petri nets refesher

/faculteit technologie management

Petri nets refesher

Prof.dr.ir. Wil van der AalstEindhoven University of Technology, Faculty of Technology Management,

Department of Information and Technology, P.O.Box 513, NL-5600 MB,

Eindhoven, The Netherlands.

Petri netsClassical Petri nets: The basic model

Process modeling

• Emphasis on dynamic behavior rather than structuring the state space

• Transition system is too low level

• We start with the classical Petri net

• Then we extend it with:– Color

– Time

– Hierarchy

Classical Petri net

• Simple process model– Just three elements: places, transitions and arcs.– Graphical and mathematical description.– Formal semantics and allows for analysis.

• History:– Carl Adam Petri (1962, PhD thesis)– In sixties and seventies focus mainly on theory.– Since eighties also focus on tools and applications

(cf. CPN work by Kurt Jensen).– “Hidden” in many diagramming techniques and

systems.

Elements

(name)

transition

arc (directed connection)

t34 t43

t23 t32

t12 t21

t01 t10

transition

• Connections are directed.

• No connections between two places or two transitions.

• Places may hold zero or more tokens.

• First, we consider the case of at most one arc between two nodes.

wait enter before make_picture after leave gone

occupied

Enabled

• A transition is enabled if each of its input places contains at least one token.

occupied

enabled Not enabled

Not enabled

Firing

• An enabled transition can fire (i.e., it occurs).

• When it fires it consumes a token from each input place and produces a token for each output place.

occupied

Play “Token Game”

• In the new state, make_picture is enabled. It will fire, etc.

occupied

Remarks

• Firing is atomic.

• Multiple transitions may be enabled, but only one fires at a time, i.e., we assume interleaving semantics (cf. diamond rule).

• The number of tokens may vary if there are transitions for which the number of input places is not equal to the number of output places.

• The network is static.

• The state is represented by the distribution of tokens over places (also referred to as marking).

Non-determinism

t34 t43

t23 t32

t12 t21

t01 t10

transition t23fires

t34 t43

t23 t32

t12 t21

t01 t10

Two transitions are enabled but only one can fire

Example: Single traffic light rg

orange

Two traffic lights

orange

Problem

Solution

How to make them alternate?

Playing the “Token Game” on the Internet

• Applet to build your own Petri nets and execute them: http://www.tm.tue.nl/it/staff/wvdaalst/Downloads/pn_applet/pn_applet.html

• FLASH animations: http://www.tm.tue.nl/it/staff/wvdaalst/courses/pm/flash/

Exercise: Train system (1)

• Consider a circular railroad system with 4 (one-way) tracks (1,2,3,4) and 2 trains (A,B). No two trains should be at the same track at the same time and we do not care about the identities of the two trains.

• Consider a railroad system with 4 tracks (1,2,3,4) and 2 trains (A,B). No two trains should be at the same track at the same time and we want to distinguish the two trains.

• Consider a railroad system with 4 tracks (1,2,3,4) and 2 trains (A,B). No two trains should be at the same track at the same time. Moreover the next track should also be free to allow for a safe distance. (We do not care about train identities.)

• Consider a railroad system with 4 tracks (1,2,3,4) and 2 trains. Tracks are free, busy or claimed. Trains need to claim the next track before entering.

WARNINGIt is not sufficient to understand the (process) models. You have to be

able to design them yourself !

Multiple arcs connecting two nodes

• The number of arcs between an input place and a transition determines the number of tokens required to be enabled.

• The number of arcs determines the number of tokens to be consumed/produced.

Example: Ball game

Exercise: Manufacturing a chair

• Model the manufacturing of a chair from its components: 2 front legs, 2 back legs, 3 cross bars, 1 seat frame, and 1 seat cushion as a Petri net.

• Select some sensible assembly order.

• Reverse logistics?

Exercise: Burning alcohol.

• Model C2H5OH + 3 * O2 => 2 * CO2 + 3 * H2O

• Assume that there are two steps: first each molecule is disassembled into its atoms and then these atoms are assembled into other molecules.

Exercise: Manufacturing a car

• Model the production process shown in the Bill-Of-Materials.

engine

subassembly1

subassembly2

wheelchassis

chair2

Formal definition

A classical Petri net is a four-tuple (P,T,I,O) where:

• P is a finite set of places,

• T is a finite set of transitions,

• I : P x T -> N is the input function, and

• O : T x P -> N is the output function.

Any diagram can be mapped onto such a four tuple and vice versa.

Formal definition (2)

The state (marking) of a Petri net (P,T,I,O) is defined as follows:

• s: P-> N, i.e., a function mapping the set of places onto {0,1,2, … }.

Exercise: Map onto (P,T,I,O) and S

Exercise: Draw diagram

Petri net (P,T,I,O):

• P = {a,b,c,d}

• T = {e,f}

• I(a,e)=1, I(b,e)=2, I(c,e)=0, I(d,e)=0, I(a,f)=0, I(b,f)=0, I(c,f)=1, I(d,f)=0.

• O(e,a)=0, O(e,b)=0, O(e,c)=1, O(e,d)=0, O(f,a)=0, O(f,b)=2, O(f,c)=0, O(f,d)=3.

State s:

• s(a)=1, s(b)=2, s(c)=0, s(d) = 0.

Enabling formalized

Transition t is enabled in state s1 if and only if:

Firing formalized

If transition t is enabled in state s1, it can fire and the resulting state is s2 :

Mapping Petri nets onto transition systems

A Petri net (P,T,I,O) defines the following transition system (S,TR):

Reachability graph

• The reachability graph of a Petri net is the part of the transition system reachable from the initial state in graph-like notation.

• The reachability graph can be calculated as follows:

1. Let X be the set containing just the initial state and let Y be the empty set.

2. Take an element x of X and add this to Y. Calculate all states reachable for x by firing some enabled transition. Each successor state that is not in Y is added to X.

3. If X is empty stop, otherwise goto 2.

Examplered

(1,3) (1,2)

(3,1) (3,0)

Nodes in the reachability graph can be represented by a vector “(3,2)” or as “3 red + 2 black”. The latter is useful for “sparse states” (i.e., few places are marked).

Exercise: Give the reachability graph using both notations

Different types of states

• Initial state: Initial distribution of tokens.• Reachable state: Reachable from initial state.• Final state (also referred to as “dead states”):

No transition is enabled.• Home state (also referred to as home marking):

It is always possible to return (i.e., it is reachable from any reachable state).

How to recognize these states in the reachability graph?

Exercise: Producers and consumers

• Model a process with one producer and one consumer, both are either busy or free and alternate between these two states. After every production cycle the producer puts a product in a buffer. The consumer consumes one product from this buffer per cycle.

• Give the reachability graph and indicate the final states.

• How to model 4 producers and 3 consumers connected through a single buffer?

• How to limit the size of the buffer to 4?

Exercise: Two switches

• Consider a room with two switches and one light. The light is on or off. The switches are in state up or down. At any time any of the switches can be used to turn the light on or off.

• Model this as a Petri net.

• Give the reachability graph.

Modeling

• Place: passive element

• Transition: active element

• Arc: causal relation

• Token: elements subject to change

The state (space) of a process/system is modeled by places and tokens and state transitions are modeled by transitions (cf. transition systems).

Role of a token

Tokens can play the following roles:

• a physical object, for example a product, a part, a drug, a person;

• an information object, for example a message, a signal, a report;

• a collection of objects, for example a truck with products, a warehouse with parts, or an address file;

• an indicator of a state, for example the indicator of the state in which a process is, or the state of an object;

• an indicator of a condition: the presence of a token indicates whether a certain condition is fulfilled.

Role of a place

• a type of communication medium, like a telephone line, a middleman, or a communication network;

• a buffer: for example, a depot, a queue or a post bin;

• a geographical location, like a place in a warehouse, office or hospital;

• a possible state or state condition: for example, the floor where an elevator is, or the condition that a specialist is available.

Role of a transition

• an event: for example, starting an operation, the death of a patient, a change seasons or the switching of a traffic light from red to green;

• a transformation of an object, like adapting a product, updating a database, or updating a document;

• a transport of an object: for example, transporting goods, or sending a file.

Typical network structures

• Causality

• Parallelism (AND-split - AND-join)

• Choice (XOR-split – XOR-join)

• Iteration (XOR-join - XOR-split)

• Capacity constraints– Feedback loop

– Mutual exclusion

– Alternating

Causality

Parallelism

Parallelism: AND-split

Parallelism: AND-join

Choice: XOR-split

Choice: XOR-join

Iteration: 1 or more times

XOR-join before XOR-split

Iteration: 0 or more times

XOR-join before XOR-split

Capacity constraints: feedback loop

AND-join before AND-split

Capacity constraints: mutual exclusion

Capacity constraints: alternating

We have seen most patterns, e.g.:

How to make them alternate?

Example of mutual exclusion

Exercise: Manufacturing a car (2)

• Model the production process shown in the Bill-Of-Materials with resources.

• Each assembly step requires a dedicated machine and an operator.

• There are two operators and one machine of each type.

• Hint: model both the start and completion of an assembly step.

engine

subassembly1

subassembly2

wheelchassis

chair2

Modeling problem (1): Zero testing

• Transition t should fire if place p is empty.

Solution

• Only works if place is N-bounded

tN input and output arcs

Initially there are N tokens

Modeling problem (2): Priority

• Transition t1 has priority over t2

Hint: similar to Zero testing!

A bit of theory

• Extensions have been proposed to tackle these problems, e.g., inhibitor arcs.

• These extensions extend the modeling power (Turing completeness*).

• Without such an extension not Turing complete.• Still certain questions are difficult/expensive to

answer or even undecidable (e.g., equivalence of two nets).

* Turing completeness corresponds to the ability to execute any computation.

Exercise: Witness statements

• As part of the process of handling insurance claims there is the handling of witness statements.

• There may be 0-10 witnesses per claim. After an initialization step (one per claim), each of the witnesses is registered, contacted, and informed (i.e., 0-10 per claim in parallel). Only after all witness statements have been processed a report is made (one per claim).

• Model this in terms of a Petri net.

Exercise: Dining philosophers

• 5 philosophers sharing 5 chopsticks: chopsticks are located in-between philosophers

• A philosopher is either in state eating or thinking and needs two chopsticks to eat.

• Model as a Petri net.

High level Petri netsExtending classical Petri nets with color, time and hierarchy (informal introduction)

Limitations of classical Petri nets

• Inability to test for zero tokens in a place.

• Models tend to become large.

• Models cannot reflect temporal aspects

• No support for structuring large models, cf. top-down and bottom-up design

Inability to test for zero tokens in a place

“Tricks” only work if p is bounded

Models tend to become (too) larger1

steeringwheel

Size linear in the number of products.

Models tend to become (too) large (2)

Size linear in the number of tracks.

transfer

transfer transfer

claim_track

transfer transfer

transfer

claim_track claim_track claim_track

Models cannot reflect temporal aspects

Duration of each phase is highly

relevant.

No support for structuring large models

High-level Petri nets

• To tackle the problems identified.• Petri nets extended with:

– Color (i.e., data)– Time– Hierarchy

• For the time being be do not choose a concrete language but focus on the main concepts.

• Later we focus on a concrete language: CPN.• These concepts are supported by many variants

of CPN including ExSpect, CPN AMI, etc.

Running example: Making punch cards

donewait

waiting patients

served patients

free desk employees

patient/ employees

Extension with color (1)

• Tokens have a color (i.e., a data value)

{Brand="BMW", RegistrationNo="GD-XW-11", Year=1993, Colour="blue", Owner= "Inge"}

{Brand="Lada", RegistrationNo="PH-14-PX", Year=1986, Color="grey", Owner="Inge"}

• Places are typed (also referred to as color set).

{Brand="BMW", RegistrationNo="GD-XW-11", Year=1993, Colour="blue", Owner= "Inge"}

{Brand="Lada", RegistrationNo="PH-14-PX", Year=1986, Color="grey", Owner="Inge"}

record Brand:string * RegistrationNo:string * Year:int * Color:string * Owner:string

• The relation between production and consumption needs to be specified, i.e., the value of a produced token needs to be related to the values of consumed tokens.

in sum

1 The value of the token produced for place sum is the sum of the values of

the consumed tokens.

Running example: Tokens are colored

donewait

{Name="Klaas", Address="Plein 10", DateOfBirth="13-Dec-1962", Gender="M"}

{EmpNo=641112, Experience=7}

Running example: Places are typed

donewait

record EmpNo:int * Experience:int

record Name:string *Address:string *DateOfBirth:str *Gender:string

record Name:string *Address:string *DateOfBirth:str *

Gender:stringrecord Name:string *Address:string *DateOfBirth:str *Gender:string *EmpNo:int *Experience:int

Running example: Initial state

donewait

start is enabled

Running example: Transition start fired

stop is enabled

donewait

{Name="Klaas", Address="Plein 10", DateOfBirth="13-Dec-1962", Gender="M", EmpNo=641112, Experience=7}

New value is created by simply merging the two records.

Running example: Transition stop fired

New values are created by simply spliting the record into two parts.

donewait

The number of tokens produced is no longer fixed (1)

samplenegative

positive

{sample_number=931101011, measurement_outcomes="XYV"}

{sample_number=931101023, measurement_outcomes="VXY"}

Note that the network structure is no longer a complete specification!

The number of tokens produced is no longer fixed (2)

samplenegative

positive

{sample_number=931101011, measurement_outcomes="XYV"}

The number of tokens produced for each output place is between 0 and 3 and the sum should be 3.

Example

Model as a colored Petri net.

steeringwheel

increase

[{prod="bike", num=4},{prod="wheel", num=2},{prod="bell", number=3},{prod="steering wheel", num=3},{prod="frame", num=2}]

decrease

{prod="bell", num=2}

The entire stock is

represented by the value of a single token, i.e., a list of

records.

Product and quantity are in the value of the

increase

[{prod="bike", num=4},{prod="wheel", num=2},{prod="bell", number=3},{prod="steering wheel", num=3},{prod="frame", num=2}]

decrease

{prod="bell", num=2}

color Product = string;

color Number = int;

color StockItem = record prod:Product * num:Number;

color Stock = list StockItem;

StockItem

Extension with time (1)

• Each token has a timestamp.

• The timestamp specifies the earliest time when it can be consumed.

Extension with time (2)

• The enabling time of a transition is the maximum of the tokens to be consumed.

• If there are multiple tokens in a place, the earliest ones are consumed first.

• A transition with the smallest firing time will fire first.• Transitions are eager, i.e., they fire as soon as they

can.• Produced token may have a delay.• The timestamp of a produced token is the firing

time plus its delay.

Running example: Enabling time

• Transition start is enabled at time 2 = max{0,min{2,4,4}}.

donewait

Running example: Delays

• Tokens for place busy get a delay of 3

• @+3 = firing time plus 3 time units

donewait

Running example: Transition start fired

• Transition start fired a time 2.

donewait

Continue to play (timed) token game…

Exercise: Final state?

@+1@+1

Extension with hierarchy

• Timed and colored Petri nets result in more compact models.

• However, for complex systems/processes the model does not fit on a single page.

• Moreover, putting things at the same level does not reflect the structure of the process/system.

• Many hierarchy concepts are possible. In this course we restrict ourselves to transition refinement.

Instead of

tl1 tl2

We can use hierarchy

tl1 tl2

• Reuse saves design efforts.

• Hierarchy can have any number of levels

• Transition refinement can be used for top-down and bottom-up design

Exercise: model three (parallel) punch card desks in a hierarchical manner

donewait

Analysis of Process Models:Reachability graphs, invariants, and simulation

Questions raised when considering the handling of customer orders

• How many orders arrive on average?

• How many orders can be handled?

• Do orders get lost?

• Do back orders always have priority?

• What is the utilization of office workers?

• If the desired product is no longer available, does the order get stuck?

• Etc.

PN-100

Questions raised when considering the handling of customers in the canteen

• What is the average waiting time from 12.30-13.00?

• What is the variance of waiting times?

• What is the effect of an additional cashier on the queue length?

• Etc.

PN-101

Questions raised when considering the an intersection with multiple traffic lights

• How much traffic can be handled per hour?

• Give some volume of traffic, what is the probability to get a red light?

• Is the intersection safe, i.e., crossing flows have never a green light at the same time?

• Can a light go from yellow to green?

• Is the intersection fair (i.e., a trafficlight cannot turn green twice whilecars are waiting on the other side)?

PN-102

Questions raised when considering a printer shared by multiple users

• Can two print jobs get mixed?

• Do small jobs always get priority?

• Can the settings of one job influence the next job?

• Do out-of-paper events cause jobsto get lost?

• How many jobs can be handled per day?

• What is the probability of a paper jam?

PN-103

Questions raised when considering a teller machine

• What is the average response time?

• Is there a balance, i.e., the amount of money leaving the machine matches the amount taken from bank accounts?

• How often should the machine be filledto guarantee 90% availability?

• Is fraud possible?

• Etc.

PN-104

Analysis

Operational process I nf ormation

System

• Analysis is typically model-driven to allow e.g. what-if questions.

• Models of both operational processes and/or the information systems can be analyzed.

• Types of analysis:

– validation– verification– performance analysis

PN-105

Three analysis techniques (Chapter 8)

• Reachability graph• Place & transition invariants• Simulation

• Each can be applied to both classical and high-level Petri nets. Nevertheless, for the first two we restrict ourselves to the classical Petri nets.

• Use:– reachability graph (validation, verification)– invariants (validation, verification)– simulation (validation, performance analysis)

PN-106

Reachability graph

(1,0,0,1,0,0,1)

(0,1,0,1,0,0,0) (1,0,0,0,1,0,0)

(1,0,0,0,0,1,0)(0,0,1,1,0,0,0)

Five reachable states.Traffic lights are safe!

PN-107

Alternative notation

r1+r2+x

g1+r2 r1+g2

r1+o2o1+r2

PN-108

Reachability graph (2)

• Graph containing a node for each reachable state.

• Constructed by starting in the initial state, calculate all directly reachable states, etc.

• Expensive technique.

• Only feasible if finitely many states (otherwise use coverability graph).

• Difficult to generate diagnostic information.

PN-109

Infinite reachability graphrg1

PN-110

Exercise: Construct reachability graph

occupied

PN-111

Exercise: Dining philosophers (1)

• 5 philosophers sharing 5 chopsticks: chopsticks are located in-between philosophers

• A philosopher is either in state eating or thinking and needs two chopsticks to eat.

• Model as a Petri net.

PN-112

• Assume that philosophers take the chopsticks one by one such that first the right-hand one is taken and then the left-hand one.

• Model as a Petri net.• Is there a deadlock?

PN-113

• Assume that philosopher take the chopsticks one by one in any order and with the ability to return a chopstick.

• Model as a Petri net.• Is there a deadlock?

PN-114

Structural analysis techniques

• To avoid state-explosion problem and bad diagnostics.

• Properties independent of initial state.

• We only consider place and transition invariants.

• Invariants can be computed using linear algebraic techniques.

PN-115

Place invariant

• Assigns a weight to each place.

• The weight of a token depends on the weight of the place.

• The weighted token sum is invariant, i.e., no transition can change it

couple

marriage divorce

1 man + 1 woman + 2 couple

PN-116

Other invariants

• 1 man + 0 woman + 1 couple

(Also denoted as: man + couple)

• 2 man + 3 woman + 5 couple

• -2 man + 3 woman + couple

• man – woman

• woman – man

(Any linear combination of invariants is an invariant.)

couple

marriage divorce

PN-117

Example: traffic light

• r1 + g1 + o1

• r2 + g2 + o2

• r1 + r2 + g1 + g2 + o1 + o2

• x + g1 + o1 + g2 + o2

• r1 + r2 - x

PN-118

Exercise: Give place invariants

free producer

start_production

end_production

wait consumer

start_consumption

end_consumption

product

PN-119

Transition invariant• Assigns a weight to

each transition.• If each transition

fires the number of times indicated, the system is back in the initial state.

• I.e. transition invariants indicate potential firing sets without any net effect.

couple

marriage divorce

2 marriage + 2 divorce

PN-120

Other invariants

• 1 marriage + 1 divorce

(Also denoted as: marriage + divorce)

• 20 marriage + 20 divorce

Any linear combination of invariants is an invariant, but transition invariants with negative weights have no obvious meaning.

Invariants may be not be realizable.

couple

marriage divorce

PN-121

Example: traffic light

• rg1 + go1 + or1

• rg2 + go2 + or2

• rg1 + rg2 + go1 + go2 + or1 + or2

• 4 rg1 + 3 rg2 + 4 go1 +3 go2 + 4 or1 + 3 or2

PN-122

Exercise: Give transition invariants

free producer

start_production

end_production

wait consumer

start_consumption

end_consumption

product

PN-123

Exercise: four philosophers

• Give place invariants.

• Give transition invariants

st4 se4

PN-124

Two ways of calculating invariants

• "Intuitive way": Formulate the property that you think holds and verify it.

• "Linear-algebraic way": Solve a system of linear equations.

Humans tend to do it the intuitive way and computers do it the linear-algebraic way.

PN-125

Incidence matrix of a Petri net

• Each row corresponds to a place.

• Each column corresponds to a transition.

• Recall that a Petri net is described by (P,T,I,O).

• N(p,t)=O(t,p)-I(p,t) where p is a place and t a transition.

couple

marriage divorce

PN-126

Example

couple

marriage divorce

manwoman

couple

marriage

divorce

PN-127

Place invariant

• Let N be the incidence matrix of a net with n places and m transitions

• Any solution of the equation X.N = 0 is a place invariant – X is a row vector (i.e., 1 x n matrix)

– O is a row vector (i.e., 1 x m matrix)

• Note that (0,0,... 0) is always a place invariant.

• Basis can be calculated in polynomial time.

PN-128

Example

Solutions:

• (0,0,0)

• (1,0,1)

• (0,1,1)

• (1,1,2)

• (1,-1,0)

couple

marriage divorce

couplewomanman

PN-129

Transition invariant

• Let N be the incidence matrix of a net with n places and m transitions

• Any solution of the equation N.X = 0 is a transition invariant – X is a column vector (i.e., m x 1 matrix)

– 0 is a column vector (i.e., n x 1 matrix)

• Note that (0,0,... 0)T is always a transition invariant.

• Basis can be calculated in polynomial time.

PN-130

Example

Solutions:• (0,0)T

• (1,1)T

• (32,32)T

couple

marriage divorce

divorce

marriage

PN-131

Exercise

• Give incidence matrix.• Calculate/check place invariants.• Calculate/check transition invariants.

free producer

start_production

end_production

wait consumer

start_consumption

end_consumption

product

PN-132

Simulation

• Most widely used analysis technique.• From a technical point of view just a "walk" in

the reachability graph.• By making many "walks" (in case of transient

behavior) or a very "long walk" (in case of steady-state) behavior, it is possible to make reliable statements about properties/ performance indicators.

• Used for validation and performance analysis.• Cannot be used to prove correctness!

PN-133

Stochastic process

• Simulation of a deterministic system is not very interesting.

• Simulation of an untimed system is not interesting.• In a timed and non-deterministic system, durations

and probabilities are described by some probability distribution.

• In other words, we simulate a stochastic process!

• CPN allows for the use of distributions using some internal random generator.

PN-134

Uniform distribution

pdf cumulative

PN-135

Negative exponential distribution

PN-136

Normal distribution

PN-137

Distributions in CPN Tools

Assume some library with functions:

• uniform(x,y)

• nexp(x)

• erlang(n,x)

• Etc.

A nice function is also C.ran() which returns a randomly selected element of finite color set C, e.g.,color C = int with 1..5;fun select1to5() = C.ran()returns a number between 1 and 5

PN-138

Example

throw_dice()trigger

color BT = unit;color Dice = int with 1..6;

Dice.ran()()

BT Diceoutcome

PN-139

Example(2)

throw_dice

(x+1)@+(Delay.ran())

trigger

color INT = int;color TINT = int timed;color Dice = int with 1..6;color Delay = int with 0..99;

Dice.ran()0

TINT Diceoutcome

PN-140

Subruns and confidence intervals

• A single run does not provide information about reliability of results.

• Therefore, multiple runs or one run cut into parts: subruns.

• If the subruns are assumed to be mutually independent, one can calculate a confidence interval, e.g., the flow time is with 95% confidence within the interval 5.5+/-0.5 (i.e. [5,6]).

PN-141

Example of a simulation model

• Gas station with one pump and space for 4 cars (3 waiting and 1 being served).

• Service time: uniform distribution between 2 and 5 minutes.

• Poisson arrival process with mean time between arrivals of 4 minutes.

• If there are more than 3 cars waiting, the "sale" is lost.

• Questions: flow time, waiting time, utilization, lost sales, etc.

PN-142

Top-level page: main

environment

gas_station

arrive Car

Cardrive_on

depart Car

color Car = string

PN-143

Subpage gas_station

color Car = string;color Pump = unit;color TCar = Car timed;color Queue = list Car;var c:Car;var q:Queue;fun len(q:Queue) = if q=[] then 0 else 1+len(tl(q));

arrive Car

depart Car

[len(q)<3] [len(q)>=3]

put_in_queue drive_on

queue Queue

fill_up

pump_free

TCar Pump

Cardrive_on

Out Out

c@+uniform(2,5)

qq^^[c]

PN-144

Assuming pages for the environment and measurements the last two pages allow for ...

• Calculation of flow time (average, variance, maximum, minimum, service level, etc.).

• Calculation of waiting times (average, variance, maximum, minimum, service level, etc.).

• Calculation of lost sales (average).• Probability of no space left.• Probability of no cars waiting.For each of these metrics, it is possible to formulate

a confidence interval given sufficient observations.

PN-145

Alternatives

Model the following alternatives:

• 5 waiting spaces• 2 pumps• 1 faster pump

color Car = string;color Pump = unit;color TCar = Car timed;color Queue = list Car;var c:Car;var q:Queue;fun len(q:Queue) = if q=[] then 0 else 1+len(tl(q));

arrive Car

depart Car

[len(q)<3] [len(q)>=3]

put_in_queue drive_on

queue Queue

fill_up

pump_free

TCar Pump

Cardrive_on

Out Out

c@+uniform(2,5)

qq^^[c]

PN-146

Simulation of a Production system

X Y Z CSABC

PN-147

Data X Y Z

A 2 5 8

B 3 6 9

C 4 7 1

Processing times

Resources per work center

Replenishment lead times

Kanbans in-between work

centers

Time in-between

subsequent orders

Use distributions

PN-148

Top level page: maincolor INT = int;color Prod = string;color PT = product Prod * INT;color PTimed = Prod timed;color PTTimed = PT timed;var p:Prod;var t:INT;var i:INT;

kanban1

supplier

product1

work_center_

kanban2

product2

work_center_

YHS HS

kanban3

product3

work_center_

kanban4

product4

customer

Prod Prod Prod Prod

ProdProdProdProd

2`"A"++1`"B"++1`"C"

io_lead_time PT

1`("A",2)++1`("B",1)++

1`("C",2)

processing_time_X

1`("A",2)++1`("B",3)++

1`("C",4)

1`("A",5)++1`("B",6)++

1`("C",7)

1`("A",8)++1`("B",9)++1`("C",1)

c_ia_time PTTimed

1`("A",7)++1`("B",9)++

1`("C",8)

processing_time_Y

processing_time_Z

2`"A"++1`"B"++

2`"A"++1`"B"++1`"C"

resources_X INT

resources_Y INT

resources_Z INT

supplierio_lead_time = io_lead_timekanban_in = kanban1product_out = product1

work_centerprocessing_time = processing_time_Xresources = resources_Xkanban_in = kanban2product_in = product1kanban_out = kanban1product_out = product2

work_centerprocessing_time = processing_time_Yresources = resources_Ykanban_in = kanban3product_in = product2kanban_out = kanban2product_out = product3

work_centerprocessing_time = processing_time_Zresources = resources_Zkanban_in = kanban4product_in = product3kanban_out = kanban3product_out = product4

customerc_ia_time = c_ia_timekanban_out = kanban4product_in = product4

PN-149

Sub page: supplier

kanban_in

product_out

PTimedio_lead_time PT

(p,t)In/Out

accept_order

deliver_order

PN-150

Sub page: customer

kanban_out

product_in

PTTimedc_ia_time

In/Out

place_order

consume

(p,t)@+t

PN-151

Sub page: work_center

product_out

kanban_in

INTresources

In/Out

end_proc

start_proc

wip PTimed

[i>=1]

kanban_out

Prod p

product_in

PTprocessing_time

In/Out(p,t)

PN-152

Overview

color INT = int;color Prod = string;color PT = product Prod * INT;color PTimed = Prod timed;color PTTimed = PT timed;var p:Prod;var t:INT;var i:INT;

kanban1

supplier

product1

work_center_

kanban2

product2

work_center_

YHS HS

kanban3

product3

work_center_

kanban4

product4

customer

Prod Prod Prod Prod

ProdProdProdProd

2`"A"++1`"B"++1`"C"

io_lead_time PT

1`("A",2)++1`("B",1)++

1`("C",2)

processing_time_X

1`("A",2)++1`("B",3)++

1`("C",4)

1`("A",5)++1`("B",6)++

1`("C",7)

1`("A",8)++1`("B",9)++1`("C",1)

c_ia_time PTTimed

1`("A",7)++1`("B",9)++

1`("C",8)

processing_time_Y

processing_time_Z

2`"A"++1`"B"++

2`"A"++1`"B"++1`"C"

resources_X INT

resources_Y INT

resources_Z INT

supplierio_lead_time = io_lead_timekanban_in = kanban1product_out = product1

work_centerprocessing_time = processing_time_Xresources = resources_Xkanban_in = kanban2product_in = product1kanban_out = kanban1product_out = product2

work_centerprocessing_time = processing_time_Yresources = resources_Ykanban_in = kanban3product_in = product2kanban_out = kanban2product_out = product3

work_centerprocessing_time = processing_time_Zresources = resources_Zkanban_in = kanban4product_in = product3kanban_out = kanban3product_out = product4

customerc_ia_time = c_ia_timekanban_out = kanban4product_in = product4

kanban_in

product_out

PTimedio_lead_time PT

(p,t)In/Out

accept_order

deliver_order

kanban_out

product_in

PTTimedc_ia_time

In/Out

place_order

consume

(p,t)@+tproduct_out

kanban_in

INTresources

In/Out

end_proc

start_proc

wip PTimed

[i>=1]

kanban_out

Prod p

product_in

PTprocessing_time

In/Out(p,t)

Results:• response time• utilization• % backorders• average stock• etc.

PN-153

Classical versus high-level Petri nets

• Simulation clearly works for all types of nets.

• Hierarchy is never a problem.

• Time allows for new types of analysis.

• Reachability graphs and invariants can also be extended to high-level nets.– More complex (both technique and computation)

• Sometimes abstraction from color is possible to derive invariants (consider previous example).

PN-154

Exercise: Five Chinese philosophers• Recall hierarchical CPN model of five

Chinese philosophers alternating between states thinking and eating. – Give place invariants– Give transition invariants

• Change the model such that philosophers can take one chopstick at a time but avoid deadlocks and a fixed ordering of philosophers.– Give place invariants– Give transition invariants

PN-155

Top-level pagecolor BlackToken = unit;var b:BackToken

BlackTokenPH1

CS1CS2

BlackToken

PH2PH5

PH3PH4

BlackTokenBlackToken

BlackToken

philosopherleft = CS5right = CS4

PN-156

Page philosopher

take_chopsticks

put_down_chopsticks

BlackToken

left right

BlackToken BlackTokenIn/Out In/Out

PN-157

Flat model is obtained by replacing substitution transitions by subpagescolor BlackToken = unit;var b:BackToken

BlackTokenPH1

CS1CS2

BlackToken

PH2PH5

PH3PH4

BlackTokenBlackToken

BlackToken

take_chopsticks

put_down_chopsticks

BlackToken

left right

Repeat 5 times...

Naming:•PH3.think•PH3.eat•PH3.take_chopsticks•PH3.put_down_chopsticks

PN-158

Alternative page

start_eating

start_thinking

BlackToken

hold_left hold_ right

BlackToken BlackToken

take_left

take_right

left right

return_left return_right

PN-159

You should be able to ...• Construct a reachability graph for a classical Petri

net.• Give meaningful place and transition invariants for

a classical Petri net.• Construct a reachability graph and give

meaningful place and transition invariants for a hierarchical CPN after abstracting from data and time and removing hierarchy.

• Build a simple simulation model using CPN.• Motivate the use of each of the analysis

techniques.

petri nets refesher

Documents

elasticity and petri nets

coloured petri nets

discrete timed petri nets - pure - aanmelden · colored...

petri nets 2000

coloured petri nets -...

petri nets - taunachumd/models/nets.pdf · petri nets petri...

petri nets and model checking - universitetet i oslo ·...

implementation of petri nets based controller using sfc ·...

petri nets 2004

petri nets peterson

petri nets refresher

petri nets 2000 · petri nets 2000 21st international...

petri nets 2000 - tidsskrift.dk

elementary petri nets - cas.mcmaster.ca

petri nets:

24 petri nets - edisciplinas.usp.br

1 petri nets classical petri nets: the basic model

applications of petri nets

continuous petri nets

stochastic petri nets - unipi.it