3 EXPERIMENTAL RESULTS
Simulation analysis validates the quality of the operational policies provided by using IBMB. IBMB minimizes
total retrieval time and improves picker utilization across diverse and practical order picking situations.
Table 1 summarizes the results of varying the operational scenario and the batch size. Our batching
strategy constrains each batch to be less than or equal to the capacity of the cart or picking support vehicle,
i.e., a constant number of items and each batch packed as tightly as possible. Typically, the expected
number of picks per batch should be very close to the cart size, because the batches are packed optimally.
In a standard picking situation, we find that IBMB reduces 64.4~91.1% of picker blocking with, on average,
0.18~0.64 seconds of computational time per cycle (i.e., 5 batches) and that utilization improves by
2.14~6.98%. Specifically, the time blocked is 0.97~1.38% versus FCFS values of 4.75~5.05%. Hand-off
delay shows minor increases or decreases.
3 EXPERIMENTAL RESULTSSimulation analysis validates the quality of the operational policies provided by using IBMB. IBMB minimizestotal retrieval time and improves picker utilization across diverse and practical order picking situations.Table 1 summarizes the results of varying the operational scenario and the batch size. Our batchingstrategy constrains each batch to be less than or equal to the capacity of the cart or picking support vehicle,i.e., a constant number of items and each batch packed as tightly as possible. Typically, the expectednumber of picks per batch should be very close to the cart size, because the batches are packed optimally.In a standard picking situation, we find that IBMB reduces 64.4~91.1% of picker blocking with, on average,0.18~0.64 seconds of computational time per cycle (i.e., 5 batches) and that utilization improves by2.14~6.98%. Specifically, the time blocked is 0.97~1.38% versus FCFS values of 4.75~5.05%. Hand-offdelay shows minor increases or decreases.
การแปล กรุณารอสักครู่..