An Integrated Approach for the Analysis of Manufacturing System States
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Abstract
With advancement in the manufacturing technology and rise in the purchasing ability, demand for newer products is increasing continuously. This is forcing manufacturing companies to persistently look for new techniques to improve the productivity of a manufacturing system and ensure optimum utilization of all the elements of a manufacturing system, including facility layout. Traditional research had viewed facility layout, material handling and productivity improvement as separate activities. Researchers depending on their area of specialization focused on either the production aspects of a company, the material handling aspects or facility layout. However, to ensure productivity, this study proposes a new theory to analyze the current state of the system with an integrated approach of production system and material handling system. In this study, the current state of the system is classified into three different states and a methodology is proposed to identify the current state of the system. This new theory can be used by manufacturers to identify appropriate strategies for improving productivity. The identification of the state of the system is necessary for effective improvement of the system.
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Published
August 4, 2017
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References
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Han, M., and McGinnis, L. F. (1989). Flow control in flexible manufacturing: minimization of stockout cost. International Journal of Production Research, 27(4), 701-715.
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Ishii, N., and Talavage, J. J. (1991). A transient –based real-time scheduling algorithm in FMS. International Journal of Production Research, 29(12), 2501-2520.
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Leus, R., & Herroelen, W. (2005). The complexity of machine scheduling for stability with a single disrupted job. Operations Research Letters, 33(2), 151-156.
Mahadevan, B., and Narendran, T. T. (1993). Estimation of number of AGVs for an FMS: an analytical model. The International Journal of Production Research, 31(7), 1655-1670.
Mukhopadhyay, S. K., Maiti, B., and Garg, S. (1991). Heuristic solution to the scheduling problems in flexible manufacturing system. The International Journal of Production Research, 29(10), 2003-2024.
O’Grady, P., and Lee, K. H. (1988). An intelligent cell control system for automated manufacturing. International Journal of Production Research, 26(5), 845-861.
Park, S., Raman, N., & Shaw, M. (1989). Heuristic learning for pattern directed scheduling in a flexible manufacturing system. Paper presented at the Proceedings of the Third ORSA/TIMS Conference on Flexible Manufacturing Systems.
Petrovic, D., & Duenas, A. (2006). A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions. Fuzzy sets and systems, 157(16), 2273-2285.
Qiu, L., Hsu, W. J., Huang, S. Y., and Wang, H. (2002). Scheduling and routing algorithms for AGVs: a survey. International Journal of Production Research, 40(3), 745-760.
Rajotia, S., Shanker, K., and Batra, J. L. (1998). Determining of optimal AGV fleet size for an FMS. International Journal of Production Research, 36(5), 1177-1198.
Ro, I. K., and Kim J. I. (1990). Multi-criteria operational control rules in flexible manufacturing systems (FMSs). International Journal of Production Research, 28(1), 47-63.
Sabuncuoglu, I., and Hommertzheim, D. L. (1992). Experimental investigation of FMS machine and AGV scheduling rules against the mean flow-time criterion. International Journal of Production Research, 30(7), 1617-1635.
Sinriech, D., and Tanchoco, J. M. A. (1992). An economic model for determining AGV fleet size. International Journal of Production Research, 30(6), 1255-1268.
Slomp, J., Gaalman, G., & Nawijn, W. (1988). Quasi on-line scheduling procedures for flexible manufacturing systems. The International Journal of Production Research, 26(4), 585-598.
Tanchoco, J. M. A., Egbelu, P. J., and Taghaboni, F. (1987). Determination of the total number of vehicles in an MHU-based material transport system. Material Flow, 4, 33-51.
Tompkins, J.A., White, J.A., Bozer, Y.A., & Tanchoco, J.M.A. (2010). Facilities Planning. Hoboken, NJ: John Wiley & Sons.
Um, I., Cheon, H., & Lee, H. (2009). The simulation design and analysis of a flexible manufacturing system with automated guided vehicle system. Journal of Manufacturing Systems, 28(4), 115-122.
Wu, S. D., and Wysk, R. A. (1989). An application of discrete-event simulation to on-line control and scheduling in flexible manufacturing. International Journal of Production Research, 27(9), 1603-1623.
Zandieh, M., & Adibi, M. A. (2010). Dynamic job shop scheduling using variable neighborhood search. International Journal of Production Research, 48(8), 2449-2458.
Akturk, M. S., and Yilmaz, H. (1996). Scheduling of automated guided vehicles in a decision making hierarchy. International Journal of Production Research, 34(2), 577-591.
Arifin, R., and Egbelu, P. J. (2000). Determination of vehicle requirements in automated guided vehicle systems: a statistical approach. Production Planning and Control, 11(3), 258-270.
Aytug, H., Barua, A., Lawley, M., & Uzsoy, R. (2003). Observations on the interactions among deadlock avoidance policies and dispatching rules in automated manufacturing systems. International Journal of Production Research, 41(1), 81-95.
Blazewicz, J., Eiselt, H. A., Finke, G., Laporte, G., and Weglarz, J. (1991). Scheduling tasks and vehicles in a flexible manufacturing system. The International Journal of Flexible Manufacturing Systems, 4(1), 5-16.
Chandra, J., and Talavage, J. (1991). Intelligent dispatching for flexible manufacturing. The International Journal of Production Research, 29(11), 2259-2278.
Chen, I. J., and Chung, C. H. (1991). Effects of loading and routeing decisions on performance of flexible manufacturing systems. International Journal of Production Research, 29(11), 2209-2225.
Co, H. C., Biermann, J. S., and Chen, S. K. (1990). A methodical approach to the flexible-manufacturing-system batching, loading and tool configuration problems. International Journal of Production Research, 28(12), 2171-2186.
Davis, W. J., and Jones, A. T. (1989). On-line concurrent simulation in production scheduling. Paper presented at the Third ORSA/TIMS Conference on Flexible Manufacturing Systems.
De Koster, R. B. M., Le-Anh, T., and Van der Meer, J. R. (2004). Testing and classifying vehicle dispatching rules in three real-world settings. Journal of Operations Management, 22(4), 369-386.
Denzler, D. R., and Boe, W. J. (1987). Experimental investigation of flexible manufacturing system scheduling decision rules. International Journal of Production Research, 25(7), 979-994.
Egbelu, P. J. (1993). Concurrent specification of unit load sizes and automated guided vehicle fleet size in manufacturing system. International Journal of Production Economics, 29(1), 49-64.
Egbelu, P. J. (1987). The use of non-simulation approaches in estimating vehicle requirements in an automated guided vehicle based transport system. Material Flow, 4(1), 17-32.
Han, M., and McGinnis, L. F. (1989). Flow control in flexible manufacturing: minimization of stockout cost. International Journal of Production Research, 27(4), 701-715.
Ho, Y. C., & Liu, H. C. (2009). The performance of load-selection rules and pickup-dispatching rules for multiple-load AGVs. Journal of Manufacturing Systems, 28(1), 1-10.
Ho, Y. C., Liu, H. C., & Yih, Y. (2012). A multiple-attribute method for concurrently solving the pickup-dispatching problem and the load-selection problem of multiple-load AGVs. Journal of Manufacturing Systems, 31(3), 288-300.
Ishii, N., and Talavage, J. J. (1991). A transient –based real-time scheduling algorithm in FMS. International Journal of Production Research, 29(12), 2501-2520.
Klei, C. M., and Kim, J. (1996). AGV dispatching. International Journal of Production Research, 34(1), 95-110.
Koo, P. H., and Jang, J. (2002). Vehicle travel time models for AGV systems under various dispatching rules. The International Journal of Flexible Manufacturing Systems, 14(3), 249-261.
Kumar, P., Tewari, N. K., and Singh, N. (1990). Joint consideration of grouping and loading problems in a flexible manufacturing system. International Journal of Production Research, 28(7), 1345-1356.
Langevin, A., Lauzon, D., and Riopel, D. (1996). Dispatching, routing, and scheduling of two automated guided vehicles in a flexible manufacturing system. The International Journal of Flexible Manufacturing Systems, 8(3), 247-262.
Lee, J., Choi, R. H., and Khaksar, M. (1990). Evaluation of automated guided vehicle systems by simulation. Computers and Industrial Engineering, 19(1), 318-321.
Lee, S. M., and Jung, H. J. (1989). A multi-objective production planning model in a flexible manufacturing environment. International Journal of Production Research, 27(11), 1981-1992.
Leus, R., & Herroelen, W. (2005). The complexity of machine scheduling for stability with a single disrupted job. Operations Research Letters, 33(2), 151-156.
Mahadevan, B., and Narendran, T. T. (1993). Estimation of number of AGVs for an FMS: an analytical model. The International Journal of Production Research, 31(7), 1655-1670.
Mukhopadhyay, S. K., Maiti, B., and Garg, S. (1991). Heuristic solution to the scheduling problems in flexible manufacturing system. The International Journal of Production Research, 29(10), 2003-2024.
O’Grady, P., and Lee, K. H. (1988). An intelligent cell control system for automated manufacturing. International Journal of Production Research, 26(5), 845-861.
Park, S., Raman, N., & Shaw, M. (1989). Heuristic learning for pattern directed scheduling in a flexible manufacturing system. Paper presented at the Proceedings of the Third ORSA/TIMS Conference on Flexible Manufacturing Systems.
Petrovic, D., & Duenas, A. (2006). A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions. Fuzzy sets and systems, 157(16), 2273-2285.
Qiu, L., Hsu, W. J., Huang, S. Y., and Wang, H. (2002). Scheduling and routing algorithms for AGVs: a survey. International Journal of Production Research, 40(3), 745-760.
Rajotia, S., Shanker, K., and Batra, J. L. (1998). Determining of optimal AGV fleet size for an FMS. International Journal of Production Research, 36(5), 1177-1198.
Ro, I. K., and Kim J. I. (1990). Multi-criteria operational control rules in flexible manufacturing systems (FMSs). International Journal of Production Research, 28(1), 47-63.
Sabuncuoglu, I., and Hommertzheim, D. L. (1992). Experimental investigation of FMS machine and AGV scheduling rules against the mean flow-time criterion. International Journal of Production Research, 30(7), 1617-1635.
Sinriech, D., and Tanchoco, J. M. A. (1992). An economic model for determining AGV fleet size. International Journal of Production Research, 30(6), 1255-1268.
Slomp, J., Gaalman, G., & Nawijn, W. (1988). Quasi on-line scheduling procedures for flexible manufacturing systems. The International Journal of Production Research, 26(4), 585-598.
Tanchoco, J. M. A., Egbelu, P. J., and Taghaboni, F. (1987). Determination of the total number of vehicles in an MHU-based material transport system. Material Flow, 4, 33-51.
Tompkins, J.A., White, J.A., Bozer, Y.A., & Tanchoco, J.M.A. (2010). Facilities Planning. Hoboken, NJ: John Wiley & Sons.
Um, I., Cheon, H., & Lee, H. (2009). The simulation design and analysis of a flexible manufacturing system with automated guided vehicle system. Journal of Manufacturing Systems, 28(4), 115-122.
Wu, S. D., and Wysk, R. A. (1989). An application of discrete-event simulation to on-line control and scheduling in flexible manufacturing. International Journal of Production Research, 27(9), 1603-1623.
Zandieh, M., & Adibi, M. A. (2010). Dynamic job shop scheduling using variable neighborhood search. International Journal of Production Research, 48(8), 2449-2458.
How to Cite
An Integrated Approach for the Analysis of Manufacturing System States. (2017). Industrial and Systems Engineering Review, 5(1), 61-72. https://doi.org/10.37266/ISER.2017v5i1.pp61-72
How to Cite
An Integrated Approach for the Analysis of Manufacturing System States. (2017). Industrial and Systems Engineering Review, 5(1), 61-72. https://doi.org/10.37266/ISER.2017v5i1.pp61-72
