2022
DOI: 10.1109/lra.2022.3185778
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Fast Simulation-Based Order Sequence Optimization Assisted by Pre-Trained Bayesian Recurrent Neural Network

Abstract: This paper presents a fast optimization method for the picking order sequence of automated order picking systems in logistics warehouses. In this order sequencing problem (OSP), the fulfillment sequence of the given picking order set is determined to optimize the performance measures such as makespan and deadlock occurrence. Simulation is generally necessary to evaluate these measures for complex automated systems. However, their order sequence cannot be optimized quickly due to the long calculation time. It m… Show more

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Cited by 5 publications
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