An Integrated Approach for the Analysis of Manufacturing System States
DOI:
https://doi.org/10.37266/ISER.2017v5i1.pp61-72Abstract
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.References
Akella, R., Choong, Y., and Gershwin, S. (1984). Performance of hierarchical production scheduling policy. IEEE Transactions on Components, Hybrid, and Manufacturing Technology, CHMT-7(3), 225-240.
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.
Published
How to Cite
Issue
Section
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
The copyediting stage is intended to improve the flow, clarity, grammar, wording, and formatting of the article. It represents the last chance for the author to make any substantial changes to the text because the next stage is restricted to typos and formatting corrections. The file to be copyedited is in Word or .rtf format and therefore can easily be edited as a word processing document. The set of instructions displayed here proposes two approaches to copyediting. One is based on Microsoft Word's Track Changes feature and requires that the copy editor, editor, and author have access to this program. A second system, which is software independent, has been borrowed, with permission, from the Harvard Educational Review. The journal editor is in a position to modify these instructions, so suggestions can be made to improve the process for this journal.
