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This paper provides simulation practitioners and consumers with a grounding in how discrete-event simulation
software works. Topics include discrete-event systems; entities, resources, control elements and
operations; simulation runs; entity states; entity lists; and entity-list management. The implementation of
these generic ideas in AutoMod, SLX, and ExtendSim is described. The paper concludes with several examples
of “why it matters” for modelers to know how their simulation software works, including discussion
of AutoMod, SLX, and ExtendSim, and also SIMAN (Arena), ProModel, and GPSS/H.
In this section we discuss the motivation for developing this paper, and comment on the paper’s structure
and the terminology and conventions used in the paper.
A “black box” approach is often taken in teaching discrete-event simulation software. The external characteristics
of the software are studied, but the foundation on which the software is based is ignored or is
touched on only briefly (for lack of time). Choices made in implementing the foundation might not be
studied at all and related to step-by-step model execution. The modeler might then not be able to think
things through when faced with such needs as developing good approaches for modeling complex situations,
using interactive tools to come to an understanding of error conditions arising during model development,
and using interactive tools to verify that complex system logic has been modeled correctly. The
objective of this paper, then, is to describe the logical underpinnings of discrete-event simulation and illustrate
this material in terms of various implementations of discrete-event simulation software.
This paper is a revised version of an identically named paper from the 1996 Winter Simulation Conference
(Schriber and Brunner 1996). The 1996 paper covered the entity-list management rules and “why
it matters” for SIMAN, ProModel, and GPSS/H. A substantially expanded version of the 1996 paper containing
figures, flow charts, and additional explanation can be found in Schriber and Brunner (1998
1.2 Structure of the Paper
In Sections 2, 3 and 4 we comment on the nature of discrete-event simulation; basic simulation constructs
such as entities, resources, control elements, and operations; and model execution. Sections 5 and 6 deal
with entity states and entity-management data structures. Section 7 discusses three specific implementations
of entity management rules. Section 8 explores various aspects of “why it matters.”
978-1-4577-2109-0/11/$26.00 ©2011 IEEE 80
Schriber and Brunner
1.3 Terminology and Conventions
Throughout this paper we use terms that we define as well as terms reserved by the developers of particular
simulation tools. Terms we define are boldfaced on first use. Tool-specific terms are Capitalized or,
where appropriate, are spelled out in ALL CAPS.
2 ABOUT DISCRETE-EVENT SIMULATION
This section introduces the transaction-flow world view, and then discusses the nature of discrete-event
simulation and the logical challenges inherent in developing discrete-event simulation languages.
2.1 The Transaction-Flow World View
The “transaction-flow world view” often provides the basis for discrete-event simulation. In this world
view, a system is visualized as consisting of discrete units of traffic that move (“flow”) from point to
point in the system while competing with each other for the use of scarce resources. The units of traffic
are sometimes called “transactions,” giving rise to the phrase “transaction flow.”
Numerous systems fit the preceding description. Included are manufacturing, material handling, health
care, communication, and information processing systems, and queuing systems in general.
2.2 The Nature of Discrete-Event Simulation
A discrete-event simulation is one in which the state of a model changes at only a discrete, but possibly
random, set of simulated time points, called event times. Two or more traffic units often have to be manipulated
at one and the same time point. Such “simultaneous” movement of traffic is achieved by manipulating
units of traffic serially at that time point. This leads to logical complexities because it raises
questions about the order in which two or more units of traffic are to be processed at a given simulated
2.3 Discrete-Event Modeling Languages
The challenges faced by a modeler escalate for the designer of a modeling language. The designer must
take the logical requirements of discrete-event simulation into account in a generalized way. Choices and
tradeoffs exist. As a result, although discrete-event simulation languages are similar in broad terms, they
can differ in subtle but important particulars.
3 ENTITIES, RESOURCES, CONTROL ELEMENTS, AND OPERATIONS
The term entity is used here to designate a unit of traffic (a “transaction”). Entities instigate and respond
to events. An event is a happening that changes the state of a model. In a model of an order-filling system,
for example, the arrival of an order might be simulated by bringing an entity into the model.
There are two possible types of entities, here referred to as external entities and internal entities.
External entities are those whose creation and movement is explicitly arranged for by the modeler. In contrast,
internal entities are created and manipulated implicitly by the simulation software itself. For example,
internal entities might be used in some languages to simulate machine failures, whereas external entities
might be used to simulate the use of machines.
The term resource designates a system element that provides service (such as a drill, an automated
guided vehicle, or space in an input buffer). The users of resources are usually entities. (For example, a
work-in-process entity claims space in an input buffer, then captures an automated guided vehicle to
move it to the input buffer.) Resources are usually capacity-limited, so entities compete for their use and
sometimes must wait to use them, experiencing delay as a result.
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The term control element designates a construct that supports other types of delay or logical alternatives
based on a system’s state. Control elements can take the form of switches, counters, user data values,
and system data values built into the modeling tool. Complex control conditions might be based on
expressions that use arithmetic and/or Boolean combinations of control elements.
An operation is a step carried out by or on an entity while it moves through a system. The operations
applicable to a ship at a harbor might be these: arrive at the harbor; request a berth; capture a berth; request
a tugboat; capture a tugboat; get pulled into the berth; free the tugboat; load cargo; request a tugboat;
get pulled out of the berth; free the berth; get pulled into open water; free the tugboat; depart.
4 OVERVIEW OF MODEL EXECUTION
We now review the concepts of experiments, replications, and simulation runs and their anatomy
4.1 Experiments, Replications, and Runs
A simulation project is comprised of experiments. Experiments are differentiated by the use of alternatives
in a model’s logic and/or data. An alternate part-sequencing rule might be tried, for example, in the
model of a production system, and/or the quantity of various types of machines might be varied. Or the
number of loading and unloading berths in a harbor might be varied.
Each experiment consists of one or more replications (trials). A replication is a simulation that uses
the experiment’s model logic and data but its own unique set of random numbers, and so produces unique
statistical results that can be analyzed in a set of such replications.
A replication consists of initializing the model, running it until a run-ending condition is met, and reporting
results. This “running it” phase is called a run.
4.2 Inside a Run
During a run the simulation clock (an internally managed, stored data value) tracks the passage of simulated
time. The clock advances (automatically) in discrete steps (typically of unequal size) during the run.
After all possible actions have been taken at a given simulated time, the clock is advanced to the time of
the next earliest event. Then the appropriate actions are carried out at this new simulated time, etc.
In essence, the execution of a run therefore takes the form of a two-phase loop: “carry out all possible actions
at the current simulated time,” then “advance the simulated clock,” with these two phases repeated
again and again until a run-ending condition (usually modeler-specified) comes about. The two phases are
respectively called the Entity Movement Phase (EMP) and the Clock Update Phase (CUP) here.
5 ENTITY STATES
Entities migrate from state to state when moving through a model. The five states are described below
5.1 The Active State
The Active State is the state of the currently moving entity. Only one entity moves at any instant of wallclock
time. This entity moves until it encounters a delay of one type or other. It then migrates to an alternative
state. (Some other entity might then become the next active entity at that simulated time, etc.)
5.2 The Ready State
During an Entity Movement Phase there may be more than one entity ready to move, and yet entities can
only move (be in the Active State) one-by-one. The Ready State is the state of entities waiting to enter
the Active State during the current Entity Movement Phase.
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5.3 The Time-Delayed State
The Time-Delayed State is the state of entities waiting for a known future simulated time to be reached
so that they can then (re)enter the Ready State. A “part” entity is in a Time-Delayed State, for example,
while waiting for the future simulated time at which an operation being performed on it will end.
5.4 The Condition-Delayed State
The Condition-Delayed State is the state of entities delayed until some specified condition comes about,
e.g., a “part” entity might wait in the Condition-Delayed State until its turn comes to use a machine. Condition-
Delayed entities are transferred automatically from the Condition-Delayed state to the Ready State
when conditions permit.
5.5 The Dormant State
Sometimes it is desirable to put entities into a state from which no escape will be triggered automatically
by changes in model conditions. We call this state the Dormant State. Dormant-State entities rely on
modeler-supplied logic to transfer them from the Dormant State to the Ready State. Job-ticket entities
might be put into a Dormant State, for example, until an operator entity decides which job-ticket to pull
next, with consequent transfer of the job ticket to the Ready State.
6 ENTITY MANAGEMENT STRUCTURES
In our generic model, the following lists are used to organize entities in the five entity states.
6.1 The Active Entity
The active entity is resident in an unnamed “list” consisting only of the active entity. The Active-State entity
moves nonstop until encountering an (attempted) step that causes it to migrate to another entity state
(transfers it to another list) or removes it from the model. A Ready-State entity then becomes the next Active-
State entity. Eventually there are no more Ready-State entities at the current time. The EMP then
ends and a Clock Update Phase begins.
6.2 The Current Events List
Entities in the Ready State are kept in a single list we call the current events list (CEL). Entities migrate
to the CEL from the future events list, from delay lists, and from user-managed lists. (Each of these latter
lists is described below). In addition, entities (if any) cloned from the Active-State entity usually start
their existence on the CEL.
CEL Entities are generally ranked in FIFO order. Some software tools provide a built-in entity Priority
attribute used to order Entities on the CEL by priority (with priority ties resolved FIFO).
6.3 The Future Events List
Entities in the Time-Delayed State belong to a single list into which they are inserted at the beginning of
their time-based delay. This list, called the future events list (FEL) here, is usually ranked by increasing
entity move time. (Move time is the simulated time at which an entity is scheduled to try to move again.)
At the time of entity insertion into the FEL, the entity’s move time is calculated by adding the value of the
simulation clock to the known (sampled) duration of the time-based delay.
After an Entity Movement Phase is over, the Clock Update Phase sets (advances) the clock’s value to
the move time of the FEL’s highest ranked (smallest move time) entity. This entity is then transferred
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from the FEL to the CEL, migrating from the Time-Delayed State to the Ready State and setting the stage
for the next EMP to begin.
The preceding statement assumes there are not other entities on the FEL whose move time matches
the clock’s updated value. In the case of move-time ties, some tools will transfer all the time-tied entities
from the FEL to the CEL during a single CUP, whereas other tools take a “only one entity transfer per
Languages that provide internal entities usually use the FEL to support the timing requirements of
these entities. The FEL is then generally comprised of external and internal entities in such languages.
6.4 Delay Lists
Delay lists (there can be many) are lists of entities in the Condition-Delayed State. These entities are waiting
(e.g., waiting their turn to use a machine) until their delay is resolved so they can be transferred automatically
into the Ready State on the CEL. Delay lists, which are generally created automatically by the
simulation software, are managed by using related waiting or polled waiting.
If a delay can be related easily to model events that might resolve the delay, then related waiting can
be used to manage the delay list. For example, suppose a machine’s status changes from busy to idle. In
response, the software can automatically remove the next waiting entity from the appropriate delay list
and put it in the Ready State on the current events list. Related waiting is the prevalent approach used to
manage conditional delays.
If the delay condition is too complex to be related easily to events that might resolve it, polled waiting
can be used. With polled waiting the software checks routinely to see if entities can be transferred from
one or more delay lists to the Ready State. Complex delay conditions for which polled waiting can be useful
include Boolean combinations of state changes, e.g., a berth is empty and a tugboat is idle.
6.5 User-Managed Lists
User-managed lists (there can be many) are lists of entities in the Dormant State. The modeler must take
steps to establish such lists and usually must provide the logic needed to transfer entities to and from the
lists. (Except for very simple one-line, one-server service points in a system, the underlying software has
no way to know why entities have been put into user-managed lists in the first place, and therefore has no
plausible basis for automatically removing entities from such lists.)
7 IMPLEMENTATION IN THREE TOOLS
The tools chosen here for commentary on implementation particulars are AutoMod (Phillips 1997); SLX
(Henriksen 2000; Schulze 2008); and ExtendSim (Diamond et al. 2007; Krahl and Lamperti 1997). A
previous version of this paper (Schriber and Brunner 1996) covered SIMAN (Kelton 2009), ProModel
(ProModel Corporation 2008), and GPSS/H (Henriksen and Crain 2000) in similar detail. These six are
among forty eight reported in 2009 for discrete-event simulation (Swain 2009). We think these tools are
representative, but they clearly are not exhaustive.
AutoMod equivalents for the preceding generic terms are given in this section.
7.1.1 The Current Event List
The current events list is named the Current Event List in AutoMod. (See Table 1.) Cloned Loads, Loads
leaving the Future Event List due to a clock update, and Loads ordered off Order Lists are placed imme-
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diately on the CEL. The insertion rule is to rank first by priority (priority is a built-in attribute of every
Load) and then FIFO within priority.
Table 1: AutoMod Terminology
Generic Term AutoMod Equivalent
External Entity Load
Internal Entity Logical Load
Resource Resource; Queue; Block
Control Element Counter; Process Traffic Limit
Current Events List Current Event List
Future Events List Future Event List
Delay List Delay List; Condition Delay List; Load Ready List
User-Managed List Order List
When the CEL becomes empty, the Condition Delay List (see below) is checked, and Loads may be
transferred from there to the CEL. This continues until the CEL is empty and no more Loads can be transferred,
at which point the EMP is over and a CUP is initiated.
7.1.2 The Future Event List
The AutoMod Future Event List (FEL) is like future events lists in other tools. Loads arrive on the FEL in
the Time-Delayed State by executing a WAIT FOR statement. AutoMod allows the specification of time
units (day, hr, min, sec) in a WAIT FOR statement.
The AutoMod CUP removes multiple Loads from the FEL if they are tied for the earliest move time,
inserting them one by one into their appropriate place on the CEL.
There are also internal entities in AutoMod, called Logical Loads, that do things such as wait on the
FEL to trigger scheduled shift breaks.
7.1.3 Delay Lists
Delay Lists (DL’s) are lists of Loads waiting to claim capacity provided by a finite-capacity element (a
resource, i.e., Resource, Queue, Block; or a control element, i.e., Counter, or Traffic Limit Process). Each
finite capacity element within the model has a DL associated with it.
The waiting that results in these five cases is related waiting. Whenever capacity is freed, one Load
from the head of the element’s DL is tentatively placed on the CEL (but a placeholder is left on the DL).
When that Load is encountered during the EMP, it tries to claim the requested capacity. If it fails (for example
because it wants two units but only one is free), it is returned to the DL in its original place.
Immediately after this evaluation, if there is still any unused capacity, the next Load (if any) on the
DL is placed on the CEL. Processing of the active Load then continues. After each time a tentatively
placed Load is evaluated during the EMP, the existence of available capacity will cause another Load to
be removed from the DL.
7.1.4 The Condition Delay List
For conditional waiting apart from the five cases described above, AutoMod has a WAIT UNTIL statement
that results in polled waiting. WAIT UNTIL conditions can be compounded using Boolean operators.
If a Load executes a WAIT UNTIL and the condition is false, the Load is placed on a single global
AutoMod list called the Condition Delay List (CDL).
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After the Current Events List has been emptied, but before the simulation clock is updated, all Loads
on the Condition Delay List are moved to the Current Events List (actually, the Condition Delay List “becomes”
the Current Events List) if there has been a state change for at least one element of the same general
type (e.g. Queue) for which any Load on the Condition Delay list is waiting. (This mechanism is primarily
“polled,” where the polling process is triggered by a change in state of at least one element of the
same general type.)
If the Current Events List is now non-empty, the Entity Movement Phase resumes. If the condition for
which a CEL Load is waiting is not yet satisfied, AutoMod moves that Load from the Current Events List
back to the Condition Delay List. The Condition Delay List in some cases may be emptied multiple times
during one EMP until eventually the Current Events List has been emptied without having triggered a
state change related to any Load on the Condition Delay List. A Clock Update Phase then occurs.
Because of the potential for repetitive list migration with WAIT UNTIL, AutoMod’s vendor encourages
the use of Order Lists or other explicit control mechanisms to manage complex waiting.
7.1.5 Order Lists
AutoMod implements the Dormant State with Order Lists, which are user-managed lists of Loads. After a
Load puts itself onto an Order List (by executing a WAIT TO BE ORDERED Action), it can only be removed
by another Load (or another active model element such as a Vehicle) that executes an ORDER
Action. An ORDER Action may specify a quantity of Loads, or a condition that must be satisfied for a
given Load if that Load is to be ordered, or both. Loads successfully ordered are placed immediately on
the CEL (one at a time according to how they were chosen from the Order List, and ranked on the CEL by
priority (with priority ties resolved FIFO).
Order Lists can achieve performance improvements over Condition Delay List waiting because Order
Lists are never scanned except on explicit request.
AutoMod Order Lists offer several interesting wrinkles, including: the ability for an ordering Load to
place a back order if the ORDER quantity is not satisfied; the ability for a Load on an Order List to be ordered
to continue to the next Action instead of to a Process (this feature is useful for control handshaking);
and the ability to have a function called for each Load on the Order List (by using the
7.1.6 Other Lists
AutoMod has several material handling constructs that are integrated with Load movement. For vehicle
systems there are three other types of lists (not included in Table 1). Loads on Load Ready Lists (LRL)
(one list per vehicle system) are waiting to be picked up by a vehicle. Loads claimed (but not yet picked
up) by a vehicle reside on the vehicle’s Vehicle Claim List (VCL). Claimed loads that have been picked
up reside on the vehicle’s Vehicle Onboard List (VOL). The vehicle then becomes the active “load” and
moves among AutoMod’s lists (FEL, CEL, and possibly DL’s) rather than the Loads themselves.
SLX is a hierarchical language in which the built-in primitives are at a lower level than most simulation
languages, facilitating user (or developer) definition of the behavior of many system elements. This design
philosophy allows the SLX user (or developer) to create higher-level modeling tools whose constructs
have precisely defined modifiable behavior.
Equivalents for the generic terms for users of low-level SLX are given in Table 2. For example, SLX
uses Control Variables to act as Control Elements. The “control” modifier can be attached to a Variable
of any data type (integer, real, string, etc.). A Control Variable can be global, or it can be a local Variable
declared in an Object’s Class definition. (A Class-declared Variable is an attribute in other tools.)
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Table 2: SLX Terminology (Low-level)
Generic Term SLX Equivalent
External Entity Active Object and its Puck(s)
Internal Entity none
Resource Control Variable
Control Element Control Variable
Current Events List Current Events Chain
Future Events List Future Events List
Delay List Delay List
User-Managed List Set (see section 7.2.4)
SLX has two types of Objects: Active and Passive. The two are distinguished by the presence of actions
– executable Statements – in an Active Object’s Class definition. (Even without actions, Passive Objects
are useful in their own right, functioning as user-defined complex data structures.)
Table 3 shows how higher-level SLX-based tools might exploit the definitional capabilities of SLX.
Table 3: Tools Based on SLX
Generic Term SLX Equivalent
Resource Active or Passive Object
Control Element Active or Passive Object
Operation User-defined Statement
Delay List User-defined (based on Set)
User-Managed List User-defined (based on Set)
7.2.1 The Current Events Chain
The current events list is named the Current Events Chain (CEC) in SLX. The members of the CEC are
given the interesting name Puck. What is a Puck? SLX dissociates the concept of an Active Object (with
its associated local data) from a Puck, which is the “moving entity” that executes the actions, carries its
own entity scheduling data, and migrates from list to list. The effect of this dissociation is that a single
Object can “own” more than one Puck. All Pucks owned by a single Object share the Object’s local data
(attributes). For example, one application of this “local parallelism” feature (as compared with the “global
parallelism” offered by “clone” or “split” actions in other languages) is the use of a second Puck to simulate
a balk time while the original Puck is waiting for some condition. (If the condition comes about before
the balk time has elapsed, no balking occurs; otherwise, balking does occur.)
Activating a new Object creates one Puck and launches that Puck into action. In many cases no additional
Pucks are ever created for that Object, and the combination of an Active Object and its Puck forms
the equivalent of an entity. (Passive Objects have no actions and therefore own no Pucks.)
Newly activated Pucks, Pucks leaving the FEL due to a clock update, and reactivated Pucks (see
7.2.4) are placed on the CEC, ranked FIFO by priority. The CEC is empty when an EMP ends.
7.2.2 The Future Events List
The SLX Future Events List (FEL) is like future events lists in other tools. Pucks arrive on the FEL in the
Time-Delayed State by executing an ADVANCE statement.
The SLX CUP will remove multiple Pucks from the FEL if they are tied for the earliest move time,
inserting them one by one into their appropriate place on the CEC.
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Because the SLX kernel functionality does not include downtimes or even repetitive Puck generation
(scheduled arrivals), all activity on the SLX FEL unfolds as specified by the developer of the SLX model.
More generally, if a user is using a model (or is using a model builder) that contains higher-level primitives
defined by a developer, chances are that all kinds of things are going on behind the scenes, hidden
from the higher-level user’s view.
7.2.3 Delay Lists
Delay Lists (DL’s) are lists of Pucks waiting (via WAIT UNTIL) for state changes in any combination of
Control Variables and the simulation clock value. A Puck waiting for a compound condition involving
two or more Control Variables is listed on more than one DL. All higher-level constructs defined by developers
can use this mechanism. Each Control Variable (which may be a local Variable, in which case
there is one for each Object in the Class) has a separate DL associated with it.
A DL is ranked by order of insertion. All pucks on a DL are removed whenever the associated Control
Variable changes value and are inserted one at a time into the CEC. Removed Pucks that are waiting
on compound conditions are also tentatively removed from each of the other Delay Lists to which they
belong. As these Pucks are encountered on the CEC during the EMP, those failing to pass their WAIT
UNTIL are returned to the Delay List(s) for those Control Variables still contributing to the falseness of
For conditions that include a clock reference, the Puck is inserted if necessary into the FEL, subject to
early removal from the FEL if the condition becomes true due to other Control Variable changes.
This low-level related waiting mechanism based on Control Variables is the default SLX approach to
modeling all types of simple or compound Condition-Delayed states.
7.2.4 Sets and User-Managed Waiting
SLX handles the Dormant State in a unique way. Instead of moving the Puck from the active state to a user-
managed list and suspending it, all in the same operation, SLX breaks this operation into two pieces.
First, the Puck usually joins a Set. But joining a Set does not automatically suspend the Puck. A Puck
can belong to any number of Sets. Set membership merely provides any other Puck with access to the
To go into the Dormant state, a Puck executes a WAIT statement. It then is suspended indefinitely,
outside of any particular list, until another Puck identifies the waiting Puck and executes a
REACTIVATE statement for it. Often this other Puck is scanning a Set to find the Puck to
REACTIVATE, but a Set is not exactly the same as a user-managed list in our terminology. A Dormantstate
Puck might be a member of no Sets (as long as a pointer tjo it has been stored somewhere) or of one
or more Sets.
An SLX developer can easily define a user-managed list construct, using Sets, WAIT, and
REACTIVATE as building blocks, that mimics those of other languages or offers its own unique features.
ExtendSim (originally named Extend) uses a message-based architecture for discrete-event simulation.
Various types of messages are used to schedule events, propel Items (Entities) through a model, enforce
the logic incorporated into a model, and force computation. The senders and receivers of messages are
Blocks (Operations), including the Executive Block (master controller). In ExtendSim, it is Block execution
that is scheduled. (When a Block executes, for example, this can trigger the sending of messages
back and forth among Blocks, with the effect of moving an Item along its Block-based path in a model.)
Table 4 summarizes ExtendSim equivalents for the terms introduced in the earlier generic discussion.
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Table 4: ExtendSim Terminology
Generic Term ExtendSim Equivalent
External Entity Item
Internal Entity none
Resource Resource; Resource Pool
Control Element Block Dialog
Current Events List Next Times Array
and Current Events Array
Future Events List Time Array
Delay List List of Items Resident in a Pre-Programmed Block
User-Managed List List of Items Resident in a User-Programmed Block
Blocks are ExtendSim’s basic modeling construct. Each Block has an icon, message-passing connectors,
dialog capability, and behavior-defining code. Residence Blocks can hold Items while simulated time
goes by, whereas Passing Blocks cannot. (Items go through Passing Blocks in zero simulated time.) Models
can be constructed by selecting pre-programmed Blocks from ExtendSim’s Block libraries. The modeler
can also modify the source code given for library Blocks. (All Blocks in the base version of ExtendSim
are open source.) Finally, the modeler can create customized Blocks from scratch (userprogrammed
Blocks) using development tools that ExtendSim provides.
7.3.2 The Time Array
ExtendSim uses a Time Array to schedule future Block executions. For a given model, the Time Array
contains one or more elements for each Block. A Time Array element records the future time for which
execution of that Block has been scheduled. (The potential for a Block to have more than one Time Array
element is an enhancement in Version 7 of the language. This feature can be useful when a Block has
multiple, dissimilar events, as for example in conveyor modeling.)
Blocks not currently scheduled for future execution are temporarily “blacked out” by recording arbitrarily
large time values for them in the Time Array.
Residence Blocks that can hold multiple Items manage the corresponding event times internally, with
only the earliest of the Block’s event times kept in the Time Array.
Block execution can result in scheduling future Block executions. For example, if messages are
passed that result in an Item entering a unit-capacity Residence Block designed to hold the Item until a
sampled amount of simulated time has elapsed, then the Time Array entry for that Block will have its value
The number of Blocks in a given model is constant, which means the Time Array is of fixed and relatively
small size. Because of its small size, the Time Array is searched to find imminent event time; it is
not kept in sort order. This makes it straightforward for a Block to change its event time because no
searching of the event list is required.
7.3.3 The Next Times and Current Events Arrays
The Next Times Array is used to manage the execution of Blocks whose execution has been scheduled
via the Time Array. The Next Times Array is populated just prior to a Block Execution Phase (ExtendSim’s
equivalent of an Entity Movement Phase) as follows. At each Clock Update Phase, the Time
Array is searched to find the earliest future time at which a Block execution has been scheduled. Identifiers
for the corresponding Block (or Blocks, in case of time ties) is (or are) then put into the Next Times
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Array. The Block Execution Phase (BEP) then begins, with the Executive messaging the most highly
qualified Block in the Next Times array to start its execution.
The Current Events Array is used to manage the resumption of execution of Blocks whose execution
has been temporarily suspended during the course of a Block Execution Phase. For example, suppose a
Block sends a message, and the receiving Block replies (returns control) immediately to the sending
Block (even though the receiving Block still has to do additional processing at the simulated time in question).
In this case, the receiving Block’s identifier is added to the Current Events Array. When the sending
Block is finished executing, the Executive sends a message to the most highly qualified Block in the Current
Events Array to resume its execution. Eventually, the Current Events Array becomes empty. Then the
Executive turns again to the Next Times Array, sending a message to the most highly qualified Block to
During a Block Execution Phase, it is possible for Blocks to schedule themselves to be executed at
the current simulated time (that is, during the ongoing BEP). The Current Events Array comes into play
here, too, to manage the execution of Blocks in such cases.
For example, if a capacity-constrained Block becomes non-full as a result of some other Block’s execution,
the non-full Block puts its identifier into the Current Events Array. The Executive will later (but at
the same simulated time) send a message to the Block to start executing. The Block will then try to pull
into itself Items (if any) that have been waiting to enter the Block. (In ExtendSim, Items can be both
pulled and pushed through a model.)
When the Current Events Array and the Next Times Array both become empty, this brings ExtendSim’s
Block Execution Phase to an end. Then the next CUP and BEP take place, repeating until a
simulation-ending condition is satisfied.
7.3.4 Delay Lists
Delay lists are comprised of Items delayed in Residence Blocks, waiting their turn to be pulled or pushed
into their next Block(s). Message passing is used to accomplish the pulling and pushing when model conditions
permit. ExtendSim provides related-waiting management of delay lists based on user-specified
FIFO, LIFO, Priority, Attribute, Reneging, and Matching alternatives.
Waiting for the resolution of compound conditions is normally achieved in ExtendSim by appropriately
combining Blocks and exploiting ExtendSim’s message-based architecture. We view this here as a
form of related waiting, because it is a change in an underlying value that triggers a re-evaluation of the
condition that brought about the waiting in the first place.
Because of ExtendSim’s messaging architecture, polled waiting is generally not necessary. A message
is sent when a value changes and any conditions are evaluated at that moment. Waiting for a clock
based event can be achieved by using a Block that schedules events, e.g., Shift; Lookup Table; Equation.
These Blocks send a message at scheduled times. Polled waiting is available, however, with use of the
Gate block and selection of its "Check demand at each event" option.
7.3.5 User-Managed Lists
The modeler can work with user-programmed Blocks to create and manage lists of the modeler’s own design.
The code for custom blocks can be written to achieve the modeler’s objectives in this regard, just as
the code for ExtendSim’s pre-programmed Blocks has been written to specify the behavior of those
Blocks. ExtendSim provides functions that can be used by Blocks to share lists (arrays) with other
Blocks, further supporting customized list management in models.
Schriber and Brunner
8 WHY IT MATTERS
In sections 8.1-8.5 we describe situations that reveal some practical differences in implementation particulars
among SIMAN, ProModel (Version 3), GPSS/H, AutoMod, SLX, and ExtendSim. None of the
alternative approaches mentioned is either intrinsically “right” or “wrong.” The modeler simply must be
aware of the alternative in effect in the simulation software being used and work with it to produce the
desired outcome. (Otherwise, it is possible to mis-model a situation and perhaps not become aware of it.)
In section 8.6, we comment on how knowledge of software internals is needed to make effective use
of model checkout tools. Finally, in section 8.7, we point out that knowledge of internals aids in understanding
8.1 Trying to Re-capture a Resource Immediately
Suppose a job in a flexible job shop releases a machine (for which other less qualified jobs are waiting),
then as its next step decides to re-capture that machine. Will the job re-capture the machine immediately?
Of interest here is the order of events following the giving up of a resource. There are at least three alternatives:
(1) Coupled with the giving up of the resource is the immediate choosing of the next user of
the resource, without the releasing entity having yet become a contender for the resource. (2) The choosing
of the next user of the resource is deferred until the releasing entity has become a contender. (3)
Without paying heed to other contenders, the releasing entity recaptures the resource immediately.
SIMAN and ExtendSim implement (1). ProModel implements (2). GPSS/H and AutoMod implement
(3) by default. In SLX, using a low-level Control Variable as the resource state, the result is also (3). (Developers
can implement higher-level resource constructs in SLX that behave in any of the three ways.)
8.2 The First in Line is Still Delayed
Suppose two Condition-Delayed entities are waiting in a delay list because no units of a particular resource
are idle. Suppose the first entity needs two units of the resource, whereas the second entity only
needs one unit. Now assume that one unit of the resource becomes idle. The needs of the first list entity
cannot yet be satisfied, but the needs of the second entity can. What will happen?
There are at least three possible alternatives: (1) Neither entity claims the idle resource unit. (2) The
first entity claims the one idle resource unit and waits for a second unit. (3) The second entity claims the
idle resource unit and migrates to the Ready State.
As in Section 8.1, each of these alternatives comes into play in the tools considered here. SIMAN
(SEIZE) and ProModel (GET or USE) implement (1) and (2) respectively, by default. AutoMod (GET or
USE), GPSS/H (ENTER or TEST), and SLX (WAIT UNTIL on a Control Variable) implement (3) by default.
ExtendSim also implements (3) by default. But ExtendSim gives the modeler the choice of locally
implementing (1) for resources specified by the modeler. The modeler does this by checking an “Only allocate
resource pool to the highest ranked Item” option for each such resource.
8.3 Yielding Control Temporarily
Suppose the active entity wants to give control to one or more Ready-State entities, but then needs to become
the active entity again before the simulation clock has been advanced. This scenario might come into
play, for example, if the active entity has opened a switch permitting a set of other entities to move past
a point in the model, and then needs to re-close the switch after the forward movement of the other entities
has been accomplished. (Perhaps a group of identically flavored cartons of ice cream is to be transferred
from an accumulation point to a conveyor leading to a one-flavor-per-box packing operation.)
In SIMAN and AutoMod, the effect can be accomplished approximately with a DELAY (SIMAN) or
WAIT FOR (AutoMod) that puts the active entity into a Time-Delayed State for an arbitrarily short but
non-zero simulated time.
Schriber and Brunner
In ProModel, “WAIT 0” can be used to put the active entity back on the FEL. It will be returned later
(at the same simulated time) by the CUP to the Active State.
In GPSS/H, the active Xact (Transaction) can execute a YIELD Block to migrate immediately from
the Active State to the Ready State (positioned last in its priority class) and force a restart of the CEC
scan. Higher-ranked CEC Xacts are then given a chance to become active before the yielding Xact becomes
active again at the same simulated time.
In SLX there is also a YIELD statement. A normal YIELD shifts the active Puck to the back of its
priority class on the CEC and picks up the next Puck. It is also possible to YIELD to a specific other Puck
that is on the CEC, in which case the active Puck is not shifted.
In ExtendSim, a message is sent out through the appropriate Block connector when an Item moves into
or out of a Block. This message propagates to other connected Blocks, perhaps changing system status
or moving Items from one Block to another as a result. When the originating Block eventually receives
the reply, it continues processing the original Item. Hence, “yield and then eventually resume” is part of
the fabric of ExtendSim’s message-based architecture.
8.4 Conditions Involving the Clock
Every language provides a time-delay capability for FEL waiting. This works well when an entity needs
to wait until a known clock value has been reached. But what if an entity needs to wait for a compound
condition involving the clock, such as “wait until my input buffer is empty or it is exactly 5:00 PM?”
A typical approach to this is to clone a dummy (“shadow”) entity to do the time-based waiting. Management
of such dummy entities can be cumbersome, particularly for very complex rules. ProModel does
not use polled waiting, so a dummy entity would be the best approach available. (Otherwise, the condition
would not be checked until the other component of the compound condition had a value change.) ExtendSim
also does not use polled waiting, so a similar situation applies for ExtendSim.
Even when a polled waiting mechanism is present, if a single entity tries to wait on a compound condition
involving the clock, a similar problem can arise. This is because the next polling time may not
match the target clock time. SIMAN and AutoMod detect the truth of compound conditions via their endof-
EMP polling mechanisms. GPSS/H also detects the truth via its version of polled waiting (refusalmode
TEST). But in the absence of a clone that waits on the FEL until exactly 5:00 PM (i.e., the approach
recommended above for ProModel and ExtendSim), all three of those tools are subject to the possibility
that the first EMP that finds the condition true occurs when the clock has a value greater than 5:00 PM.
SLX recognizes the clock as a related wait-until target. A WAIT UNTIL using a future clock value in
a way that contributes to the falseness of the condition will cause the Puck to be scheduled onto the FEL
to force an EMP at the precise time referenced. This solves the greater-than-the-desired-time problem.
Note that this Puck may also be waiting on one or more delay lists.
8.5 Mixed-Mode Waiting
Suppose many entities are waiting to capture a particular resource, while a user-created controller entity is
waiting for the condition “shift status is off-shift and number waiting is less than six and resource is not
currently in use” to take some action (such as shutting the resource down, in languages that allow userdefined
entities to shut down resources; or printing a status message). How can we guarantee that the controller
will be able to cut in front of the waiting entities at the appropriate simulated time (before the idle
resource is recaptured)?
One way to handle this would be through entity priorities in languages that offer this mechanism.
However, as described below, that might not work even if the controller has relatively high priority.
The key issue is the method used to implement the waiting. If it is “related” for the entities waiting to
capture the resource and “polled” for the controller entity waiting for the compound condition (this is
what we mean by the term “mixed-mode waiting”), things can get complicated. Every time the resource
Schriber and Brunner
becomes free, a new entity will be selected from a delay list immediately in SIMAN and via the CEL in
AutoMod, in both cases preceding the end-of-EMP checking for polled wait conditions (and thereby ignoring
the entity priority of the controller). There are ways to work around this if desired, such as using a
different type of operation to force a polled wait for entities wishing to use the resource.
In GPSS/H, using a high-priority controller Xact at a refusal-mode TEST Block, the controller waits
at the front of the CEC. The Facility RELEASE triggers a scan restart and the controller does its job.
In ProModel there is no polled waiting but there can be related waiting on compound conditions involving
Variables. Variables would have to be defined and manipulated for each element of the Boolean
condition and, to assure equal competition, the entities waiting to capture the resource might also have to
use WAIT UNTIL instead of GET or USE. Another possibility with ProModel would be to have the entity
that frees the resource do some state-checking right away (in effect becoming a surrogate for the controller).
This is possible because of the deferred-selection method used by ProModel (see Section 8.2).
In the related waiting of SLX, a Puck awaiting a compound condition will be registered on the delay
lists of those (and only those) Control Variables that are contributing to the falseness of the condition.
The SLX architecture (in which only global or local Control Variables and the clock can be referenced in
any sort of conditional wait at the lowest level) assures that there will already be Variables underlying the
state changes being monitored. The modeler only needs to define them as Control Variables.
As with ProModel and SLX, ExtendSim would use related waiting to detect and immediately respond
to a change in the compound condition. The desired effect is achieved in ExtendSim by use of a Program
Block, which can be used to issue a message to create a controller Item with its priority set to a value that
assures it will be processed before other Items are processed at a specified simulated time. This Item
would wait in ExtendSim’s related-waiting fashion (using connectors to monitor the state changes).
8.6 Interactive Model Verification
We now comment briefly on why a detailed understanding of “how simulation software works” supports
interactive probing of simulation-model behavior.
In general, simulation models can be run interactively or in batch mode. Interactive runs are of use in
checking out (verifying) model logic during model building and in troubleshooting a model when execution
errors occur. Batch mode is then used to make production runs.
Interactive runs put a magnifying glass on a simulation while it executes. The modeler can follow the
active entity step by step and display the current and future events lists and the delay and user-managed
lists as well as other aspects of the model. These activities yield valuable insights into model behavior for
the modeler who knows the underlying concepts. Without such knowledge, the modeler might not take
full advantage of the software’s interactive tools or, worse yet, might not even use the tools.
8.7 Performance Issues
Simulation experiments can consume substantial amounts of computer time. Other things equal (including
the model builder’s skill), computer-time requirements depend on the design and implementation of the
software used to build models. This dependency can be understood with knowledge of “how simulation
software works.” For example, consider user-managed lists vs. related waiting in models in which large
numbers of entities contend for a resource. Performance is an important enough issue to motivate some
simulation software (e.g., ExtendSim; SLX) to supply performance profilers which, for example, can produce
histograms showing where CPU time is spent during model execution.
Much of the information in this paper was provided by software-vendor personnel. The authors gratefully
acknowledge the support of David T. Sturrock, Deborah A. Sadowski, C. Dennis Pegden and Vivek
Schriber and Brunner
Bapat (SIMAN); Charles Harrell (ProModel); Kenneth Farnsworth and Tyler Phillips (AutoMod); Robert
C. Crain and James O. Henriksen (GPSS/H and SLX); and David Krahl (ExtendSim).
Diamond, B., J. S. Lamperti, D. Krahl, A. Nastasi, and C. Damiron. 2007. ExtendSim User Guide. San Jose,
California: Imagine That Incorporated.
Henriksen, J. O. 2000. SLX: The X is for Extensibility. In Proceedings of the 2000 Winter Simulation
Conference, ed. J. A. Joines et al, 183-190. Baltimore, Maryland. INFORMS.
Henriksen, J. O., and R. C. Crain. 2000. GPSS/H: A 23-year retrospective view. In Proceedings of the
2000 Winter Simulation Conference, ed. J. A. Joines et al, 177-182. Baltimore, Maryland. INFORMS.
Kelton, D., R. Sadowski, and N. Swets. 2009. Simulation with Arena, 5th Ed. New York, New York:
Krahl, D., and J. S. Lamperti. 1997. A Message-Based Discrete Event Simulation Architecture. In Proceedings
of the 1997 Winter Simulation Conference, ed. S. Andradottir, et al, 1361-1367. Baltimore,
Phillips, T. 1997. Know your AutoMod Current Events. In AutoFlash, 10(7). Bountiful, Utah: AutoSimulations,
ProModel Corporation. 2008. ProModel Version 7.5 User Guide. Orem, UT: ProModel Corporation.
Schriber, T. J., and D. T. Brunner. 1996. Inside Simulation Software: How It Works and Why It Matters.
(This is one of ten “landmark papers” given during the first forty years of the Winter Simulation Conference
as identified at the 40th Anniversary WSC in 2007.) In Proceedings of the 1996 Winter Simulation
Conference, ed. J. Charnes et al, 23-30. Baltimore, Maryland. INFORMS.
Schriber, T. J. and D. T. Brunner. 1998. How Discrete-Event Simulation Software Works. Chapter 24 in
Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice, ed. J.
Banks. New York, New York: John Wiley & Sons.
Schulze, T. 2008. Simulation Needs SLX. Annandale, Virginia: Wolverine Software.
Swain, J. J. 2009. To Boldly Go (Biennial survey of discrete-event simulation software). OR/MS Today
36(9): 32-43. Baltimore, Maryland: INFORMS.
THOMAS J. SCHRIBER is a Professor of Business Information Technology at The University of Michigan.
He is a recipient of the INFORMS Simulation Society’s Lifetime Professional Achievement Award
and of the Society’s Distinguished Service Award. He is a Fellow of the Decision Sciences Institute and
author of Simulation Using GPSS. A former WSC Program Chair, he served ten years on the WSC Board
of Directors. He is a member of ASIM (the German-language simulation society), the Decision Sciences
Institute, the Institute of Industrial Engineers, and INFORMS, and Who’s Who in America. His email and
web addresses are: firstname.lastname@example.org and http://www.bus.umich.edu/.
DANIEL T. BRUNNER is Vice President of Strategic Solutions Design at Kiva Systems, Inc., a provider
of innovative material handling systems for order fulfillment and warehousing. He holds a BSEE from
Purdue University and an MBA from The University of Michigan. He has served as a WSC Business
Chair and General Chair and as Transportation Applications Track Coordinator. His email and web addresses
are: email@example.com and http://www.kivasystems.com.
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David C. Yen and William S. Davis
The ultimate objective of natural language processing is to allow people to communicate with computers in much the same way they communicate with each other. This chapter briefly introduces key natural language processing concepts and terminology.
Natural language processing removes one of the key obstacles that keeps some people from using computers. More specifically, natural language processing facilitates access to a database or a knowledge base, provides a friendly user interface, facilitates language translation and conversion, and increases user productivity by supporting English-like input.
As of mid-1998 when this chapter was written, natural language processing was not yet capable of supporting true conversational input. Most commercially available software limits the number of different users and/or such parameters as the user’s vocabulary, syntax, or speed, and free-form English input must occasionally be supplemented with commands. Most natural language processing software is designed to locate key words first and then interpret the meaning of a sentence or a phrase, which increases programming time and program execution time. Additionally, special equipment is needed to support natural language processing.
Natural language processing is a major area of research within the field of artificial intelligence. It is closely related (either as a front end or a user interface) with expert systems (Chapter 7), and shows great promise as a user interface (Chapter 48). State transition diagrams (Chapter 30) are sometimes used to model natural language processing tasks.
The ultimate objective of natural language processing is to allow people to communicate with computers in much the same way they communicate with each other. This chapter briefly introduces key natural language processing concepts and terminology. A detailed discussion of the underlying technology is beyond the scope of this book.
Natural language processing starts with the input of a string of plain English words (Figure 68.1). The first phase in the process is word recognition. The objective is to restructure the input string as a series of noun phrases, verb phrases, prepositional phrases, adjective phrases, and so on. A state transition diagram (Chapter 30) is sometimes used to model the process.
Next the words and phrases are analyzed to check the integrity of the sentences and to clarify any ambiguities. The knowledge base stores general knowledge (words, linguistic concepts, etc.) and application-specific knowledge. A lexical analyzer is a routine that performs semantic analysis, checking every word in a sentence against the correct spellings stored in the knowledge base and listing all the possible alternative meanings for the sentence. If necessary, an expert system is consulted to deduce the meanings of ambiguous terms and expressions based on context, questions asked earlier in the session, organization-specific rules, and other factors stored in the knowledge base.
Once the words are properly defined, a parser routine performs syntactic analysis, essentially diagramming the sentence to form a parse tree. Finally, during the natural language implementation phase, a generator outputs one or more commands based on the meaning deduced from the word meanings and the parse tree. For example, a plain English query might be converted to a set of SQL commands. The computer then executes the commands.
The natural language processing routine is typically visualized as a shell. The user communicates with the shell by entering plain English character strings. The shell translates the plain English strings into the appropriate commands and passes the commands to an application program. Using a common shell makes more sense than duplicating the same complex logic in multiple application programs.
Speech recognition is an extension of natural language processing. The idea is to use a speech recognition routine (or a chip) to break continuous speech into a string of words, input the string into a natural language processing routine, and then pass the resulting commands to an application program.
One problem with speech recognition is that human language is imprecise and many words have multiple meanings that depend on context. Add multiple languages, dialects, and accents, and the problem becomes very complex. Additionally, few people are skilled at issuing orders or using language with precision.
Natural language processing can support several types of translation. Language-to-language systems translate between two languages; English and Chinese, for example. Compiler and interpreter systems convert English-like commands into executable machine or low-level language codes. Code-to-code translators are common in word processing software, supporting conversions between Microsoft Word, ASCII, and WordPerfect formats, for example.
As the term implies, grammar analysis systems are used to check spelling and grammar. For example, the grammar analysis facility in Microsoft Word for Office 97 continuously underlines spelling errors in red and grammatical errors in green as the user types. In addition to highlighting misspellings, commonly misused words, awkward sentence structures, awkward phrases, and incorrect punctuation, a sophisticated grammar analysis system can also provide substitutes for specific words, determine the reading level of a document, and provide status and statistical data for further analysis.
Record management systems read the contents of records (received, stored, and transmitted), analyze the contents, sort the records into proper categories, and add meaningful indexes or key words and phrases for future reference.
A natural language processing system can serve as a user interface to a database system, an expert system, or a specific application. A SQL command generator is a good example of a database system interface. Natural language interfaces show great promise for expert systems, and considerable research has already been done. Other natural language interfaces are used in data communications, manufacturing, and office automation.
Natural language processing will play an important role in future robotic systems. Robotics combines such features as speech recognition, natural language processing, natural language translation, image processing, and pattern recognition, and is beyond the scope of this book.
Expert system (knowledge-based system) —
A computer program that emulates the thought process of a human expert.
A routine that outputs one or more commands that the computer can execute.
Knowledge base —
A collection of data, algorithms, and heuristic rules that forms the core of an expert system.
Lexical analyzer —
A routine that performs semantic analysis, checking every word in a sentence against the correct spellings stored in the knowledge base and listing all the possible alternative meanings for the sentence.
Natural language processing shell —
A natural language processing user interface. The user communicates with the shell by entering plain English character strings. The shell translates the plain English strings into the appropriate commands and passes the commands to an application program.
Parse tree —
A hierarchical representation of words (conceptually similar to a diagrammed sentence) arranged in a form that allows a computer program to trace relationships and infer meanings.
A routine that performs syntactic analysis, essentially diagramming a sentence to form a parse tree.
Semantic analysis —
A technique in which the system determines the meaning of each word by looking it up in a dictionary or a knowledge base.
Speech recognition —
An extension of natural language processing that uses a speech recognition routine (or a chip) to break continuous speech into a string of words, inputs the string to a natural language processing routine, and then passes the resulting commands to an application program.
Syntactic analysis —
A technique that allows a parser routine to, essentially, diagram a sentence to form a parse tree.
Word recognition —
The process of restructuring an input string into a series of noun phrases, verb phrases, prepositional phrases, adjective phrases, and so on.
Dragon Systems’ Naturally Speaking and IBM’s ViaVoice Gold are voice recognition software packages that might be used to support a speech recognition system. Other examples include Intellect from Artificial Intelligence Corp., RAMIS II English from Mathematica, Inc., Spock from Frey Associates, Inc., and NaturalLink from Texas Instruments.
1. Allen J., Natural Language Understanding, Benjamin/Cummings, Redwood City, CA, 1987.
2. Davis, W. S., Computers and Information Systems: An Introduction, West, Minneapolis, MN, 1997.
3. O’Shea, T. and Eisenstadt, M., Artificial Intelligence: Tools, Techniques, and Applications, Harper & Row, New York, 1984.
4. Rauch-Hindin, W. B., A Guide to Commercial Artificial Intelligence: Fundamentals and Real World Applications, Prentice-Hall, Englewood Cliffs, NJ, 1988.
5. Turban E., Expert Systems and Applied Artificial Intelligence, Macmillan, New York, 1992.
6. Williamson M., Artificial Intelligence for Microcomputers: The Guide for Business Decision Makers, Brady Communications, New York, 1986.
7. Winston, P. H. and Prendergast, K. A., The AI Business: Commercial Uses of Artificial Intelligence, MIT Press, Cambridge, MA, 1984.
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ورود به ويندوز xp بدون پسورد
این دفعه میخوام به شما دو راه رد کردن پسورد ویندوز XP و نفوذ به سیستم رو آموزش بدم.البته شاید یه کم قدیمی باشه ولی خوب ممکنه یه سری از دوستان این دو راه رو بلد نباشند.راستی من اين آموزش رو با اين انگيزه ياد ميدم که شما پسورد USER خودتون رو فراموش کرديد و اصلا هم قصد نفوذ به سيستم کسی رو نداريد .....
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نشریه ABC در گزارشی ۱۰ گزینه آسیب پذیر را که روزانه در معرض حمله هکرهای قرار داشته و هکرها به راحتی توانایی نفوذ به آنها را دارند، معرفی کرده است
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[ 89/02/26 ] [ 8:1 بعد از ظهر ] [ محمد رسول فیروزی ]
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[ 89/02/26 ] [ 8:1 بعد از ظهر ] [ محمد رسول فیروزی ]
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