eポートフォリオ(ePortfolio)

The order batching method improves the productivity of bucket brigade order picking systems (OPSs) by picking a group of small-sized orders as one batch in a single trip. A bucket brigade OPS usually includes several non-identical pickers. The previous order batching model optimized the composition (i.e., orders) of batches by minimizing their completion time (i.e., the sum of the start time, pick time, walk time, and blocking delay time). However, due to the complexity of the bucket brigade OPSs, the previous model only developed formulas to estimate the start and pick time, which would cause the minimized completion time to be inaccurate, leading to a loss of efficiency in the OPSs. To avoid this problem, we propose the Balanced Batching Model for Bucket Brigade (BBMB) model with non-identical pickers to optimize the composition of batches while excluding errors in the start and picking time calculations. A bucket brigade OPS can achieve maximum productivity when the system is in a balanced status, and the balanced distribution of work content in the OPS directly contributes to the system's balance. To achieve this, the BBMB model balances the work content in the OPS by optimizing the composition of batches. Simulation experiments show that the BBMB model reduced the completion time of batches by 3.25 to 7.98% under different conditions of the OPS compared to the previous model.