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

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

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

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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

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

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

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

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

Experimental Equipment Arena simulation software package Computer

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

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

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

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

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

MTP performance Results similar for the Mean Tardiness of parts performance

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

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

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

Industries most impacted Companies producing multiple parts simultaneously Automotive Aerospace