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IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 2, No 3, March 2010
ISSN (Online): 1694-0784
ISSN (Print): 1694-0814
1
A General Simulation Framework for Supply Chain Modeling:
State of the Art and Case Study
Antonio Cimino
1
, Francesco Longo
2
and Giovanni Mirabelli
3
1
Mechanical Department, University of Calabria,
Rende (CS), 87036, Italy
2
Mechanical Department, University of Calabria,
Rende (CS), 87036, Italy
3
Mechanical Department, University of Calabria,
Rende (CS), 87036, Italy
Abstract
Nowadays there is a large availability of discrete event
simulation software that can be easily used in different
domains: from industry to supply chain, from healthcare to
business management, from training to complex systems
design. Simulation engines of commercial discrete event
simulation software use specific rules and logics for
simulation time and events management. Difficulties and
limitations come up when commercial discrete event
simulation software are used for modeling complex real
world-systems (i.e. supply chains, industrial plants). The
objective of this paper is twofold: first a state of the art on
commercial discrete event simulation software and an
overview on discrete event simulation models
development by using general purpose programming
languages are presented; then a Supply Chain Order
Performance Simulator (SCOPS, developed in C++) for
investigating the inventory management problem along the
supply chain under different supply chain scenarios is
proposed to readers.
Keywords:
Discrete Event Simulation, Simulation languages,
Supply Chain, Inventory Management.
1. Introduction
As reported in [1], discrete-event simulation software
selection could be an exceeding difficult task especially
for inexpert users. Simulation software selection problem
was already known many years ago. A simulation buyer’s
guide that identifies possible features to consider in
simulation software selection is proposed in [2]. The guide
includes in the analysis considerations several aspects
such as Input, Processing, Output, Environment, Vendor
and Costs. A survey on users’ requirements about discrete-
event simulation software is presented in [3]. The analysis
shows that simulation software with good
visualization/animation properties are easier to use but
limited in case of complex and non-standard problems.
Further limitations include lack of software compatibility,
output analysis tools, advanced programming languages.
In [4] and [5] functionalities and potentialities of different
commercial discrete-event simulation software, in order to
support users in software selection, are reported. In this
case the author provides the reader with information about
software vendor, primary software applications, hardware
platform requirements, simulation animation, support,
training and pricing.
Needless to say that Modeling & Simulation should be
used when analytical approaches do not succeed in
identifying proper solutions for analyzing complex
systems (i.e. supply chains, industrial plants, etc.). For
many of these systems, simulation models must be: (i)
flexible and parametric (for supporting scenarios
evaluation) (ii) time efficient (even in correspondence of
very complex real-world systems) and (iii) repetitive in
their architectures for scalability purposes [6].
Let us consider the traditional modeling approach
proposed by two commercial discrete event simulation
software, Em-Plant by Siemens PLM Software solutions
and Anylogic by Xj-Technologies. Both of them propose a
typical object oriented modeling approach. Each discrete
event simulation model is made up by system state
variables, entities and attributes, lists processing, activities
and delays. Usually complex systems involve high
numbers of resources and entities flowing within the
simulation model. The time required for executing a
simulation run depends on the numbers of entities in the
IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 2, No 3, March 2010
www.IJCSI.org
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simulation model: the higher is the number of entities the
higher is the time required for executing a simulation run.
In addition, libraries objects, which should be used for
modeling static entities, very often fall short of recreating
the real system with satisfactory accuracy. In other words,
the traditional modeling approach (proposed by eM-Plant
and Anylogic as well as by a number of discrete event
simulation software), presents two problems: (i)
difficulties in modeling complex scenarios; (ii) too many
entities could cause computational heavy simulation
models. Further information on discrete event simulation
software can be found in [7].
An alternative to commercial discrete event simulation
software is to develop simulation models based on general
purpose programming languages (i.e. C++, Java). The use
of general purpose programming languages allows to
develop ad-hoc simulation models with class-objects able
to recreate carefully the behavior of the real world system.
The objective of this paper is twofold: first a state of the
art on commercial discrete event simulation software and
an overview on discrete event simulation models
development by using general purpose programming
languages are presented; then a Supply Chain Order
Performance Simulator (SCOPS, developed in C++) for
investigating the inventory management problem along the
supply chain under different supply chain scenarios is
proposed to readers.
Before getting into details of the work, in the sequel a
brief overview of paper sections is reported. Section 2
provides the reader with a detailed description of different
commercial discrete event simulation software. Section 3
presents a general overview of programming languages
and describes the main steps to develop a simulation
model based on general purpose programming languages.
Section 4 presents a three stages supply chain simulation
model (called SCOPS) used for investigating inventory
problems along the supply chain. Section 5 describes the
simulation experiments carried out by using the simulation
model. Finally the last section reports conclusions and
research activities still on going.
2. Discrete Event Simulation Software
Table 1 reports the results of a survey on the most widely
used discrete event simulation software (conducted on 100
people working in the simulation field). The survey
considers among others some critical aspects such as
domains of application (specifically manufacturing and
logistics), 3D and virtual reality potentialities, simulation
languages, prices, etc. For each aspect and for each
software the survey reports a score between 0 and 10.
Table 1 help modelers in discrete event simulation
software selection. Moreover the following sections
reports a brief description of all the software of table 1 in
terms of domains of applicability, types of libraries (i.e.
modeling libraries, optimization libraries, etc.), input-
output functionalities, animation functionalities, etc.
2.1 Anylogic
Anylogic is a Java based simulation software, by XJ
Technologies [8], used for forecasting and strategic
planning, processes analysis and optimization, optimal
operational management, processes visualization. It is
widely used in logistics, supply chains, manufacturing,
healthcare, consumer markets, project management,
business processes and military. Anylogic supports Agent
Based, Discrete Event and System Dynamics modeling
and simulation. The latest Anylogic version (Anylogic 6)
has been released in 2007, it supports both graphical and
flow-chart modeling and provides the user with Java code
for simulation models extension. For input data analysis,
Anylogic provides the user with Stat-Fit (a simulation
support software by Geer Mountain Software Corp.) for
distributions fitting and statistics analysis. Output analysis
functionalities are provided by different types of datasets,
charts and histograms (including export function to text
files or excel spreadsheet). Finally simulation optimization
is performed by using Optquest, an optimization tool
integrated in Anylogic.
2.2 Arena
Arena is a simulation software by Rockwell Corporation
[9] and it is used in different application domains: from
manufacturing to supply chain (including logistics,
warehousing and distribution) from customers service and
strategies to internal business processes. Arena (as
Anylogic) provides the user with objects libraries for
systems modeling and with a domain-specific simulation
language, SIMAN [10]. Simulation optimizations are
carried out by using Optquest. Arena includes three
modules respectively called Arena Input Analyzer (for
distributions fitting), Arena Output Analyzer (for
simulation output analysis) and Arena Process Analyzer
(for simulation experiments design). Moreover Arena also
provides the users animation at run time as well as it
allows to import CAD drawings to enhance animation
capabilities.
Table 1: Survey on most widely used Simulation software
IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 2, No 3, March 2010
ISSN (Online): 1694-0784
ISSN (Print): 1694-0814
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2.3 Automod
Automod is a discrete event simulation software,
developed by Applied Materials Inc. [11] and it is based
on the domain-specific simulation language Automod.
Typical domains of application are manufacturing, supply
chain, warehousing and distribution, automotive, airports
and semiconductor. It is strongly focused on transportation
systems including objects such as conveyor, Path Mover,
Power & Free, Kinematic, Train Conveyor, AS/RS,
Bridge Crane, Tank & Pipe (each one customizable by the
user). For input data analysis, experimental design and
simulation output analysis, Automod provides the user
with AutoStat [12]. Moreover the software includes
different modules such as AutoView devoted to support
simulation animation with AVI formats.
2.4 Em-Plant
Em-plant is a Siemens PLM Software solutions [13],
developed for strategic production decisions. EM-Plant
enables users to create well-structured, hierarchical models
of production facilities, lines and processes. Em-Plant
object-oriented architecture and modeling capabilities
allow users to create and maintain complex systems,
including advanced control mechanisms. The Application
Object Libraries support the user in modeling complex
scenarios in short time. Furthermore EM-Plant provides
the user with a number of mathematical analysis and
statistics functions for input distribution fitting and single
or multi-level factor analysis, histograms, charts,
bottleneck analyzer and Gantt diagram. Experiments
Design functionalities (with Experiments Manager) are
also provided. Simulation optimization is carried out by
using Genetic Algorithms and Artificial Neural Networks.
2.5 Promodel
Promodel is a discrete event simulation software
developed by Promodel Corporation [14] and it is used in
different application domains: manufacturing,
warehousing, logistics and other operational and strategic
situations. Promodel enables users to build computer
models of real situations and experiment with scenarios to
find the best solution. The software provides the users
with an easy to use interface for creating models
graphically. Real systems randomness and variability can
be either recreated by utilizing over 20 statistical
distribution types or directly importing users’ data. Data
can be directly imported and exported with Microsoft
Excel and simulation optimizations are carried out by
using SimRunner or OptQuest. Moreover, the software
technology allows the users to create customized front-
and back-end interfaces that communicate directly with
ProModel.
2.6 Flexsim
Flexsim is developed by Flexsim Software Products [15]
and allows to model, analyze, visualize, and optimize any
kind of real process - from manufacturing to supply
chains. The software can be interfaced with common
spreadsheet and database applications to import and export
data. Moreover, Flexsim's powerful 3D graphics allow in-
model charts and graphs to dynamically display output
statistics. The tool Flexsim Chart gives the possibility to
analyze the simulation results and simulation
optimizations can be performed by using both Optquest as
well as a built-in experimenter tool. Finally, in addition to
the previous described software, Flexsim allow to create
own classes, libraries, GUIs, or applications.
Anylogic Arena AutoMod Emplant Promodel Flexsim Witness
Logistic 6.5 7.5 7 7.2 6.5 7 7.5
Manufacturing 6.6 7.5 6.5 7.2 6.7 6.7 7.5
3D Virtual Reality 6.6 6.9 7.3 6.8 6.7 7.2 7
Simulation Engine 7 8 7.5 8 7 7.5 8
User Ability 7 8 6 7 9 7.5 8
User Community 6.2 9 6.7 6.5 7.5 6.6 8.5
Simulation Language 6.8 7 6.25 6.5 6.5 6.7 6.5
Runtime 7.5 7 6.5 6.5 7.5 6 7
Analysis tools 6.5 8 6.9 7.1 7.7 6 7.8
Internal Programming 7.2 7 6 7 6.2 7 6.5
Modular Construction 6.1 7 6 6.5 7.5 7 7
Price 7 6 5.6 5.8 7 5.7 6
IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 2, No 3, March 2010
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2.7 Witness
Witness is developed by Lanner Group Limited [16]. It
allows to represent real world processes in a dynamic
animated computer model and then experiment with
“what-if” alternative scenarios to identify the optimal
solution. The software can be easily linked with the most
common spreadsheet, database and CAD files. The
simulation optimization is performed by the Witness
Optimizer tool that can be used with any Witness model.
Finally the software provides the user with a scenario
manager tool for the analysis of the simulation results.
3. General Purpose and Specific Simulation
Programming Languages
There are many programming languages, general purpose
or domain-specific simulation language (DSL) that can be
used for simulation models development. General purpose
languages are usually adopted when the programming
logics cannot be easily expressed in GUI-based systems or
when simulation results are more important than advanced
animation/visualization [17]. Simulation models can be
developed both by using discrete-event simulation
software and general purpose languages, such as C++ or
Java [18].
As reported in [1] a simulation study requires a number of
different steps; it starts with problem formulation and
passes through different and iterative steps: conceptual
model definition, data collection, simulation model
implementation, verification, validation and accreditation,
simulation experiments, simulation results analysis,
documentation and reports. Simulation model
development by using general purpose programming
languages (i.e. C++) requires a deep knowledge of the
logical foundation of discrete event simulation. Among
different aspects to be considered, it is important to
underline that discrete event simulation model consists of
entities, resources control elements and operations [19].
Dynamic entities flow in the simulation model (i.e. parts in
a manufacturing system, products in a supply chain, etc.).
Static entities usually work as resources (a system part that
provides services to dynamic entities). Control elements
(such as variables, boolean expressions, specific
programming code, etc.) support simulation model states
control. Finally, operations represent all the actions
generated by the flow of dynamic entities within the
simulation model. During its life within the simulation
model, an entity changes its state different times. There are
five different entity states [19]: Ready state (the entity is
ready to be processed), Active state (the entity is currently
being processed), Time-delayed state (the entity is delayed
until a predetermined simulation time), Condition-delayed
state (the entity is delayed until a specific condition will be
solved) and Dormant state (in this case the condition
solution that frees the entity is managed by the modeler).
Entity management is supported by different lists, each
one corresponding to an entity state: the CEL, (Current
Event List for active state entity), the FEL (Future Event
List for Time-delayed entities), the DL (Delay List for
condition-delayed entities) and UML (User-Managed Lists
for dormant entities). In particular, Siman and GPSS/H
call the CEL list CEC list (Current Events Chain), while
ProModel language calls it AL (Action List). The FEL is
called FEP (Future Events Heap) and FEC (Future Event
Chain) respectively by Siman and GPSS/H. After entities
states definition and lists creation, the next step is the
implementation of the phases of a simulation run: the
Initialization Phase (IP), the Entity Movement Phases
(EMP) and the Clock Update Phase (CUP). A detailed
explanation of the simulation run anatomy is reported in
[19].
4. A Supply Chain Simulation Model
developed in C++
According to the idea to implement simulation models
based on general purpose programming languages, the
authors propose a three stage supply chain simulation
model implemented by using the Borland C++ Builder to
compile the code (further information on Borland C++
Builder can be found in [20]). The acronym of the
simulation model is SCOPS (Supply-Chain Order
Performance Simulator). SCOPS investigates the
inventory management problem along a three stages
supply chain and allows the user to test different scenarios
in terms of demand intensity, demand variability and lead
times. Note that such problem can be also investigated by
using discrete event simulation software [21], [22], [23]
and [24].
The supply chain conceptual model includes suppliers,
distribution centers, stores and final customers. In the
supply chain conceptual model a single network node can
be considered as store, distribution center or supplier. A
supply chain begins with one or more suppliers and ends
with one or more stores. Usually stores satisfy final
customers’ demand, distribution centers satisfy stores
demand and plants satisfy distribution centers demand. By
using these three types of nodes we can model a general
supply chain (also including more than three stages).
Suppliers, distribution centers and stores work 6 days per
week, 8 hours per day. Stores receive orders from
customers. An order can be completely or partially
IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 2, No 3, March 2010
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5
satisfied. At the end of each day, on the basis of an Order-
Point, Order-Up-to-Level (s, S) inventory control policy,
the stores decide whether place an order to the distribution
centers or not. Similarly distribution centers place orders
to suppliers according to the same inventory control
policies. Distribution centers select suppliers according to
their lead times (that includes production times and
transportation times).
According to the Order-Point, Order-Up-to-Level policy
[25], an order is emitted whenever the available quantity
drops to the order point (s) or lower. A variable
replenishment quantity is ordered to raise the available
quantity to the order-up-to-level (S). For each item the
order point s is the safety stock calculated as standard
deviation of the lead-time demand, the order-up to level S
is the maximum number of items that can be stored in the
warehouse space assigned to the item type considered. For
the i-th item, the evaluation of the replenishment quantity,
Q
i
(t), has to take into consideration the quantity available
(in terms of inventory position) and the order-up-to-level S.
The inventory position (equation 1) is the on-hand
inventory, plus the quantity already on order, minus the
quantity to be shipped. The calculation of s
j
(t) requires the
evaluation of the demand over the lead time. The lead time
demand of the i-th item (see equation 2), is evaluated by
using the moving average methodology. Both at stores and
distribution centers levels, managers know their peak and
off-peak periods, and they usually use that knowledge to
correct manually future estimates based on moving
average methodology. They also correct their future
estimates based on trucks capacity and suppliers quantity
discounts. Finally equations 3 and 4 respectively express
the order condition and calculate the replenishment
quantity.
)()()()( tShtOrtOhtP
iiii
(1)
i
LTt
tk
ii
kDftDlt
1
)()( (2)
))()(()( tSStstP
iii
(3)
)()( tPStQ
iii
(4)
where,
P
i
(t), inventory position of the i-th item;
Oh
i
(t), on-hand inventory of the i-th item;
Or
i
(t), quantity already on order of the i-th item;
Sh
i
(t), quantity to be shipped of the i-th item;
Dlt
i
(t), lead time demand of the i-th item;
Df
i
(t), demand forecast of the i-th item (evaluated by
means of the moving average methodology);
LT
i
, lead time of the i-th item;
s
i
(t), order point at time t of the i-th item;
S
i
, order-up-to-level of the i-th item;
SS
i
(t), safety stock at time t of the i-th item;
Q
i
(t), quantity to be ordered at time t of the i-th item.
4.1 Supply Chain Orders Perfomance Simulator
SCOPS translates the supply chain conceptual model
recreating the complex and high stochastic environment of
a real supply chain. For each type of product, customers’
demand to stores is assumed to be Poisson with
independent arrival processes (in relation to product
types). Quantity required at stores is based on triangular
distributions with different levels of intensity and
variability. Partially satisfied orders are recorded at stores
and distribution center levels for performance measures
calculation.
In our application example fifty stores, three distribution
center, ten suppliers and thirty different items define the
supply chain scenario. Figure 1 shows the SCOPS user
interface. The SCOPS graphic interface provides the user
with many commands as, for instance, simulation time
length, start, stop and reset buttons, a check box for unique
simulation experiments (that should be used for resetting
the random number generator in order to compare
different scenarios under the same conditions), supply
chain configurations (number of items, stores, distribution
centers, suppliers, input data, etc.). For each supply chain
node a button allows to access the following information
number of orders, arrival times, ordered quantities,
received quantities, waiting times, fill rates. SCOPS
graphic interface also allows the user to export simulation
results on txt and excel files. One of the most important
features of SCOPS is the flexibility in terms of scenarios
definition. The graphic interface gives to the user the
possibility to carry out a number of different what-if
analysis by changing supply chain configuration and input
parameters (i.e. inventory policies, demand forecast
methods, demand intensity and variability, lead times,
inter-arrival times, number of items, number of stores,
distribution centers and plants, number of supply chain
echelons, etc.). Figure 2 display several SCOPS windows
the user can use for setting supply chain configuration and
input parameters.
IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 2, No 3, March 2010
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6
Fig. 1 SCOPS User Interface.
Fig. 2 SCOPS Windows.
4.2 SCOPS verification, simulation run length and
validation
Verification and validation processes assess the accuracy
and the quality throughout a simulation study [26].
Verification and Validation are defined by the American
Department of Defence Directive 5000.59 as follows:
verification is the process of determining that a model
implementation accurately represents the developer’s
conceptual description and specifications, while validation
is the process of determining the degree to which a model
is an accurate representation of the real world from the
perspective of the intended use of the model.
The simulator verification has been carried out by using
the debugging technique. The debugging technique is an
iterative process whose purpose is to uncover errors or
misconceptions that cause the model’s failure and to
define and carry out the model changes that correct the
errors [1]. In this regards, during the simulation model
development, the authors tried to find the existence of
errors (bugs). The causes of each bug has been correctly
identified and the model has opportunely been modified
and tested (once again) for ensuring errors elimination as
well as for detecting new errors.
Before going into details of simulation model validation, it
is important to evaluate the optimal simulation run length.
Note that the supply chain is a non-terminating system and
one of the priority objectives of such type of system is the
evaluation of the simulation run length [1]. Information
regarding the length of a simulation run is used for the
validation. The length is the correct trade-off between
results accuracy and time required for executing the
simulation runs. The run length has been correctly
determined using the mean square pure error analysis
(MSPE). After the MSPE analysis, the simulation run
length chosen is 390 days.
Choosing for each simulation run the length evaluated by
means of MSPE analysis (390 days) the validation phase
has been conducted by using the Face Validation (informal
technique). For each retailer and for each distribution
centre the simulation results, in terms of fill rate, have
been compared with real results. Note that during the
validation process the simulation model works under
identical input conditions of the real supply chain. The
Face Validation results have been analyzed by several
experts; their analysis revealed that, in its domain of
application, the simulation model recreates with
satisfactory accuracy the real system.
5. Supply Chain Configuration and Design of
Simulation Experiments
The authors propose as application example the
investigation of 27 different supply chain scenarios. In
particular simulation experiments take into account three
different levels for demand intensity, demand variability
and lead times (minimum, medium and maximum
respectively indicated with “-”, “0” and “+” signs). Table
1 reports (as example) factors and levels for one of the
thirty items considered and table 3 reports scenarios
description in terms of simulation experiments. Each
simulation run has been replicated three times (totally 81
replications).
Table 2: Factors and levels
Minimum Medium High
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Demand
Intensity
[inter-arrival
time]
3 5 8
Demand
Variability
[item]
[18,22] [16,24] [14,26]
Lead Time
[days]
2 3 4
After the definition of factors levels and scenarios, the
next step is the performance measures definition. SCOPS
includes, among others, two fill rate performance
measures defined as (i) the ratio between the number of
satisfied Orders and the total number of orders; (ii) the
ratio between the lost quantity and the total ordered
quantity.
Simulation results, for each supply chain node and for
each factors levels combination, are expressed in terms of
average fill rate (intended as ratio between the number of
satisfied Orders and the total number of orders).
Table 3: Simulation experiments and supply chain scenarios
Run
Demand
Intensity
Demand
Variability
Lead
Time
1 - - -
2 - - 0
3 - - +
4 - 0 -
5 - 0 0
6 - 0 +
7 - + -
8 - + 0
9 - + +
10 0 - -
11 0 - 0
12 0 - +
13 0 0 -
14 0 0 0
15 0 0 +
16 0 + -
17 0 + 0
18 0 + +
19 + - -
20 + - 0
21 + - +
22 + 0 -
23 + 0 0
24 + 0 +
25 + + -
26 + + 0
27 + + +
1 - - -
5.1 Supply Chain Scenarios analysis and comparison
The huge quantity of simulation results allows the analysis
of a comprehensive set of supply chain operative
scenarios. Let us consider the simulation results regarding
the store #1; we have considered three different scenarios
(low, medium and high lead times) and, within each
scenario, the effects of demand variability and demand
intensity are investigated.
Figure 2 shows the fill rate trend at store #1 in the case of
low lead time.
Fill Rate - Store 1 - Low Lead Time
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Low Variability Medium Variability High Variability
Deamand Variability
Fill Rate
Low Intensity
Medium Intensity
High Intensity
Fig. 2 Fill rate at store #1, low lead time.
The major effect is due to changes in demand intensity: as
soon as the demand intensity increases there is a strong
reduction of the fill rate. A similar trend can be observed
in the case of medium and high lead time (figure 3 and
figure 4, respectively).
Fill Rate - Store 1 - Medium Lead Time
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Low Variability Medium Variability High Variability
Deamand Variability
Fill Rate
Low Intensity
Medium Intensity
High Intensity
Fig. 3 Fill Rate at store # 1, medium lead time.
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Fill Rate - Store 1 - High Lead Time
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Low Variability Medium Variability High Variability
Deamand Variability
Fill Rate
Low Intensity
Medium Intensity
High Intensity
Fig. 4 Fill Rate at store # 1, high lead time.
The simultaneous comparison of figures 2, 3 and 4 shows
the effect of different lead times on the average fill rate.
The only minor issue is a small fill rate reduction passing
from 2 days lead time to 3 and 4 days lead time.
As additional aspect (not shown in figures 2, 3, and 4), the
higher is the demand intensity the higher is the average on
hand inventory. Similarly the higher is the demand
variability the higher is the average on hand inventory. In
effect, the demand forecast usually overestimates the
ordered quantity in case of high demand intensity and
variability.
6. Conclusions
The paper first presents an overview on the most widely
used discrete event simulation software in terms of
domains of applicability, types of libraries (i.e. modeling
libraries, optimization libraries, etc.), input-output
functionalities, animation functionalities, etc. In the
second part the paper proposes, as alternative to discrete
event simulation software, the use of general purpose
programming languages and provides the reader with a
brief description about how a discrete event simulation
model works.
As application example the authors propose a supply chain
simulation model (SCOPS) developed in C++. SCOPS is a
flexible simulator used for investigating different the
inventory management problem along a three stages
supply chain. SCOPS simulator is currently used for
reverse logistics problems in the large scale retail supply
chain.
Acknowledgments
All the authors gratefully thank Professor A. G. Bruzzone
(University of Genoa) for his valuable support on this
manuscript.
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Antonio Cimino took his degree in Management Engineering,
summa cum Laude, in September 2007 from the University of
Calabria. He is currently PhD student at the Mechanical
Department of University of Calabria. He has published more than
20 papers on international journals and conferences. His research
activities concern the integration of ergonomic standards, work
measurement techniques, artificial intelligence techniques and
Modeling & Simulation tools for the effective workplace design.
Francesco Longo received his Ph.D. in Mechanical Engineering
from University of Calabria in January 2006. He is currently
Assistant Professor at the Mechanical Department of University of
Calabria and Director of the Modelling & Simulation Center –
Laboratory of Enterprise Solutions (MSC-LES). He has published
more than 80 papers on international journals and conferences.
His research interests include Modeling & Simulation tools for
training procedures in complex environment, supply chain
management and security. He is Associate Editor of the
“Simulation: Transaction of the society for Modeling & Simulation
International”. For the same journal he is Guest Editor of the
special issue on Advances of Modeling & Simulation in Supply
Chain and Industry. He is Guest Editor of the “International Journal
of Simulation and Process Modelling”, special issue on Industry
and Supply Chain: Technical, Economic and Environmental
Sustainability. He is Editor in Chief of the SCS M&S Newsletter
and he works as reviewer for different international journals.
Giovanni Mirabelli is currently Assistant Professor at the
Mechanical Department of University of Calabria. He has
published more than 60 papers on international journals and
conferences. His research interests include ergonomics, methods
and time measurement in manufacturing systems, production
systems maintenance and reliability, quality.