This study designs an efficient order picking system to reduce operating
costs and improve customer service. Generally, improving order picking
efficiency requires beginning from three concepts: (1) picking routing,
determining the picker travel route and picking sequence of all items
(2) storage assignment policy, namely developing a set of rules deciding
where different items will be located in a warehouse and (3) order batching,
a pre-operation conducted before the picking tour of an order.
This study discusses an order picking system involving multiple-pickers
and wide aisles. The picking routing assumably adopts the return policy.
This study develops a heuristic storage assignment policy which must simultaneously
consider travel distance and congestion. This investigation aims to improve
system efficiency via heuristic policy. This study uses a frequency-based
assignment policy as a comparison under different order bating conditions
to clarify the degree of improvement in heuristic policy. This study applies
the simulation method to compare the efficiency of each storage policy.
Heuristic policy development involves analyzing open tandem queue networks
with finite buffer. Exact queue analysis is generally complicated and
time-consuming. This thesis therefore uses the approximation method proposed
by Brandwajn and Jow (1988) to analyze the tandem queue network.
Rapid simulation software market expansion accounts thisopular type of tool.
Various discrete-event simulation languages oriented to the manufacturing environment
are used today. The eM-plant is the standard software for object-oriented, graphical
and integrated modeling for simulating and visualizing business processes. The
object-oriented simulation language operates by allowing the objects to pass
messages. Each message represents a request to an object to perform certain
This study creates an object-oriented simulation to implement a warehouse
order picking system. The research objective is to provide information
to the warehouse manager so that the manager more efficiently controls
present warehouse situations and decision making. Furthermore, the simulation
model can be web linked. The warehouse manager or all stores can search
or input data to the simulation model through internet, as presented in
In literature, several object-oriented methodologies and operations for
the manufacturing can be found (King and Kim, 1995; Macreie and Paul,
1995). The selection of the object-oriented approach to design order picking
system is mainly due to complex nature of problem (Usher, 1996).
A storage assignment policy or set of rules determining location of different
items is still one of the most effective means of enhancing warehouse
system performance and operational efficiency, in spite of software structure
differences (Goetschalckx and Ratliff, 1990). Storage policy rules are
based on demand, picking frequency, storage space, item characteristic,
||Simulation in web application
Previous literature often refers to storage space and picking frequency because they directly influence picking efficiency.
Heskett (1963) first proposed the COI (cube-per-order index) for this
reason. The COI assignment policy assigns items with a low storage space
to picking frequency ratio to the location nearest the I/O point. Kallina
and Lynn (1976) discussed some practical conclusions gathered from experience
in applying the COI rule to assist in warehouse layout. Additionally,
Caron et al. (1998) discussed correlation between the picks number
in a tour and the differently skewed COI-based ABC curves yielded for
traversal and return policies. In order to be more responsive to customers,
many companies have adopted a postponement strategy (Van Hoek, 2001) leading
to various value-adding activities that take place in the distribution
center and which have to be scheduled and integrated in the order-picking
Storage policy literature is quite limited, whether for the COI rule
or other rules. Schwarz et al. (1978) inspects automated warehouse
system performance in other assignment rules through a specific storage
policy that depends on item picking frequency. Gibson and Sharp (1992)
and Gray et al. (1992) obtain a common result that locates high
frequency close to the I/O point, enabling increased picking efficiency.
However, they do not provide a definite assignment policy.
Sahni and Gonzalez (1976) show that the storage assignment problem is
NP-complete, resulting in heuristic storage policy development. The frequency-based
policy is a part of heuristic storage policies. Items are assigned storage
locations according to their item picking frequency in the frequency-based
storage policy. High frequency items are generally located closest to
the I/O point. Riccardo et al. (2007) present a new integrated
approach to support the decision making process in optimising a picker
to part, forward-reserve, less than unit load order picking system. So
this study explains several definite storage assignment policies that
depend on item picking frequency (Jarvis and Mcdowell, 1991; Riccardo
et al., 2007).
WAREHOUSE DESCRIPTION AND ASSUMPTIONS
Consider a warehouse with three characteristics, wide-aisle, multi-pickers
and limited aisle space. The order picking routing includes return strategy,
one-way and L-pick tour and installed Computer Assisted Picking System
(CAPS). A picker obtains order data from the host computer in the I/O
point, the host computer transfers this data to a light-module and the
picker then depends on the light-module to choose items under the return
strategy. Light-modules in this warehouse are positioned at aisle entrances
(called aisle-light-modules) and also on all racks within the aisles (called
rack-light-modules). Aisle-light- modules indicate that the picker might
have to pick something needed within an aisle, while rack-light-modules
indicate the right place for picking required items. The picker should
therefore only pick from aisles with a shining aisle-light module. The
picker ignores the aisle without a shining aisle-light and moves to the
next aisle. The picker wanting to enter an aisle already containing another
picker waits in the buffer (waiting location) located between the upstream
aisle and the target aisle until the other picker leaves; this situation
is known as blocking. Buffer space is finite, so the picker can wait in
the upstream aisle when the buffer is full. Therefore the blocking probability
represents picking system efficiency. This is also a key point discussed
in this study.
Assuming manual material handling, the picker uses a Rider Pallet Truck
with infinite capacity. This study assumes that order arrival rate follows
the Poisson distribution. The picker immediately culls the items required
by the order upon order arrival at the warehouse I/O point. Therefore
labor resources are assumed infinite. The travel time (service time) of
an order within an aisle follows Exponential distribution and its average
length is determined by the picking route and amount of items requiring
picking within an aisle. An order picking system from the above resembles
a tandem queue network with a finite buffer, with each aisle resembling
a service and each picker taking an order indicating a customer.
This study analyzes total travel time of an order for individual frequency-based
storage assignment using the above picking system and then measures picking
system efficiency based on total travel time.
STORAGE ASSIGNMENT POLICIES APPLICATION
Most storage policies must consider two factors, namely required storage
space and item picking frequency. Storage assignment sequence is assumably
based on item picking frequency and storage space is ignored. The thesis
named it a frequency-based assignment policy given this assumption. Items
in this policy are assigned to storage based only on picking frequency
and high frequency items are located close to the I/O point.
Petersen and Schmenner (1999) propose four variations of frequency-based
storage policy in an order picking operation. Meanwhile, this study designs
a Heuristic assignment policy and uses it to compare the Diagonal, With-aisle
and Across-aisle of the frequency-based storage policy in the order picking
system. The different frequency-based storage policy generates adissimilar
assignment sequence and also influences system travel time. This study
objective discusses congestion for particular frequency-based assignment
policies and investigates their cause and result. Thus the following process
||Calculate the picking frequency for each item.
||According to frequency, we sort items from small to large.
||Determine the level of priority sequence given a particular
storage policy, i.e., Heuristic or With-aisle storage policy.
||Assign each item to using various storage policies.
||Calculate the mean travel time and analyze the congestion
situation for each particular storage policy.
||Use the simulation method examining and extending simulation
THE ORDER PICKING FRAMEWORK
Relevant product information such as demand rate from each store is collected
since product properties differ. The warehouse database also provides
the present warehouse situation such as storage amount and inventory to
the storage assignment method. The method produces several policies afterward.
We input each policy to the simulation model and calculate policy performance.
The warehouse manager makes decisions according to performance and implements
the decision in a real warehouse, as presented in Fig. 2.
EXAMPLE COMPUTATION AND SIMULATION
The example includes five aisles with six storage locations in the warehouse.
The warehouse I/O point is located in its lower left portion. The order
picking system is based on the earlier mentioned description. Aisle serial
numbers are assigned from left to right. The warehouse shape is an irregular
polygon, therefore aisle buffers are not equivalent. However, waiting
location capacity situated at the entrance of aisle 1 is very large because
the system avoids order loss. This study exhibits data relating to this
warehouse, as follows:
|No. of aisles (K)
|Amount of racks within aisles (S)
|Rack length (Hs)
|Rack width (Ws)
|Aisle width (Wa)
|Average velocity of truck (v)
||0.8 m sec-1
|Time of picking an item (t)
|Order arrival rate (λ)
||0.4 order min-1
|Maximum queue capacity of aisles 1~5
||50 3 3 2 2
RESULT OF FREQUENCY-BASED ASSIGNMENT POLICY
This study uses approximation to obtain various situation probability
through service rate and then uses the process to calculate mean travel
time and average number of pickers in the system (Table
The approximation process uses eM-plant to solve the complicated simultaneous
||The procedure of order picking system
||Average number of picker and mean travel time for frequency-based
Optimal storage policy from Table 1 is obviously Across-aisle
for all storage policies. Other storage policies in terms of mean travel
time (Diagonal, With-Aisle and Random) are 8.9, 10.1 and 5.8% over Across-aisle
policy. Travel distance using the Random storage policy is surprisingly
longer than for any other storage policy, but performance using the random
storage policy is better than with the Diagonal and Within-aisle storage
policies. This study previously located high frequency items in the rack
next to the I/O point, reducing travel distance in the order picking system.
However, the order-picking route is the return strategy and is one way
in this example. If the frequency-based assignment policy remains invariable,
it may generate extreme workload in aisle 1. Travel distance reduces in
the present example, but blocking probability increases substantially.
Consequently, the system anticipates reducing travel time and thus must
improve two critical factors together, as follows:
||Balance the workload of each aisle
||Reduce travel distance for all orders
Simulation is extensively applied in each field. This study implements
an order picking system to the simulation model. Consequently, some information
is obtained by the simulation model. The warehouse manager may analyze
this information and find the best policy.
Object-oriented programming is a proven powerful technique, but a systematic
design method should be used to implement reliable software, particularly
in simulation model development. Unfortunately, the sphere falls beyond
the scope of this study.