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SIMULATION MODELING AND ANALYSIS
WITH ARENA
T. Altiok and B. Melamed
Chapter 5
Arena Basics
Altiok / Melamed Simulation Modeling and Analysis with Arena
Chapter 5
1
The Arena Simulation System
• Arena is a powerful simulation environment
• consists of modeling object templates, called modules,
and transactions that move among them, called entities
• has a visual front-end
• built around SIMAN block-oriented language constructs and other facilities
• SIMAN consists of two classes of objects:
• Blocks are basic logic constructs that represent operations, such as SEIZE
blocks that model seizing of a facility by a transaction entity, while
RELEASE blocks release the facility for use by other transaction entities
• Elements are objects that represent facilities, such as RESOURCES and
QUEUES
• Arena modules are are selected from template panels
• examples: Basic Process, Advanced Process, Advanced Transfer
• Arena modules are high-level constructs that functionally
equivalent to sets of SIMAN blocks and/or elements,
and internally are built of SIMAN blocks and/or elements
Altiok / Melamed Simulation Modeling and Analysis with Arena
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The Arena Home Screen
Title Bar
Standard
Toolbar
Menu Bar
Run Interaction Toolbar
Drawing Toolbar
Template
Panel
Project Bar
Toolbar
View Toolbar
Animate
Toolbar
Animate
Transfer
Toolbar
Model Window Canvas
Flowchart View
Model Window Canvas
Spreadsheet View
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Example: A Simple Workstation
• Consider a single workstation, known in queueing theory as the
M/M/1 queue, where
• there is a machine with an infinite buffer in front of it
• jobs arrive randomly and wait in the buffer while the machine is busy
• jobs are processed by the machine and then leave the system
• job inter-arrival times are exponentially distributed with mean 30 minutes
• job processing times are exponentially distributed with mean 24 minutes
Create 1
Process 1
0
Dispose 1
0
0
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Simulation Objects and Actions
• Simulating the above workstation calls for the following actions:
• jobs are created, one at a time, according to their arrival distribution
• if the machine is busy processing another job, then the arriving job is
queued in the buffer
• when a job advances to the head of the buffer, it seizes the machine for
processing once it becomes available, and holds it for a time period,
sampled from its processing-time distribution
• on process completion, the job departs the machine and is removed from
the system
• Simulation objects and their actions and interactions
• are modeled by Arena modules
• parameterized by associated dialog boxes
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Create Module
• The Create module generates a stream of arrivals of Arena
entities (jobs, people, messages, etc.)
Dialog box for a Create module
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Create Module (Cont.)
• The Type pull-down menu for the Time Between Arrivals field
offers the following options:
• Random (exponential inter-arrival times with mean given in the Value
field)
• Schedule (allows the user to create arrival schedules using the Schedule
module from the Basic Process template panel
• Constant (specifies fixed inter arrival times)
• Expression (any type of inter-arrival time pattern specified by an Arena
expression, including Arena distributions)
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Process Module
• The Process module processes (serves) Arena entities
Dialog box for a Process module
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Process Module (Cont.)
• The Action field option, selected from the pull-down menu,
is Seize Delay Release, which stands for a sequence of
SEIZE, DELAY and RELEASE SIMAN blocks
• SEIZE and RELEASE blocks are used to model contention for a resource
possessing a capacity (e.g., machines)
• when resource capacity is exhausted, the entities contending for the
resource must wait until the resource is released
• thus, the SEIZE block operates like a gate between entities and a resource
• the processing (holding) time of a resource (called Machine in the example)
by an entity is specified via a DELAY block within the Process module
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Dispose Module
• The Dispose module implements an entity “sunset” mechanism
• entities that enter it are simply discarded
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Arena Files
• Whenever an Arena model is saved, the model is placed in a file
with a .doe extension (e.g., mymodel.doe)
• whenever an Arena model (say, mymodel.doe) is checked using
the Check Model option in the Run Menu or any run option in it,
Arena automatically creates a number of files:
• mymodel.p (program file)
• mymodel.mdb (Access database file)
• mymodel.err (errors file)
• mymodel.opw (model components file)
• mymodel.out (SIMAN output report file)
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Arena Simulation Results
• The end-result of a simulation run is a set of requisite statistics,
referred to as run results, such as
• mean waiting times
• buffer content probabilities
• resource utilization
• Arena provides a considerable number of default statistics
in a report that is automatically generated at the end of a
simulation run in Arena reports
• Additional statistics can be obtained by adding statistics
collection modules in the model, such as
• Record (Basic Process template panel)
• Statistic (Advanced Process template panel)
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Example: a Resources Report
Resources statistics from a single replication
of the simple workstation model
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Example: a Queues Report
Queues statistics from a single replication
of the simple workstation model
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Arena Data Storage Objects
• Arena variables are user-defined global data storage objects
used to store and modify state information either at run
initialization, or in the course of a run
• such (global) variables are visible everywhere in the model, namely,
they can be accessed, examined and modified from every component of the
model
• in an Arena program, variables are typically examined in Decide modules
and modified in Assign modules
• Arena Attributes are data storage objects associated with entities
• unlike variables, attributes are local to entities in the sense that each
instance of an entity has its own copy of the attributes
• Arena expressions can be viewed as specialized variables
that store the value of an associated formula (expression)
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Arena Statistics Collection
• Statistics collection via the Statistic module
• Time-Persistent statistics are time-average statistics (e.g., average queue
lengths, server utilization and various probabilities), and this option can be
used to estimate any user-defined probability or time average
• Tally statistics are customer averages, and have to be specified in a
Record module to initiate statistics collection, but it is advisable to include
their definition in the Statistic module as well, so the entire set of statistics
may be viewed in the same spreadsheet for modeling convenience
• Counter statistics are used to keep track of counts, and have to be
specified in a Record module to initiate statistics collection
• Output statistics are obtained by evaluating an expression at the end of a
simulation run, using Arena variables, such as DAVG(S) (the time average
of the time-persistent statistic S), TAVG(S) (the average of tally element S),
TFIN (simulation completion time), etc.
• Frequency statistics are used to produce frequency distributions of (random)
expressions, such as Arena variables or resource states, allowing the
estimation of steady-state probabilities
• Statistics collection via the Record module is achieved by its proper
placement in the model
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Record Module
• The Record module is used by entities to collect statistics at
selected locations in the model
• the dialog box below the list of statistics types in a Record module
Dialog box for a Record module
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Record Module (Cont.)
• The Record module has the following options in its Type field:
• the Count option maintains a count with a prescribed increment
(any real value, positive or negative), which may be defined as any
expression or function, and the counter is incremented whenever an entity
enters the Record module
• the Entity Statistics option provides information on entities,
such as time and costing/duration information
• the Time Interval option tallies the difference between the current time and
the time stored in a prescribed attribute of the entering entity
• the Time Between option tallies the time interval between consecutive
entries of entities in the Record module
(these intervals correspond to inter-departure times from the module,
and the reciprocal of the mean inter-departure times is the module’s
throughput)
• the Expression option tallies an expression, whose value is recomputed
whenever an entity enters the Record module.
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Arena Output Reports
• Arena output reports consist of two types: automatic and
user-specified
• An automatic report of summary statistics is generated
automatically at the end of a simulation run by a number of
Arena constructs, such as entities, queues and resources
• those statistics are implicitly specified by the modeler simply by dragging
and dropping those modules into an Arena model
• no further action is required of the user
• A user-specified report provides additional statistics via the
Statistic module (Advanced Process template panel)
and the Record module (Basic Process template panel)
• obtained by explicitly specifying statistics collection in those modules,
where the Statistic module is specified in a spreadsheet view, and the
Record module must be placed in the appropriate location in the model
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Example: Two Processes in Series
• Consider a manufacturing network of two workstations in series,
consisting of an assembly workstation followed by a painting
workstation, where
• jobs arrive at the assembly station with exponentially distributed inter-arrival
times of mean 5 hours
• the assembly process always has all the raw materials necessary to carry out
the assembly operation
• the assembly time is uniformly distributed between 2 and 6 hours
• after the process is completed, a quality control test is performed, and past
data reveal that 15% of the jobs fail the test and go back to the assembly
operation for rework
• jobs that pass the test proceed to the painting operation that takes 3 hours
for each unit
• We are interested in
• simulating the system for 100,000 hours
• estimating process utilizations, average job waiting times and average
job flow times (the elapsed time for a job from start to finish)
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An Arena Model
0
True
Quality Check
Assembly
Arrival Time
0
0
False
Painting
0
Job Arrivals
0
Flow Time
Completed
0
Arena manufacturing model of assembly and painting processes in series
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Assign Module
• The Assign module is used by entities to assign values to attributes
• the dialog box below assigns an arrival time to a job attribute
Dialog boxes for an Assign module
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Decide Module
• The Decide module is used by entities to make branching decisions,
based on chance or the truth/falsity of prescribed conditions
• the dialog box below makes a two-way probabilistic branching decision
Dialog box for a Decide module
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Record Module
• The Record module is used by entities to collect statistics
• the dialog box below tallies job flow times
Dialog box for a Record module
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Resources Report
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Queues Report
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User Specified Report
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Example: Production/Inventory System
• Consider a production/inventory system in which
• the production process (e.g., packaging) is comprised of three stages
1. filling each container unit (e.g., bottles)
2. sealing each unit
3. placing labels on each unit
• a raw-material storage feeds the production process, and finished units
are stored in a warehouse
• customers arrive at the warehouse with product requests (demands),
and if a request cannot be fully satisfied by on-hand inventory,
the unsatisfied portion represents lost business
Process
Warehouse
Demand
Raw
Material
Lost
Customers
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The Production Operation
• The production process component operates as follows:
• there is always sufficient raw material in storage, so the production process
never starves
• product processing is carried out in lots of 5 units, and finished lots are
placed in the warehouse
• lot processing time is uniformly distributed between 10 and 20 minutes
• the production process experiences random failures, which may occur at
any point in time
• times between failures are exponentially distributed with a mean of 200
minutes
• repair times are normally distributed, with a mean of 90 minutes and a
standard deviation of 45 minutes
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The Warehouse Operation
• The warehouse component operates as follows:
.
.
• warehouse operations implement the (r , R ) inventory control policy, where
1. the warehouse has a capacity (target level) of R = 500 units
2. the production process stops when the inventory in the warehouse
reaches the target level
3. from this point and on, the production process remains inactive until the
inventory level drops to or below the reorder point of r = 150 units
4. At this point the production process is restarted until the warehouse
inventory level reaches the target level R = 500
• the inter-arrival times between successive customers are uniformly
distributed between 3 to 7 hours, and individual demand sizes are
distributed uniformly between 50 and 100 units
• on customer arrival, the inventory is immediately checked,
and if there is sufficient stock on hand, that demand is promptly satisfied,
and otherwise, the unsatisfied portion of the demand is lost
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System Performance Measures
• The performance measures of interest are:
• process utilization
• process downtime probability
• warehouse average inventory level
• percentage of customers whose demand is not completely satisfied
on arrival at the warehouse
• average size of lost customer demands at the warehouse,
given that the demands are not completely satisfied
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Production/Inventory Arena Model
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Arena Model Logic
• The Arena model of the production/inventory system is composed of
two segments: inventory management and demand management
• In the Arena model logic of the inventory management segment:
• the packaging process takes a unit of raw material from its queue,
and processes it as a batch of 5
• the finished lot is added to the warehouse inventory (variable Inventory)
• if Inventory reaches or up-crosses the target level (variable Target Stock),
then production stops until the reorder point (variable Reorder Point)
is reached or down-crossed again
• processing of a new batch starts immediately when the reorder point
is down-crossed
• In the Arena model logic of the demand management segment:
• arrivals of customers and their demands at the warehouse are generated
• the variable Inventory is adjusted upon customer arrival
• the value of Inventory is monitored for triggering resumption of suspended
production when the reorder point is down-crossed
• track is kept of lost demand
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Inventory Management Logic
• The Arena model logic implementation of the inventory
management segment makes use of the following modules:
• the Create module, called Raw Material, generates product units for the
packaging (batching) operation
• the packaging operation is modeled using a sequence of
Seize, Delay and Release modules
• the actual processing (packaging in our case) takes place at the Delay
module, called Packaging Process, where the packaging time of a batch is
specified as Unif(10,20) minutes
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Seize and Release Modules
• The Seize module is used by entities to seize resources
• The Release module is used by entities to release resources
• the dialog boxes below seize and release a packaging machine
Dialog boxes for a Seize module and a Release module
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Resource Module
• A spreadsheet view of Resource modules (from the Basic Process
template panel) is used to list all resources and their attributes
• One of the attribute fields is Failures, which specifies resource
failures in a dialog box
• the dialog box below shows the resource Packaging Process with its
Failures field details
Spreadsheet views for a Resource module (bottom)
and its Failures dialog box (top)
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Failure Module
• All failure/repair data (uptimes and downtimes) are specified in
the spreadsheet Failure module (from the Advanced Process
template panel)
• the dialog box below illustrates the data for a random failure
NORM(90,45)
Dialog box of a Failure module
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Altiok / Melamed Simulation Modeling and Analysis with Arena
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Hold Module
• The Hold module performs a gating function on entities
• the dialog box below illustrates the use of a Hold module for checking
the production state
Dialog Box of a Hold module
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Hold Module (Cont.)
• The state of production (0 = off or 1 = on) is maintained in a
variable, called Production, initially set to 0
• the Hold module scan for the truth or falsehood of a logical condition,
(Production = = 1), and holds the entity in the module until the condition
becomes true
• once the condition becomes true, the entity proceeds to the next module,
and otherwise, it waits on in a Queue, called Shall We Produce?.Queue
• The inventory level at the warehouse is maintained in a
variable, called Inventory, initially set to 250
• each product entity (batch of 5 units) that enters the Assign module,
called Update Inventory, increases the warehouse inventory level by a
batch of 5 finished jobs by adding the Batch Size to the variable Inventory
• that entity then proceeds from the Hold module, called Shall We Produce?,
to the Decide module, called Check Target
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Decide Module
• The Decide module is used by entities to make branching decisions,
based on chance or the truth/falsity of prescribed conditions
• the dialog box below makes a two-way probabilistic branching decision by
checking the inventory target level
Dialog Box of a Decide module
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Decide Module (Cont.)
• The entity tests there whether or not the inventory target level has
been up-crossed as follows:
• if the inventory target level has been up-crossed, then the entity moves on
to the Assign module, called Stop Production, and sets Production = 0
to signal that production is suspended
• otherwise, the entity does nothing
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Wrapping Up Entity Sojourns
• An entity has completed its sojourn through the system,
and would normally be disposed of (at a Dispose module)
• However, since the Packaging module is never starved,
and there are no delays incurred since its departure from the
Packaging station, we can “recycle” the entity by always sending
it back to the Packaging Queue to play the role of a new arrival
• This modeling device is logically equivalent to disposing of the
entity and creating a new
• however, it is computationally more efficient, since it saves us this extra
computational effort
• thus, the simulation will run faster!
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Demand Management Logic
• The Arena model logic implementation of the demand
management segment makes use of the following modules:
• the Create module, called Customer Arrives, generates arrivals of
customers, and the Assign module, called Customer Demand,
assigns to the customer a demand level
• the (r ,R ) inventory policy is modeled using a sequence of
Decide and Assign modules
• the Inventory variable keeps track of the inventory level in the warehouse
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Create Module
• The Create module generates a stream of arrivals of Arena
entities (jobs, people, demands, etc.)
• the dialog box below models the arrival of customer demands
Dialog Box of a Create module
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Create and Assign Modules
• The arrival pattern of customers is specified to be random with
inter-arrival time distribution Unif( 3,7)
• On arrival, the customer entity first enters the Assign module,
called Customer Demand, where its Demand attribute is
assigned a random value from the distribution Unif( 50,100)
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Decide Module
• The customer then proceeds to the Decide module, called
Check Inventory, to test whether the warehouse has sufficient
inventory on hand to satisfy its demand
• If the variable Inventory has an equal or larger value than attribute
Demand, then
• the customer takes the True exit to the Assign module, called
Take Away From Inventory, where its inventory is decreased the by
the demand amount
• it next proceeds to the Decide module, named Restart Production,
to test whether the Reorder Level variable has just been down crossed
• if it was down crossed, the customer proceeds to the Assign module, called
Production Start, to set Production = 1, which would promptly release the
product entity currently detained in the Hold module Shall We Produce?,
effectively resuming the production process
• either way, the Customer entity proceeds to be disposed of at the
Dispose module, called Dispose1
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Decide Module (Cont.)
• If the value of variable Inventory is smaller than attribute
Demand, then the current demand is either partially satisfied
or not at all
• either way, the customer entity proceeds to the Assign module, called
Lost Customer, where it sets the Inventory variable to 0
• there it updates the variable Lost, which keeps track of customers whose
demand could not be fully satisfied (Lost = Lost + 1), and the variable
Lost Amount, which keeps track of the customer’s demand lost
(Lost Amount = Demand – Inventory)
• the customer entity next enters the Record module, called
Tally Amount Lost, to tally the lost quantity per customer whose demand
was not fully satisfied
• the customer entity then proceeds to be disposed of at the module called
Dispose1
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Variable Module
• The Variable spreadsheet module (Basic Process template panel)
is used to set or inspected user-defined variables defined in the
model and their properties
• the dialog box bellow lists user defined variables for our example system
Dialog Box of a Variable module
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Statistic Module
• The Statistic spreadsheet module (Advanced Process template
panel) is used to specify statistics collection in the model and their
properties
• the dialog box bellow lists statistics collected in our example system
Dialog Box of a Statistic module
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Statistic Module (Cont.)
• Statistics listed include
• a Time-Persistent statistic, called Stock On Hand, for the Inventory
variable
• a Time-Persistent statistic, called Production On, for the condition
Production = 1
• a Frequency statistic, called Process States, which estimates the state
probabilities of the packaging process, namely, the probabilities that the
packaging process is busy or down
• The statistical outputs for Inventory include
• the average value
• 95% confidence interval
• minimal and maximal values
• The statistical output for state Production = 1 is the percentage of
time this expression is true, that is, the probability that the
packaging process is in production
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Record Module
• The Record module is used to specify user-defined statistics
collection via entities in particular model locations
• the dialog box bellow specifies the tallying of demand loss at a Record
module, called Tally Lost Amount
Dialog Box of a Record module
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Record Module (Cont.)
• Whenever a customer entity enters the module, the expression
(in this case, the variable) Amount Lost is evaluated, and the
resultant value is tallied
• When the replication terminates, the output report will contain a
Tallies section summarizing the statistics of the amount lost per
customer
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Simulation Output Reports
• The simulation was run for one replication of length 1,000,000
minutes (slightly less than two years)
• The reports produced include
• User Specified report
• Frequencies report
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User Specified Report
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Frequencies Report
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Experimentation and Analysis
• Clearly, the customer service level (probability that the demand of
an arriving customer is fully satisfied) is quite low as measured by
the complementary probability of partially or fully unsatisfied
demands, which was estimated by the simulation as 0.25
(the value of Lost Percentage in the Output section of the
User Specified report)
• We want to modify the system to increase the customer service
level (equivalently, we want to decrease the loss probability
from 0.25 to an “acceptable” level)
in inventory-oriented systems, such as the one under consideration,
the only way to increase the customer service level is to increase
the level of inventory on-hand
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Improvement Strategy 1
• In our case, we may attempt to achieve the goal of increasing the
customer service level is to use Strategy 1 for modifying the
original Production/Inventory system as follows:
• invest more in maintenance activities
• this would reduce downtimes and consequently make the process more
available for production
• The next two reports show the improvements under this strategy
(Strategy 1)
• the average repair time is reduced from 90 minutes to 70 minutes
• consequently, the loss probability is reduced from 0.25 to 0.08!
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Frequencies Report for Strategy 1
• The Frequencies report below indicates that Strategy 1 reduces
the average repair time to 70 minutes with a standard deviation
of 25 minutes
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User Specified Report for Strategy 1
• The User Specified report below indicates that Strategy 1
reduces the loss probability to 0.08
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Improvement Strategy 2
• Strategy 2 increases the reorder level so that the reorder level is hit
sooner and production would resume earlier
• when the reorder level is doubled, then the amount lost per customers is
marginally improved from 25% to about 21.5%
• however, we would like to do better as in Strategy 1
• This analysis leads us to conclude the following:
a significant improvement in the customer service level can be achieved
by improving the production process, rather than modifying the inventory
replenishment policy
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Time-Dependent Arrivals
• So far we assumed that random phenomena (e.g., arrivals,
services, etc.) are modeled as variates from a fixed probability
law that does not change in time, so that the underlying
processes are stationary
• However, it is quite common in practice for the underlying
probability law to vary in time, in which case the underlying
process is non-stationary (time dependent)
• for example, rush hour traffic as opposed to ebb hour traffic
• The Arena Create module provides a Schedule option in its
Time Between Arrivals section, to be used only for single
parameter distributions
• example: the exponential distribution, which gives rise to the
Poisson arrival process
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Schedule Option of Create Module
Dialog box of a Create module (bottom)
and spreadsheet view of its Schedule option (top)
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Schedule Option Specification
Dialog box for a schedule specifying time-dependent arrival rates
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Schedule Option Specification (Cont.)
Dialog box for the Options… field of a schedule
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