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The performance of load-selection rules and pickup-dispatching rules for multiple-load AGVs

The performance of load-selection rules and pickup-dispatching rules for multiple-load AGVs

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The performance of load-selection rules and pickup-dispatching rules for multiple-load AGVs

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  1. The performance of load-selection rules and pickup-dispatching rules for multiple-load AGVs Ying-Chin Ho, Hao-Cheng Liu Nathan Christensen November 9, 2009

  2. Function Address the problem of load selection and pickup-dispatching of multiple-load AGVs Understand the performance of load selection rules Study the effects that load selection and machine selection rules have on each other

  3. Importance AGVs give routing flexibility to the FMS system, efficiency is important Many studies have been done for single load AGVs but multiple load AGVs are more efficient and becoming more popular Multiple load AGV’s allow for a reduction in fleet size, over the criteria shown in class Multiple load AGV’s give better throughput performance

  4. References [1] Kim CW, Tanchoco JMA, Koo PH. AGV dispatching based on workload balancing. International Journal of Production Research 1999;37(17):405366. [2] Hirao S, Tamaki M, Ohno K. Optimal dispatching control of an AGV in a JIT production system. Production Planning and Control 2002;13(8):74653. [3] Matthias L, Grunow M, Günther H-O. Deadlock handling for real-time control of AGVs at automated container terminals. OR Spectrum 2006;28(4):63157. [4] Kim KH, Jeon SM, Ryu KR. Deadlock prevention for automated guided vehicles in automated container terminals. OR Spectrum 2006;28(4):65979. [5] Mahadevan B, Narendran TT. Estimation of number of AGVs for an FMS: An analytical model. International Journal of Production Research 1993;31(7): 165570. [6] Rajotia S, Shanker K, Batra JL. Determination of optimal AGV fleet size for an FMS. International Journal of Production Research 1998;36(5):117798. [7] Arifin R, Egbelu PJ. Determination of vehicle requirements in automated guided vehicle systems: A statistical approach. Production Planning and Control 2000;11(3):25870. [8] Sinriech D, Tanchoco JMA. An introduction to the segmented flow approach for discrete material flow systems. International Journal of Production Research 1995;33(12):3381410. [9] Ko KC, Egbelu PJ. Unidirectional AGV guidepath network design: A heuristic algorithm. International Journal of Production Research 2003;41(10):232543. [10] Seo Y, Lee C, Moon C. Tabu search algorithm for flexible flow path design of unidirectional automated-guided vehicle systems. OR Spectrum 2007;29(3): 47187. [11] Nayyar P, Khator KS. Operational control of multi-load vehicles in an automated guided vehicle system. Computers and Industrial Engineering 1993;25(14):5036. [12] Lee J, Tangjarukij M, Zhu Z. Load selection of automated guided vehicles in flexible manufacturing systems. International Journal of Production Research 1996;34(12):3388400. [13] Lee J, Srisawat T. Effect of manufacturing system constructs on pick-up and drop-off strategies of multiple-load AGVs. International Journal of Production Research 2006;44(4):65373. [14] Bilge U, Tanchoco JMA. AGV systems with multi-load carriers: Basic issues and potential benefits. Journal of Manufacturing Systems 1997;16(3):15971. [15] Ho YC, Shaw HC. The performance of multiple-load AGV systems under different guide path configurations and vehicle control strategies. International Journal of Manufacturing Technology and Management 2000;1(23):21831. [16] Ho YC, Hsieh PF. A machine-to-loop assignment and layout design methodology for tandem AGV systems with multiple-load vehicles. International Journal of Production Research 2004;42(4):80132. [17] Grunow M, Günther H-O, Lehmann M. Dispatching multi-load AGVs in highly automated seaport container terminals. OR Spectrum 2004;26(2):21135. [18] Ho YC, Chien SH. A simulation study on the performance of task-determination rules and delivery-dispatching rules for multiple-load AGVs. International Journal of Production Research 2006;44(20):4193222. [19] Ho YC, Liu HC. A simulation study on the performance of pickup-dispatching rules for multiple-load AGVs. Computers and Industrial Engineering 2006; 51(3):44563. [20] Ozden M. A simulation study of multiple-load-carrying automated guided vehicles in a flexible manufacturing system. International Journal of Production Research 1988;26(8):135366. [21] Egbelu PJ, Tanchoco JMA. Characterization of automatic guided vehicle dispatching rules. International Journal of Production Research 1984;22(3): 35974. [22] Law AM, Kelton WD. Simulation modeling and analysis. Boston: McGraw-Hill; 2000. [23] Rockwell Automation. Arena user's guide. Milwaukee: Rockwell Software; 2004.

  5. Relation to Technical Area Bottlenecking should not occur in the transport system Decreases in the number of AGVs increases productivity and efficiency Machine and Load selection criteria are essential for efficient multiple load AGV system performance

  6. Design Create a computer model to simulate factory floor Determine the delivery or pick up point Determine the loads to pick up

  7. Algorithms Pickup Dispatching rules (12) Longest Time in the System (LTS) Visit the pickup point containing the load with the longest time in the system Longest Average Time in the System (LATS) Visit the pickup point whose loads have the greatest average time in the system Greatest Waiting Time in Queue (GWTQ) Visit the pickup point containing the load with the greatest waiting time in the pickup point's queue Greatest Average Waiting Time in Queue (GAWTQ) Visit the pickup point that has loads with the greatest average waiting time in the pickup point's queue Earliest Due Time (EDT) Visit the pickup point containing the load with the earliest due time Earliest Average Due Time (EADT) Visit the pickup point whose loads have the smallest average due time

  8. Pickup Dispatching rules continued Smallest Remaining Processing Time (SRPT) Visit the pickup point containing the load with the smallest remaining processing time Smallest Average Remaining Processing Time (SARPT) Visit the pickup point containing loads with the smallest average remaining processing time Smallest Slack Time (SST) Visit the pickup point containing the load with the smallest slack time Smallest Average Slack Time (SAST) Visit the pickup point that has loads with the smallest average slack time Shortest Travel Distance Rule (STD) Visit the nearest pickup point Greatest Queue Length (GQL) Visit the pickup point whose queue length is the greatest, i.e. has the greatest number of loads waiting in the queue

  9. Load-selection rules (6) • Longest Time in the System First (LTSF) Pick up the load that has the greatest time in the system from LIQ • Greatest Waiting Time in Queue First (GWTQF) Pick up the load that has the greatest waiting time in queue from LIQ • Earliest Due Time First (EDTF) Pick up the load with the earliest due time from LIQ • Smallest Remaining Processing Time First (SRPTF) Pick up the load with the smallest remaining processing time from LIQ • Smallest Slack Time First (SSTF) Pick up the load with the smallest slack time from LIQ • Identical Destination First (IDF) based on the similarity between the destinations of various loads. If the IDF rule is used, the load with a destination identical to the next destination of any current load is selected

  10. Experimental Equipment Arena simulation software package Computer

  11. Design Principles DTF, SD and NV rules apply from earlier studies Assume one way traffic AGV always arrives at delivery point before pickup point 20 simulations of each principle 30,000 minute simulation 3000 min startup Number of WIP 150

  12. Design Principles Continued • Six different part types • Factory floor layout shown • Study through put and Mean Tardiness of Parts

  13. Experimental data • Using Anova and Duncan tests the data was analyzed • The main effects and two way interactions were shown to have significance of less than five percent

  14. Data IDF rule shown to be ideal with most pickup dispatching rules Due to the increased number of parts dropped off at any given location

  15. Data GQL shown to be the most efficient load selection Rule SAST shown to be worst

  16. MTP performance Results similar for the Mean Tardiness of parts performance

  17. Correlation of results with models Computer model assumed accurate with actual Manufacturing system No actual experimental data were taken because of the costs involved

  18. Industrial Use • Companies With multiple load AGV systems already in place • Decreased initial startup cost compared to single load AGV systems • Increased production

  19. Technological advancement • Multiple load AGVs are shown to be more efficient than single load AGVs • Correct algorithm selection allows for highest efficiency

  20. Industries most impacted • Companies producing multiple parts simultaneously • Automotive • Aerospace