Improving picking performance at a large retailer warehouse by combining probabilistic simulation, optimization, and discrete-event simulation.

  • SCI-E
  • SSCI
作者: Amorim-Lopes, Mario;Guimaraes, Luis;Alves, Joao;Almada-Lobo, Bernardo
作者机构: Catolica Porto Business Sch, P-4169005 Porto, Portugal.
Univ Porto, Fac Engn, INESC TEC, P-4099002 Porto, Portugal.
LTP Labs, P-4149008 Porto, Portugal.
语种: 英文
关键词: warehouse design;storage assignment policies;picking performance;discrete-event simulation;mixed integer programming;simulation-optimization
期刊: INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
ISSN: 0969-6016
年: 2021
卷: 28
期: 2
页码: 687-715
摘要: Distribution warehouses are a critical part of supply chains, representing a nonnegligible share of the operating costs. This is especially true for unautomated, labor-intensive warehouses, partially due to time-consuming activities such as picking up items or traveling. Inventory categorization techniques, as well as zone storage assignment policies, may help in improving operations, but may also be short-sighted. This work presents a three-step methodology that uses probabilistic simulation, optimization, and event-based simulation (SOS) to analyze and experiment with layout and storage assignment policies to improve the picking performance. In the first stage, picking performance is estimated under different storage assignment policies and zone configurations using a probabilistic model. In the second stage, a mixed integer optimization model defines the overall warehouse layout by selecting the configuration and storage assignment policy for each zone. Finally, the optimized layout solution is tested under demand uncertainty in the third, final simulation phase, through a discrete-event simulation model. The SOS methodology was validated with three months of operational data from a large retailer's warehouse, successfully illustrating how it may be successfully used for improving the performance of a distribution warehouse.

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Improving picking performance at a large retailer warehouse by combining probabilistic simulation, optimization, and discrete-event simulation.
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