Proceedings of the 2005 Winter Simulation Conference
M. E. Kuhl, N. M. Steiger, F. B. Armstrong, and J. A. Joines, eds.
MODELING AND ANALYSIS OF A GENERIC CROSS-DOCKING FACILITY
Ghazi M. Magableh
Manuel D. Rossetti
Scott Mason
Yarmouk University
P. O. Box 4957
Irbid, 211-63, JORDAN
Department of Industrial Engineering
University of Arkansas
4207 Bell Engineering Center
Fayetteville, AR 72701, U.S.A.
chain network by simply changing the inputs to the simulation model. Each CF can be analyzed individually, thereby
reducing the need for a more complicated simulation
model that analyzes the entire distribution network. The
generic CF simulation model incorporates the following
problem aspects: resource contention for dock doors, flexible assignment of loads to in-bound (IB) and out-bound
(OB) doors, worker resource requirements, material handling contention, and outbound load building.
In the following section, we provide background and
motivation for the use of simulation on problems in logistics and distribution. Then, we provide a basic overview of
the operations of a CF and describe those aspects of a CF
that were incorporated into the generic CF simulation
model. We then discuss the development and use of the
simulation model. Finally, we conclude with some ideas
gained from the modeling exercise and how the model
could be used in the future.
ABSTRACT
Many companies utilize a number of complex product distribution networks to transport various types of goods.
Most of their distribution networks consist of transporting
goods from their suppliers to their Distribution Centers
(DCs). In the process of transporting goods from suppliers
to DCs, cross docking facilities are utilized to consolidate
loads in order to minimize costs. A simulation model of a
generic cross-docking facility (CF) was developed to examine the operational risks associated with individual CFs
within a company’s distribution network under a dynamic
environment. The model was tested and validated on a
large cross-docking facility. In addition, it was used to examine the effect of increasing demand through the crossdocking facility. The model is generic and can easily be
expanded to model other cross-docking facilities.
1
INTRODUCTION
2
Many companies utilize a number of complex product distribution networks to transport various types of goods.
Most of their distribution networks consist of transporting
goods from their suppliers to their Distribution Centers
(DCs). In the process of transporting goods from suppliers
to DCs, cross-docking facilities (CFs) are utilized to consolidate loads in order to minimize costs.
The dense network of suppliers, CFs, and DCs
throughout large geographic areas creates an issue of assignment of CFs to DCs. With growing demand, companies may need to define new strategic locations for possible CFs or identifying the assignment of CFs to DCs to
help determine if more demand can be routed through the
existing CFs through expansion.
In this paper, we discuss the development and use of a
generic simulation model to represent the operations within
a cross-docking facility, specifically the processing of inbound and outbound shipments. The generic CF model
can be tailored to model any CF in the company’s supply
BACKGROUND
Discrete event simulation is considered as one of the stateof-the-art technologies that are critical to success and is
commonly used in designing and executing operations
within the supply chain (Lendermann and McGinnis 2001).
It is one of the effective methods used to represent complex interdependencies between organizations (Swaminathan et al. 1994). Simulation is considered one of the best
means to analyze and deal with the dynamic nature of the
supply chain (Schunk and Plot 2000) and can represent the
performance effects of operational factors (Beamon and
Chen 2001). Simulation can also deal with supply chain
uncertainty and complex systems dynamics. It has the capability of helping the optimization process by evaluating
the alternative policies (Ding 2004). Also, simulation can
be used to study and evaluate new policies before implementation. It is one of the best tools that can be used to
study and analyze the effect of demand changes (Wikner et
al. 1991).
1613
Magableh, Rossetti, and Mason
Several real life case studies were analyzed and tested
using simulation. Information from a real database has
been used to distribute vehicles and resources through a
network (Dala et al. 2003). Simulation was used to study
demand variation as one of the main sources of uncertainty. Yee (2002) considers customer demand variation as
a key source of uncertainty in a supply chain. He develops
an order-to-delivery supply chain using detailed inputs associated with demand, supply, and production processes.
Venkateswaran and Son (2002) develop and analyze multiple simulation models, for different demand patterns, in
varying degrees of detail. A set of strategies are then
evaluated against each other to find the best strategy for
each model across demand patterns.
Simulation has been used to determine the requirements for logistics operations, to allow continuous operations to provide critical decision support (Kuo 2001), to determine the way a change in the size of loading and
carrying fleets would affect the performance of the system
(Giacaman et al. 2002), to validate and test the adequacy of
an expression of technique or to calculate safety stock and
the proper replenishment policy (Garcia et al. 2002), and to
capture the complexities in the supply chain and produce
the entire warehouse or transportation link (Duarte et al.
2002).
Lately simulation is considered to be one of the most
important techniques used in manufacturing and logistics
systems (Wenzel et al. 2003). Both simulation and optimization/heuristic models are needed to meet the challenges
of transportation and logistics/supply chain problems of
today and the future. Simulation has been used in warehousing and inside the distribution centers (Carson II et al.
1997). Burnett and LeBaron (2001) to develop a flexible
simulation model for the Ryder System, Inc., to validate
automated warehouse designs, predict resource requirements, and determine operational throughput capacities for
its E-channel operations. Carr and Way (1997) developed a
flexible dynamic simulation model that describes the loading, staging, travel, and unloading of rail cars at a Tropicana facility and two distribution centers. The model output and analysis enabled management to optimize rail car
availability and crew sizing. Takakuwa et al. (2000) developed simulation models of complicated and non-automated
distribution warehouse. Their method consists of two
phases: the program for generation parameters and the
simulation program. They demonstrate the applicability by
illustrating an actual case study. A simulation model for
universal warehouse storage using the ProModel simulation language was developed by Macro and Salmi (2002).
The model was used to analyze the storage capacity and
rack efficiency of the warehouse. They considered the
model to be scalable and can be modified to simulate any
warehouse.
As can be seen from the many applications of simulation to warehousing and distribution, simulation is an ex-
cellent tool for analyzing situations with complicated demand patterns. For analyzing the changes caused by
reallocating demand within a distribution network, it was
felt that a generic CF simulation model was needed. This
would enable a flexible modeling approach that enables
analysts to examine the operational risks associated with
individual CFs within the network under dynamic conditions.
3
SYSTEM DESCRIPTION AND MODELING
OVERVIEW
Often the suppliers for a company are situated at some distance from the location of final demand. For example consider a retailing network consisting of suppliers, distribution centers (DC), and store locations. Whenever a store
requires a stock of goods, the store places an order at a DC.
The DC collects orders from many stores in its vicinity and
places a purchase order (PO) with the respective suppliers.
Demand is such that the goods from a supplier will not
necessarily fill an entire truckload (TL) shipment. Further,
each supplier cannot send a less-than-truckload (LTL)
shipment the entire distance to the DC due to transportation costs. As a result, suppliers send the goods as LTL
shipments to intermediate consolidation facilities, termed
cross-docking facilities (CF). The CF consolidates all
goods going to the same DC, and fills an outbound truck.
When this occurs, outbound trucks may remain docked at
the CF until 1) additional demand arrives for the truck to
form a full load or 2) the truck has been waiting for a
threshold-hour time window, whichever occurs first and
then the load is sent to the destination DC (when either of
the conditions occur, the truck is then dispatched to its destination DC). Figure 1 is an illustration of the position of
the CF in such a supply chain network.
Figure 1. CF Supply Chain Network
As soon as an LTL shipment enters the CF, it is registered
as an incoming load. The LTL trailer is then placed in the
dock. When a door in the CF is free and the dispatcher decides to unload the LTL shipment, a yard driver is assigned
1614
Magableh, Rossetti, and Mason
assignments and results in a probability mass function
across the supplier zip codes as indicated in Figure 2.
to bring the LTL trailer to a specific door. Workers within
the CF are then assigned the task of unloading the trailer.
The workers may use forklifts, clamps, and carts during
this process. During the unloading process, the goods are
either directly loaded onto an outbound truck (if there is
one available at the outbound doors of the CF) or placed on
the bay to be loaded later.
When there are enough goods to make a TL to a specific DC in the bay, the dispatcher requests a yard driver to
bring an empty trailer to a specific door. Another worker
is then assigned to load the trailer from the bay using
whatever equipment is necessary for the shipments. Finally, once loaded the TL is sealed and dispatched to the
DC. If any POs remain at the CF for more than the threshold hours, they are deemed to be late and should be sent
directly to the assigned DC.
The demand that is assigned to a CF, the in-bound and
out-bound door assignments, the availability of workers
and material handling devices, and the shipment characteristics all effect the ability of the CF to meet its throughput
requirements. A simulation model must incorporate these
aspects as well as incorporating variability in demand arrivals, origin and destination mixes, processing times, and
resource availability. The entire process depends upon a
realistic model of the demand placed upon the CF. The generic term “demand” represents LTL shipments and their
resulting POs that are sent by suppliers to the CF for crossdocking and consolidation. LTL shipments arrive according to a random process. Each LTL shipment has an origin
(i.e. the supplier who sent the shipment). Each LTL shipment carries a number of POs. Each incoming PO has a
destination (i.e. the DC who requires the shipment). Each
incoming PO has an associated weight and cube. Actual
data from real life company for the demand and resources
of central warehouse were collected, analyzed, and incorporated in the model as follows.
Origin PMF
PMF
0.25
0.2
0.15
0.1
0.05
0
1
Supplier Zip
Figure 2: Example Origin PMF
3.2 LTL Shipment Purchase Order Model
In general, the number of POs per shipment may depend
upon the supplier and the destination. A model at this level
of fidelity would require I × J distributions to be fit for
each of the origin/destination pairs. For simplicity, we assume that the number of POs per shipment is normally distributed with a mean ( μ ) and a standard deviation ( σ )
and that it does not depend upon shipment origin or destination. Thus, if Q is a random variable that represents
the number of POs per LTL, we have:
Q ~ max ⎡Normal (μ , σ 2 )⎤ ,1 . In estimating the parameters μ and σ , we simply use the sample average and
sample variance of the observed data.
(
)
3.3 Purchase Order Destination Model
Each PO requires a destination DC to be assigned. In general, the probability that a PO is destined for a particular DC
may depend upon which supplier sent the PO. This would
require the estimation of an origin/destination probability
matrix, i.e. pij = P{D = j | O = i} , the probability that a PO
3.1 LTL Shipment Arrival Process and Origin
Modeling
We assume that the LTL shipments arrive according to a
non-homogeneous Poisson process (NHPP). We let λ t
goes to destination j given that its origin was supplier i .
In order to reduce the data requirements associated with the
simulation model, we assume that the destination does not
depend upon the origin. This allows us to model the probability that any given PO is assigned to a particular DC, as a
discrete distribution over the distribution centers, P{D = j} = q j ; j = 1, K, J where J is the total num-
represent the mean arrival rate of LTL shipments during
the weekdays at each hour in of the day,
t ∈ {1,2,3,4, K,24} . Let λW represent the LTL arrival rate
during the weekends. After a LTL shipment arrives, we
randomly determine the supplier that sent the shipment according to a probability distribution. Let O be a random
variable that represents the origin of the shipment. We
model the probability that a shipment comes from a particular supplier according to a discrete probability distribution across the supplier zips, P{O = i} = pi ; i = 1, K, I
where I is the total number of supplier origins. In the
data, supplier origins were associated with U.S. zip-code
ber of DCs. We felt that this assumption was reasonable because we are primarily focused on the volume of material
flowing through the cross-docking facility. The total volume drives the unloading requirements. The distribution
across destinations as we have modeled should still give a
good approximation for the waiting that must occur to fill a
1615
Magableh, Rossetti, and Mason
truck for a particular distribution. The destinations with low
probabilities will take longer to fill as one would see in practice. The disassociation between origin and destination will
affect performance measures on waiting for particular suppliers, but those are not the focus of this analysis.
can be modeled according to a triangular distribution. The
velocity of the transport device was modeled by a triangular distribution. For example, the unloading time distribution for the cart was triangular(52, 265, 375) seconds.
3.6 In-bound and Out-bound Shipment Dispatching
3.4 Purchase Order Weight and Cube Model
Currently, when a door is freed, we randomly determine
whether or not the door will be assigned for loading or
unloading. based on the volume of demand consolidated
by the CF. Currently each CF has a consolidation ratio of
approximately 3 to 1. In other words, based on the volume
of demand, it is seen that for every 3 LTL’s entering the
CF, 1 TL is formed. Thus, we assume that when a door
becomes available that 75% of the time it will be set up for
LTL and 25% of the time it will be set up for TL. It is important to understand that these percentages do not represent the percentage of the total doors currently dedicated to
inbound (LTL) or outbound (TL) freight. Since outbound
freight takes longer to load because the TL requirements
must be met, on average a CF may have more doors dedicated to TL than to LTL at any given time. By assigning a
free door to LTL shipments 75% of the time, we are in essence giving preference or priority to LTL shipments to receive the open door. In reality, the decision to allocate a
free door is made by the dispatcher based on the number of
inbound and outbound purchase orders that are presently
on the bay and in the yard, as well as other factors. Rather
than model this complicated decision logic at this time, we
decided to give priority to inbound freight such that the
consolidation ratio is approximately met.
The simulation model provides many metrics associated with purchase order processing such as purchase order
cycle time. In addition, the model estimates space utilization, doors utilization, and resource utilization. The percentage of POs exceeding cycle time threshold limit is calculated by tracking an indicator variable:
Each PO may have its own weight and cube. We assume
that the weight and cube requirements do not depend upon
the destination. This leaves us with 2 × I distributions to
model the weight and cube for each of the suppliers.
Rather than attempt to collect the data required to fit 2 × I
distributions, we assume that each of the 2 × I distributions can be modeled with triangular distributions. The
Pareto principle was applied to identify the top 20% of the
supplier zip codes that account for 80% of the POs passing
through the CF. For this top 20%, an individual estimate
of the parameters for each of those supplier zips were developed. The weight and cubic volume of the POs coming
from the rest of the suppliers was assumed to be well modeled by one standard set of parameters for the triangular
distribution. For example, the weight distribution for one
of the top 20% suppliers was, triangular(24, 2227, 23620)
pounds and the corresponding cube distribution was triangular(3,289, 2625) cubic feet. There were approximately
400 suppliers.
With the demand modeled and incorporated into the
generic CF simulation model, we now present how the resources within the center point are modeled.
3.5 Center Point Resource and Process Modeling
Within the generic CF simulation model, we simplify resource modeling to only include the following characteristics: 1) resources involved in loading, and 2) resources involved in unloading. Specifically, we model resource
contention for dock doors, workers involved in loading and
unloading, material handling equipment used during
unloading and loading (fork trucks, clamps, and carts), and
travel distances within the CF. When a worker is assigned
the task of unloading a LTL shipment, they will pick up the
load and drop it off either in the cross-docking bay or at the
designated outbound trailer.
The model requires information on the number of dock
doors, number of workers, number of fork lifts, clamps,
and cart, the length of the facility, and the width of the facility. In addition, each truck arrives with a loads that can
be handled in a certain way. Loads are either floor, pallet,
or slip types. A distribution across these types was developed. The capacity in terms of cubic feet and pounds was
also specified for fork lifts, clamps, carts, and slip movers.
The processing time requirements will vary according to
the type of load. We assume that the time to unload (from
the transporter) or to load (on the transporter) a shipment
4
SIMULATION MODEL IMPLEMENTATION
The generic CF model was developed using the Arena
simulation modeling environment. A detailed presentation
of the Arena logic is beyond the scope of this paper and
would require significant space to present. Instead, we
present a narrative overview of how some of the issues
were handled. The model was documented using detailed
descriptions as illustrated in Figure 3.
The simulation model starts with the creation of LTLs
at the in-bound (IB) side of the CF, as soon as an LTL enters the CF, the LTL trailer is then placed in a queue for the
dock. When a door in the CF is free and the dispatcher decides to unload this LTL, the yard driver is assigned to
bring the LTL trailer to the specific door. A worker in the
CF is then assigned the task of unloading the trailer. The
worker may use forklifts, clamps, carts, and slip load mov-
1616
Magableh, Rossetti, and Mason
ers in this process. During the unloading process, the
goods are either directly loaded onto an outbound TL (if
there is a trailer for the destination at another door of the
CF) or placed on the bay to be loaded later. When there
are enough goods in the bay to fill a TL to a specific DC
either because of weight or cube requirements, the dispatcher will request the yard driver to bring an empty
trailer to a specific outbound dock door. Another worker is
assigned to load the trailer from the bay using the necessary equipment. Finally, the outbound TL will be sealed
and dispatched to the DC; however, there are instances
when the number of POs destined for a CF do not form a
TL, when this occurs the POs will either (1) wait for more
POs to arrive or (2) if the threshold of 48 hours of waiting
has come, the POs are deemed to be late and should immediately sent directly to the assigned DC. The arrival process of the LTLs was modeled based on a random distribution and resources such as doors, equipment, and workers
were used in the loading/unloading process.
trailer. A worker will then be assigned to the loading task
who will request a transporter based on the load type being
picked up (pallet load, slip load, or floor load). The worker
will then use the transporter to pick up the appropriate PO
from the bay to complete the loading process. Once the
worker loads the PO onto the OB trailer, the transporters
are returned and the worker is released. If the OB trailer is
full at this point as a result of this load, the dispatcher will
release the OB trailer so that the door can be free.
The dispatcher in a CF has three main functions; (1)
manage the use of doors, (2) assign unloading tasks and (3)
assign loading tasks. The model has three logical entities
that fulfill each of the functions of the dispatcher. When
the door dispatcher is alerted that a trailer is ready to be
processed, the dispatcher looks for a free door. If a free
door is found, the dispatcher decides whether to use the
door for loading or unloading based on the amount of IB
and OB products present in the bay and in the dock. When
the dispatcher decides to use the door for unloading, the IB
trailer is removed from the dock and the dispatcher requests for an empty OB trailer. After assigning a destination, the IB/OB trailer is sent to the respective free door.
When IB and OB trailers are assigned to free doors
(door dispatcher sub model), a board in the middle of the CF
showing which doors are serving which trailers is updated.
This board makes it easy for the loaders/unloaders to perform their activities. If the trailer is IB, the dispatcher is
alerted to start the unloading process. After unloading, the
array is again updated; the dispatcher is then informed that
the door is free and that the POs are on the bay.
Create an IB trailer based on the time of the day
Create the weekend IB trailers
Assign the origin of the trailer based on a discrete distribution
Assign the number of POs of the trailer based on a normal
distribution
Assign the load type of the trailer based on a discrete distribution
Assign the IB property of the trailer to 1
Assign the OB property of the trailer to 0
Assign a serial number to the trailer
Assign the IB trailer arrival time
Assign a variable to count the IB trailers
Signal Door Dispatcher that IB trailer available in dock for
unloading
Store IB trailer in IBInDock.Queue
4.2 Statistical Collection
The output from Arena was used to collect statistics to assess the CF utilizations and other sensitivity analysis. The
resource and transporter utilization, The Bay space utilization (BSU), CF utilization (CFU), The % of POs exceeding cycle time threshold limit, Purchase order cycle time,
the OB truck waiting time, The ratio between the IB trailers and the OB trailers. The number of late TL is also a statistical output, % of trailers leaving LTL, % of trailers
leaving LTL lower than weight criteria, % of trailers leaving LTL lower than cube criteria ,average weight of OB
TL, average cube of OB TL, average number of OB trailers
at doors, average number of IB trailers at doors, % of doors
assigned for OB, % of doors assigned for IB.
Figure 3: Example Model Documentation
5
4.1 Process Modeling
As soon as an IB trailer is created, the trailer is placed in
queue until an available resource is dispatched to process
the trailer. Each trailer has a distinct number of POs based
on its origin. The load types on a trailer can be either a
pallet load, slip load, or a floor load. The loading process
begins when a PO has been assigned to a specific OB
UTILIZING THE GENERIC CF
SIMULATION MODEL
This system start with an empty dock and ends with and
empty dock. As a terminating system, the system will start
at 7:00 AM Monday and end at 7:00 AM Saturday. The
running time for the simulation was set at 120 hours (Five
days); this was chosen to imitate the real system, which
1617
Magableh, Rossetti, and Mason
also runs 24 hours a day, five days a week all year round.
A sample size of 20 replications for 95% confidence interval was found to be suitable for the system. The time
measurements are in hours. While the simulation is running, random arrivals are initiated and enter the system.
Previously we discussed the fact that each CF has a
consolidation ratio of approximately 3:1. In other words,
based on the volume of demand, it is seen that for every 3
LTLs entering the CF, 1 TL is formed. To see if our model
is capturing the consolidation ratio, we compared the simulation output to data supplied by the leading company. The
simulated consolidation ratio was very close to the consolidation ratio which indicates that the model is replicating the actual consolidation ratio. In addition, the percentage of late POs is 0.052 (5.2%) which is very close to the
percentage that the company personnel expect (around 5%
POs will exceed 48 hrs limit).
expanded, contracted, opened, or closed. The results of
such an analysis may indicate that additional demand may
need to flow through specific CF’s within the network. To
facilitate analysis of whether or not a specific CF can handle new network configurations, the generic SF model can
be utilized with data for the specific CF in question.
Future research will investigate the use of this model
for analyzing dispatching rules within CF’s. The dispatching rules used in this model were developed to simplify the
modeling effort and to approximate the overall flow
through a CF. Recent research on cross-docking facilities
has indicated that best-practices suggest that companies
should utilize sophisticated trailer assignment algorithms
and dispatching rules to improve the overall efficiency of
their crossing docking operations. We plan to expand the
model to investigate such questions.
PO Time in Bay
5.1 Assessments of the Current System
4
Time (hrs)
The current system configurations (number of resources)
were fixed while the demand was increased by 10-12% per
year. The results are shown in Table 1 show that the door
utilization and the Bay utilization will increase as the demand increases. As the demand increases, the PO time inside the Bay will increase (Figure 4) while the OB trailer
waiting time will decrease (Figure 5) which makes the PO
cycle time almost the same (Figure 6). This happens because as the demand increases, the OB trailers do not have
to wait as long to be filled; since the current fraction of
demand for a specific DC is relatively small.
When increasing the demand, the percentage of late
POs and the percentage of late OB trucks will decrease.
This is because the increase in demand will allow more
trucks to depart to the destination DC under the current
availability of resources (not fully utilized). Also, it is important to realize, with the current demand, while the ratio
of late POs is 5.2%, the ratio of late OB trucks is 15.1%,
which indicates that the number of trucks that carry late
POs (exceed 48 hours) is relatively high. It is clear that
when decreasing the threshold value from 48 to 24 hrs, the
number of late POs and number of Late TL will increase.
3
2
1
0
1
2
3
4
5
6
7
8
9
10 11
Year
Figure 4. PO Time in CF
OB Trailer Waiting Time
Time (hrs)
5
4
3
2
1
0
1
2
3
4
5
6
7
8
9
10 11
Year
Figure 5. Average OB Trailer Waiting Time
Average PO Cycle Time
6 CONCLUSION
10
We have discussed in detail the modeling assumptions
within the generic cross-docking facility model and described the implementation of the model within the Arena
simulation environment. In addition, we have illustrated
the use of the model based on the available data. The
model can assist logistics planners who are evaluating
changes to the overall distribution network. From a strategic planning perspective, optimization can be used to analyze the network to determine which CF’s supply which
distribution centers and to determine which CF’s should be
Hours
8
6
4
2
0
1
2
3
4
5
6
7
8
9
10 11
Year
Figure 6. Average PO Cycle Time
1618
Magableh, Rossetti, and Mason
Metric
Percentage of Late POs
Percentage of Late TL
IB to OB Trailer Ratio
PO Time at the Bay
PO time Inside CF
PO Cycle Time
OB Trailer Waiting Time
Doors Utilization%
Workers Utilization%
Forklifts Utilization%
Clamps Utilizations%
Carts Utilization%
Bay Utilization%
Y*: Year
Current
0.052
0.151
2.754
1.996
3.3
8.016
4.716
0.829
0.106
0.014
0.043
0.038
0.036
Table 1. Results of Current System Assessment
Y*1
Y2
Y3
Y4
Y5
Y6
0.055 0.051 0.053 0.045 0.036 0.045
0.14
0.093 0.101 0.063 0.103 0.073
2.893 2.991 3.297 3.87
4.463 4.578
2.197 2.318 2.502 2.731 2.613 3.417
3.595 3.811 4.062 4.295 4.102 5.166
8.191 8.42
7.973 7.808 7.209 8.724
4.596 4.609 3.91
3.513 3.106 3.558
0.832 0.834 0.837 0.842 0.845 0.848
0.116 0.138 0.152 0.157 0.155 0.196
0.015 0.018 0.022 0.021 0.022 0.03
0.043 0.051 0.063 0.064 0.063 0.091
0.039 0.046 0.053 0.054 0.055 0.074
0.044 0.05
0.06
0.067 0.072 0.102
Y7
0.026
0.042
5.468
2.903
4.455
6.945
2.49
0.852
0.183
0.023
0.069
0.062
0.092
Y8
0.027
0.063
6.396
3.188
4.818
7.152
2.335
0.854
0.214
0.031
0.09
0.083
0.113
Y9
0.032
0.048
6.818
3.539
5.281
7.433
2.152
0.858
0.264
0.042
0.125
0.111
0.144
Y10
0.025
0.049
7.082
3.654
5.418
7.383
1.965
0.863
0.282
0.047
0.142
0.123
0.147
Metric
Table 2. Performance Measures with 24 hrs Limit
Y2
Y3
Y4
Y5
Y6
Current Y1
Y7
Y8
Y9
Y10
Percentage of Late POs
0.075
0.071
0.067
0.073
0.064
0.05
0.063
0.048
0.037
0.053
0.037
Percentage of Late TL
0.259
0.227
0.167
0.123
0.061
0.089
0.054
0.033
0.038
0.037
0.068
Ding H., C. Hans, L. Benyoucef, J. Schumacher, X. Xie.
2004. “One” Anew Tool For Supply Chain Network
Optimization And Simulation. Proceedings of the
2003 Winter Simulation Conference. Eds, R.G. Ingalls,
M.D. Rossetti, J.S. Smith, and B.A. Peters. Institute of
Electrical and Electronics Engineers, Piscataway, New
Jersey.
Duarte B.M., J. W. Fowler, E. Gel, And D. Shunk. 2002.
Parameterization Of Fast And Accurate Simulation
For Complex Supply Networks. Proceedings of the
2002 Winter Simulation Conference. Eds , E.Yucesan,
C.H. Cehn, J.L. Snowdon, and J.M. Charnes. Institute
of Electrical and Electronics Engineers, Piscataway,
New Jersey.
Garcia E.S., E. Saliby, C.F. Silva. 2002. A Simulation
Model To Validate And Evaluate The Adequacy On
An Analytical Expression For Paper Safety Stock Sizing. Proceedings of the 2002 Winter Simulation Conference. Eds , E. Yucesan, C.H. Cehn, J.L. Snowdon,
And J.M. Charnes. Institute Of Electrical And Electronics Engineers, Piscataway, New Jersey.
Giacaman G.J., R.P. Medel, J.A. Tabilo. 2002. Simulation
of the Material Transporting and Loading Process In
Pedro De Valdivia Mine. Proceedings of the 2002
Winter Simulation Conference. Eds, E.Yucesan, C.H.
Cehn, J.L. Snowdon, and J.M. Charnes. Institute of
Electrical and Electronics Engineers, Piscataway, New
Jersey.
Ingalls R.G. 1998. The Value of Simulation In Modeling
Supply Chains. Proceedings of the 1998 Winter Simulation Conference. Ed., D.J. Medeiros, E.F. Watson,
REFERENCES
Beamon, B. M., and V.C.P. Cehen. 2001. Performance
Analysis of Conjoined Supply Chains. International
Journal of Production Research 36(14):3195-3218.
Burnett D., T. Le Baron. 2001. Efficiently Modeling
Warehouse Systems. Proceedings of the 2001 Winter
Simulation Conference. Eds, B.A. Peters, J.S. Smith,
D.J. Medeiros, and M.W. Rohrer. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey.
Carr M., H. Way. 1997. Million Dollar Logistic Decisions
Using Simulation. Proceedings of the 1997 Winter
Simulation Conference. Eds , S. Andradottir, K.J.
Healy, D.H.Withers, and B.L. Nelson. Institute Of
Electrical And Electronics Engineers, Piscataway,
New Jersey.
Carson Ii J.S., M.S. Manivannan, M. Brazier, E. Miller,
and M. Brazier. 1997. Panel on Transportation and
Logistics Modeling. Proceedings of the 1997 Winter
Simulation Conference. Eds , S. Andradottir, K.J.
Healy, D. H. Withers, and B.L. Nelson. Institute of
Electrical and Electronics Engineers, Piscataway, New
Jersey.
Dala M.A., H.Bell, M. Denizen, And M.P. Keller. 2003.
Initializing A Distribution Supply Chain Simulation
With Live Data. Proceedings of the 2003 Winter Simulation Conference. Eds , S. Chick, P.J. Sanchez, D.
Ferrin, and D.J. Morrice. Institute Of Electrical And
Electronics Engineers, Piscataway, New Jersey.
1619
Magableh, Rossetti, and Mason
J.S. Carson, and M.S. Manvannan, 1371-1375. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey.
Kuo, S. S., E.J. Chen, P.L.Selikson, Y.M. Lee. 2001. Modeling Continuous Flow with Discrete Event Simulation. Proceedings of the 2001 Winter Simulation Conference. Eds, B.A. Peters, J.S. S, Ith, D.J. Medeiros,
And M.W. Rohrer. Institute Of Electrical And Electronics Engineers, Piscataway, New Jersey.
Lendermann, P., and L.F. McGinnis. 2001. Distributed
Simulation With Incorporated APS Procedures For
High-Fidelity Supply Chain Optimization. Proceedings of the 2001 Winter Simulation Conference. B.A.
Peters, J.S. S, Ith, D.J. Medeiros, and M.W. Rohrer,
Eds. Institute of Electrical and Electronics Engineers,
Piscataway, New Jersey.
Macro J.G., R.E. Salmi. 2002. A Simulation Tool to Determine Warehouse Efficiencies and Storage Allocations. . Proceedings of the 2002 Winter Simulation
Conference. Eds, E. Yucesan, C.H. Cehn, J.L. Snowdon, and J.M. Charnes. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey.
Schunk D., and B. Plot. 2000. Using Simulation to Analyze
Supply Chains. Proceedings of the 2000 Winter Simulation Conference, Ed., J.A. Joines, R.R. Barton, K.
Kang And P.A. Fishwick, 1211-1216, Institute Of
Electrical And Electronics Engineers, Piscataway,
New Jersey.
Swaminathan, J.M., S.F. Smith, and N.M. Sadeh. 1994.
Modeling the Dynamics of Supply Chains. In Proceedings Of Aaai-94 Sigman Workshop On Intelligent
Manufacturing Systems: 113-122.
Takakuwa S., H. Takizawa, K. Ito, and S. Hiraoka. 2000.
Simulation and Analysis of Non-Automated Distribution Nwarehouse. Proceedings of the 2000 Winter
Simulation Conference. Eds, J.A. Joines, R.R Barton,
K.Kang, and P.A. Fishwick. Institute of Electrical and
Electronics Engineers, Piscataway, New Jersey.
Venkateswaran J., Y.J. Son. 2002. Investigation of Influence Of Modeling Fidelities On Supply Chain Dynamics. Proceedings of the 2002 Winter Simulation Conference. Eds, E. Yucesan, C.H. Cehn, J.L. Snowdon,
and J.M. Charnes. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey.
Wenzel S., J. Bernhard, and U. Jessen. 2003. A Taxonomy
of Visualization Techniques for Simulation Production
and Logistics. Proceedings of the 2003 Winter Simulation Conference. Eds, S. Chick, P.J. Sanchez, D. Ferrin, and D.J. Morrice. Institute of Electrical And Electronics Engineers, Piscataway, New Jersey.
Winker, J., D.R. Towill, and M. Naim. 1991. Smoothing
Supply Chain Dynamics. International Journal of
Production Economics 22: 231-248.
Yee S.T. 2002 Establishment of Product Offering and Production Leveling Principles Via Supply Chain Simula-
tion Under Order-To-Delivery Environment. Proceedings of the 2002 Winter Simulation Conference. Eds,
E. Yucesan, C.H. Cehn, J.L. Snowdon, and J.M. Charnes. Institute of Electrical and Electronics Engineers,
Piscataway, New Jersey.
AUTHOR BIOGRAPHIES
GHAZI MAGABLEH is currently working for the Directorate of Electrical and Mechanical Engineering at
RJAF/Jordan. He received his B.Sc in Mechanical Engineering from Mu’tah University (Jordan), his MSc in Industrial Engineering/Management from University of Jordan (Jordan), and his Ph. D. in Industrial Engineering from
University of Arkansas (USA). His research interests include: applied operation research, transportation logistics,
and supply chain management.
MANUEL D. ROSSETTI, Ph. D., P. E. is an Associate
Professor in the Industrial Engineering Department at the
University of Arkansas. He received his Ph.D. in Industrial and Systems Engineering from The Ohio State University. Dr. Rossetti has published over thirty-five journal
and conference articles in the areas of transportation,
manufacturing, health care and simulation and he has obtained over $1.5 million dollars in extra-mural research
funding. His research interests include the design, analysis, and optimization of manufacturing, health care, and
transportation systems using stochastic modeling, computer simulation, and artificial intelligence techniques. He
was selected as a Lilly Teaching Fellow in 1997/98 and has
been nominated three times for outstanding teaching
awards. He is currently serving as Departmental ABET
Coordinator. He serves as an Associate Editor for the International Journal of Modeling and Simulation and is active in IIE, INFORMS, and ASEE. He served as co-editor
for the WSC 2004 conference. His email and web adrossetti@uark.edu
and
dresses
are
www.uark.edu/~rossetti.
SCOTT J. MASON is an Assistant Professor and Associate Department Head of Industrial Engineering at the University of Arkansas. He received his BSME and MSE
(Operations Research) degrees from The University of
Texas at Austin and his PhD (Industrial Engineering) from
Arizona State University. His research and teaching interests include production planning and scheduling; semiconductor manufacturing; and manufacturing and transportation applications of operations research. He is a member
of INFORMS and a Senior Member of IIE.
1620
View publication stats