, G. A., Vidalis, M. I., and Papadopoulos, H. T., "A Heuristic Algorithm for Buffer Allocation in Unreliable Production Lines", International Journal of Quantitative Methods, 6(1), 2000, pp. 23-43.
[19] Spinellis, D.D. and Papadopoulos, C.T., "Stochastic Algorithms for Buffer Allocation in Reliable Production Lines", Mathematical Problems in Engineering, 5, 2000a, pp. 441-458.
[20] Spinellis, D.D. and Papadopoulos, C.T., "A Simulated Annealing Approach for Buffer Allocation in Reliable Production Lines", Annals of Operations Research, 93, 2000b, pp. 373-384.
[21] Gershwin, S.B. and Schor, J.E., "Efficient algorithms for buffer space allocation", Annals of Operations Research, 93, 2000, pp. 117-144.
[22] Savsar, M. and Youssef, A. S., "An Integrated Simulation-Neural Network Meta Model Application in Designing Production Flow Lines", WSEAS Transactions on Electronics, 2 (1), 2004, pp. 366-371.
[23] Savsar, M. "Effects of Maintenance Policies on the Productivity of Flexible Manufacturing Cells", OMEGA, Vol. 34, 2006, pp. 274-282.