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Improving picking performance at a large retailer warehouse by combining probabilistic simulation, optimization, and discrete‐event simulation
Author(s) -
AmorimLopes Mário,
Guimarães Luís,
Alves João,
AlmadaLobo Bernardo
Publication year - 2021
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/itor.12852
Subject(s) - computer science , order picking , warehouse , discrete event simulation , probabilistic logic , event (particle physics) , operations research , mathematical optimization , reliability engineering , simulation , engineering , artificial intelligence , mathematics , physics , marketing , quantum mechanics , business
Abstract 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.