Meng, Hsien-Mi

Permana, Hendra

Ting, Ching-Jung

English

Georgia Southern University: Digital Commons@Georgia Southern

16th International Material Handling Research ColloquiumDresden, Saxony, Germany, June 20‐23, 202321 Decision Rules for the Robotic Mobile Fulfillment System of A PCB Assembly Factory Warehouse Ching‐Jung Tinga, Hendra Permanab and Hsien‐Mi MengcaYuan Ze University, Taiwan bMinistry of Industry, IndonesiacGigabyte Technology Co., Ltd., Taiwan Introduction ResultsAcknowledgementThis research was funded by the National Council of Science and Technology Grant (NSC 111-2221-E-155-030). The authors would like to thank Yuan Ze University and Department of IndustrialEngineering and Management for providing the traveling expense to participate this ColloquiumSurface mount technology (SMT) becomes an important method in PCB assembly. For aproduction order, operators manually pick the required components rely on the paper-baseddocument of the PCB BOM by walking in the warehouse. This pickers-to-goods operation takes longtime to pick the required components for a production order and could yield picking error and moretime spent reviewing the picking order.Robotic mobile fulfillment system (RMFS) is a popular goods-to-pickers system for e-commercefulfillment center in recent years. To the best of our knowledge, there is limited RMFS application infactory warehouse. The case company does not really know the decision rules used in the system.This research will collect data from the warehouse and construct a simulation model and evaluatedifferent decision rules for interdependent sub-problems.Objectives• Define the decision rules of the studied operational problems in RMFS• Observe and collect data from the current RMFS• Construct the simulation model of the RMFS based on RAWSim-O• Analyze the performance for different rule configurations• Provide good rule configurationsMethodologyThe open source simulation model RAWSim-O developed by Merschformann et al. (2018) isadapted and new functions are added for the designed rules in this research. The decision rules foreach problem are listed in table 1. 96 rule configurations are tested and compared.Decision Problems Proposed RulesPick Order Selection (POS)0: Random (POS-R)1: Largest Quantity (POS-H)2: Index Number (POS-I)3: Pod-Match (POS-P)Pick Pod Selection (PPS)0: Random (PPS-R)1: Nearest (PPS-N)2: Largest Components Qty (PPS-C)3: Largest Reels Qty (PPS-Q)Pick Bot Selection (PBS)0: Random (PBS-R)1: Nearest (PBS-N)Pod Storage Assignment (PSA)0: Random (PBS-R)1: Nearest (PBS-N)2: Fixed (PBS-F)Table 1. Decision rules for each operational problem We consider four interdependent operational problems:pick order selection (POS), pick pod selection (PPS), pickbot selection (PBS), and pod storage assignment (PSA).Assumptions:1. AGV breakdown does not occur andbattery charged in not considered.2. The FAR path planning is used.3. There are up to 9 AGVs used in thesystem.4. At most three picking stations can beused.5. Picking time is collected from the realdata (constant).6. The reel selected from the pod isbased on the rule.Table 2 presents the parameter settingsin the simulation model. There are 216pods stored in the warehouse. Figure 1shows the factory warehouse layout ofthe case company.Figure 1. Factory warehouse layoutObject Parameter ValuePodCapacity 2880 ReelsSlot per pod 144 SlotsReel per slot 20 reelsRobotAcceleration/deceleration 2 M/Sec2Top Speed 2 M/SecLoading pod time 3 SecUnloading pod time 3 SecRotating pod times 3 Sec/3600StationPicking time 15 Sec/reelMaximum Queueing Bots5 Bots/stationTable 2. Parameter settings Two collected orders are simulated (140 SKUs/257,629 components and 143 SKUs/774,417components). We compare eight key performance indicators: makespan (MS) in seconds, bottraveling distance (BDT) in meters, bot utilization (UT), station utilization (ST), number of pod trips(PT), number of reel handled (RH), Pile-on (PO), and inventory reduction (IR) in percentage. Table 3shows the results for the first order simulation. POS-I achieves the smallest makespan, while POS-Pprovides the largest inventory reduction (# of reels handled). The pod order selection and pick podselection problems affect the makespan and inventory reduction as shown in table 4.Table 5 presents the results for the larger assembly order simulation. In this order, PPS-C yieldsthe smallest makespan, and POS-P has the largest inventory reduction (# of reels handled). Figure 2shows the number of reels handled for different decision rules. We further analyze the multiple factorANOVA on the makespan for different rules of operational problems. Figure 3 presents interaction oftwo operational problems on the makespan. Only POS and PPS show the difference among differentrule combinations. The POS-I and PPS-C rule combination provides the smallest makespan.RuleMS (Sec)BDT (m)BU %SU %PT RH POIR %POS-R 4305.8 4540.1 70.1 91.5 70.5 263.5 3.7 6.1POS-H 3975.6 4519.0 70.4 90.9 70.4 241.6 3.4 5.5POS-I 3803.5 4393.2 70.3 90.9 67.9 230.6 3.4 5.3POS-P 6840.8 3513.6 67.4 95.7 53.6 436.2 8.2 10.0PPS-R 5077.9 4511.1 69.4 92.9 66.0 315.8 5.0 7.2PPS-N 4715.3 4004.7 69.5 92.1 68.9 291.4 4.5 6.7PPS-C 3974.0 4131.3 70.1 90.8 61.2 243.7 4.2 5.6PPS-Q 5158.5 4318.9 69.2 93.0 66.2 321.2 5.0 7.4PBS-R 4736.2 4255.0 69.5 92.2 65.7 293.2 4.7 6.7PBS-N 4726.7 4228.0 69.7 92.3 65.5 292.9 4.7 6.7PSA-R 4735.2 4230.5 69.6 92.4 65.4 293.6 4.7 6.7PSA-N 4752.9 4232.6 69.4 92.1 65.7 294.1 4.7 6.8PSA-F 4706.3 4261.4 69.6 92.2 65.6 291.4 4.7 6.7Conclusions• Decision rules in Pick pod selection and pick order selection significantly affect the makespan andinventory reduction.• The largest component quantity in pick pod selection problem can achieve the smallest makespan,while largest quantity in pick order selection and largest reel quantity in pick pod selection willprovide the largest inventory reduction.• Number of bots and picking stations will affect the KPIs. For a single picking station, increase thenumber of AGVs in the system cannot reduce the makespan.Rule MS BDT BU SU PT RH PO IRPOS-R 6011.7 5356.5 69.1 93.1 88.3 373.6 4.2 1.0POS-H 5999.3 5692.8 69.5 92.5 93.5 371.5 3.9 1.0POS-I 6077.7 6064.5 70.0 91.6 103.3 374.7 3.9 1.0POS-P 9481.1 4063.0 66.9 96.6 64.5 610.6 9.5 1.7PPS-R 7031.4 6116.9 69.3 93.4 94.5 440.1 5.2 1.2PPS-N 6890.1 5215.6 69.1 93.3 96.9 430.6 5.1 1.2PPS-C 5011.9 4496.2 69.1 92.0 68.5 312.1 4.7 0.9PPS-Q 8636.2 5348.1 68.0 95.0 89.6 547.6 6.5 1.5PBS-R 6893.7 5456.3 68.9 93.5 87.5 432.7 5.4 1.2PBS-N 6891.2 5132.1 68.9 93.4 87.3 432.5 5.4 1.2PSA-R 6894.7 5313.8 68.9 93.5 87.6 433.1 5.4 1.2PSA-N 6895.6 5240.0 68.8 93.3 87.2 432.4 5.4 1.2PSA-F 6887.0 5328.8 68.9 93.5 87.4 432.3 5.4 1.2Figure 2 Boxplot of number of reels handledTable 3. Results of different decision rules on first order Table 4. ANOVA of KPIs on first order Table 5. Results of different decision rules on second orderFigure 3 Multi factor ANOVA on makespanRule MS BDT PT RH IRPOS 0.000* 0.000 0.000 0.000 0.000PPS 0.000 0.000 0.000 0.000 0.000PBS 0.915 0.448 0.653 0.960 0.960PSA 0.912 0.731 0.919 0.926 0.926Table 6. ANOVA of KPIs on second order Rule MS BDT PT RH IRPOS 0.000* 0.000 0.000 0.000 0.000PPS 0.000 0.000 0.000 0.000 0.000PBS 0.986 0.000 0.888 0.985 0.985PSA 0.999 0.632 0.982 0.997 0.997

Decision Rules for the Robotic Mobile Fulfillment System of A PCB Assembly Factory Warehouse

https://digitalcommons.georgiasouthern.edu/cgi/viewcontent.cgi?article=1084&context=imhrc

Decision Rules for the Robotic Mobile Fulfillment System of A PCB Assembly Factory Warehouse

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