Throughput models for a dual-bay VLM order picking system under different configurations

DOIhttps://doi.org/10.1108/IMDS-11-2018-0518
Date08 July 2019
Pages1268-1288
Published date08 July 2019
AuthorFabio Sgarbossa,Martina Calzavara,Alessandro Persona
Subject MatterInformation & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems
Throughput models for a dual-bay
VLM order picking system under
different configurations
Fabio Sgarbossa
Department of Management and Engineering,
University of Padua, Padova, Italy and
Department of Mechanical and Industrial Engineering,
Norwegian University of Science and Technology, Trondheim, Norway, and
Martina Calzavara and Alessandro Persona
Department of Management and Engineering, University of Padua, Padova, Italy
Abstract
Purpose Vertical lift module (VLM) is a parts-to-picker system for order picking of small products, which
are stored into two columns of trays served by a lifting crane. A dual-bay VLM order picking (dual-bay VLM-
OP) system is a particular solution where the operator works in parallel with the crane, allowing higher
throughput performance. The purpose of this paper is to define models for different operating configurations
able to improve the total throughput of the dual-bay VLM-OP system.
Design/methodology/approach Analytical models are developed to estimate the throughput of a dual-
bay VLM-OP. A deep evaluation has been carried out, considering different storage assignment policies and
the sequencing retrieval of trays.
Findings A more accurate estimation of the throughput is demonstrated, compared to the application of
previous models. Some use guidelines for practitioners and academics are derived from the analysis based on
real data.
Originality/value Differing from previous contributions, these models include the acceleration/
deceleration of the crane and the probability of storage and retrieve of each single tray. Thispermits to apply
these models to different storage assignment policies and to suggest when these policies can be profitably
applied. They can also model the sequencing retrieval of trays.
Keywords Automated warehouses, Vertical lift module (VLM), Order picking, Analytical modelling,
Throughput performance analysis
Paper type Research paper
Nomenclature
Hdual-bay VLM height
vdual-bay VLM
crane velocity
adual-bay VLM crane
acceleration/deceleration
t
p/d
delay time to pick up or
deposit a tray
i,jtray indices (1 M)
Mtotal number of dual-bay
VLM trays
p
i
,p
j
probability of extracting
tray i(tray j)
h
i
,h
j
storageheight of tray i(tray j)
t
ij
crane travel time from tray
location ito tray location j
t
Ai
,t
Bi
cranetraveltimefrompicking
bayA(B)totraylocationi
t
jA
,t
jB
crane travel time from tray
location jto picking bay A (B)
Ltotal number of lines
per order
l
i
number of lines of the order
in tray i
p
T
average picking time for
one SKU
SA storage assignment policy
(RS ¼random storage,
CBS ¼class-based storage)
E[CT
SA
]expected cycle time under
the SA policy
Industrial Management & Data
Systems
Vol. 119 No. 6, 2019
pp. 1268-1288
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-11-2018-0518
Received 22 November 2018
Revised 26 March 2019
Accepted 16 April 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
1268
IMDS
119,6
E[DC
SA
]expected dual command
time under SA policy
S[DC
SA
]standard deviation of the
dual command time under
SA policy
X
SA
factor for occupancyproblem
VLMU average utilisation of
dual-bay VLM
( from simulation)
PU average utilisation of
operator (from simulation)
E[R
SA
(L,M)] expected number of trays
delivered for an order of L
lines in case of dual-bay
VLM with Mtrays
E[DC
SA
(L,M)] expected dual command for
an order of Llines in case of
dual-bay VLM with Mtrays
1. Introduction and background
Order picking is one of the most time- and cost-consuming activities in a warehouse, often
requiring the presence of human operators, who travel w ithin the warehouse aisles to retrieve
the items that are needed to fulfil the various orders of the customers (De Koster et al., 2007).
Consequently, the travelling activity can represent up to 50 per cent of the total picking time, as
demonstrated by Tompkins et al. (2010). Moreover, this aspect can become even more critical
when also small objects are stored in pallets, occupying a large amount of space (Azzi et al.,
2014; Battini et al., 2014; Bartholdi and Hackman, 2017; Battini et al., 2018). Some examples of
small objects order picking systems are the ones for general e-commerce products, personal
care and home care products, drugs and other healthcare products, small electronic devices etc.
In the case of a traditional picking low-level picker-to-parts warehouse, the items are
stored on pallets that are positioned on the lower stocking locations of the shelves. The
pickers usually use electric pallet trucks to move along the aisles and to transport one or
more mixed pallets, composed of the items collected during their order picking activity
(Battini et al., 2018). As per the authorsexperience, and also confirmed by relevant scientific
contributions (Bartholdi and Hackman, 2017; Franzke et al., 2017), in this type of warehouse,
the average time per order line is typically from 60 to 120 s/line. The main part is due to
travelling and searching activities (Tompkins et al., 2010; Dijkstra and Roodbergen, 2017).
Moreover, the picking activity could be severe from an ergonomic perspective, especially
when the operators are picking the last (so, farthest) items from the pallet (Calzavara et al.,
2017; Calzavara et al., 2018).
An alternative picker-to-parts solution for small objects picking is the creation of a
dedicated storage area with small racks where the products are stocked in cartons or boxes
(Battini et al., 2018). The main benefit is reducing the total space needed and, hence, the
distances travelled, leading to a higher system throughput (Choe and Sharp, 1991; Tompkins
and Smith, 1998; Caputo and Pelagagge, 2006; Battini et al., 2015a). Other solutions are the
parts-to-pickerones, where an automated systembrings products to the operator, whoworks
in a fixed picking station (Choe and Sharp, 1991; Tompkins and Smith, 1998).
The present paper focuses on a parts-to-picker solution based on vertical lift modules,
also called VLMs. A VLM consists of a storage column in which small products are stored in
extractable trays. These trays are inserted and extracted by a powered device, which travels
vertically between the front and the rear shelving of the column. It delivers and retrieves the
specific tray in front of the picker, in the so-called picking bay, where the operator processes
the picking order. The moving device is guided by an automated control system, which is
usually interfaced with a software system, to set the correct order of trays retrieval. There
are different alternatives of this automated system developed by various manufacturers
(such as Modula, Spacesaver, Kardexremstar etc.); however, the most implemented one is
like the one just described, in which there is one crane for each couple of storage columns,
with a picking bay and an operator serving at least one VLM.
1269
A dual-bay
VLM order
picking system

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