System modeling using ERG and Petri Nets

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c r a f t i n g i n n o v a t i o n t o g e t h e r

CEP course on System Modeling

for DRDO Scientists during 18-22

January 2016 at Center for

Artificial Intelligence and Robotics

(CAIR), DRDO, Bengaluru

•Modeling Causality with Event Relationship Graphs

•Petri Nets for Dynamic Event driven system modeling

By Navneet BhushanCrafitti Consulting Private

Limited

Talks on 21st January 2016

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2

SYSTEM AND FUNCTION

• System - from fundamental principles point of view – set of elements working together to achieve an

objective or perform a function. – set of elements (Energy-matter organized in space-time) working together

(exchanging energy and information) to achieve an objective or perform a function (Create Change - in Matter, Energy and Information in Space-time).

• When the system is achieved by thought and consciousness we make systems artificial – A TECHNICAL SYSTEM

System

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Three Types of Certainty – Leonhard Euler, 1761

May 1, 2023© Crafitti Consulting Private Ltd. 3

• Perceptual certainty– “I saw it with my own eyes”

• Demonstrative certainty– Deductive logic/ Logical certainty

• Moral certainty– Told by others – with some established

authority

CONSENSUS

CONSISTENCY

AUTHORITY

REVELATION

DURABILITY

SCIENCE

SIX TRUTH FILTERS

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Sources of Uncertainty and Science


John L. Casti (ref) – “Searching for Certainty” – two main sources of uncertainty – randomness and imprecision

Science has and can deal with randomness to a great extent – but needs precision or least vagueness in language and expression

Observation Empirical Laws

Laws of nature Theories

Experiment

TheoryProcess of Science

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Paradigm level issues in Modeling and Simulation (Rand, Paul K. Davis)

• Models as Tools Vs Models as representation of knowledge• About uncertainty

– Soft Factors– Complexity– Uncertainty

• Parametric – Point scenarios (insufficient) – Spanning set of scenarios/Capabilities based planning

• Structural


Models• Record and communicate knowledge of

complex and complicated systems• Computational experiments for generative

models

Reality

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How to Evaluate a System (4 Scientific Ways)

• DIRECT OBSERVATION AND MEASUREMENT

• EXPERT JUDGEMENT / GROUP DECISION MAKING

• ANALYTICAL/MATHEMATICAL MODEL (deterministic/stochastic – but a closed form solution)

• SIMULATION• Emergent/agent based simulations


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Everything is not Software!!

May 1, 2023 7© Crafitti Consulting Private Ltd.

… But we can make a Model and Simulate the model of

nearly everything,

potentially … as everything has

Information

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Decisions must be made and actions must be taken today, but the results are not clear until tomorrow, at best.

The uncertainty of the situation at hand makes every decision a burden

Handbook of Foreign Policy Analysis

Play ˀ “If only I know …”

ˀ “If only I had known …”

Decision

Dealing with UNCERTAINTY

To minimize uncertainty, … by taking pains to get more information about the environment

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SituationAssessment Explanation

Forecasting

Options Generation

Making Decision - Choice

Science is not an oracle – but it can help to reduce uncertainty

Situation Assessment

· Data Collection

· Data Cleansing

· Data Collation· Classification· Observation

Explanation

· Causal Analysis

· Cognitive Mapping

· Systems Analysis

Forecasting

· Historical Analogies

· General Analogies

· Prediction· Projection· Forecasting· “What if”

Analysis

Options Generation

· Decision Trees

· Scenario Writing

· Alternatives

· Brainstorming

· Solution

Choice

· Optimization· Decision

Making under uncertainty and partial information

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Situation Assessment – Where do we stand? Simple Indicators and Checklists/ Complex Indicators/ Scaling (R-factor Analysis)/ Typologies (Q-factor Analysis) /Cluster Analysis /Multidimensional Scaling/ Artificial

Neural Networks (ANN)/ Value Stream Mapping / TRIZ – 9 Windows/ TRIZ- Ideal Final Result

Explanation – Why are things as they are?Correlation Analysis/ Regression Analysis/Analysis of Non-Linear Relationships/ Partial

and Multiple Correlation Analysis/ Multiple Regression Analysis/ Path Analysis

Forecast – What will happen? Systematic Expert Judgment/ Decision Matrix/ Analytic Hierarchy Process/ Bayesian

Inference/ Cross-Impact Analysis/ Early warning Indicators/ Extrapolation with Moving Averages/ Trend Analysis/ Time Series Analysis/ Spectral Analysis/Combined Trend and

Time Series Analysis /Trend Impact Analysis

Preparation of Decisions – What are the Options? Game Theory/ Gaming/ Computer Simulation/ Cellular Automata/ Petri Nets/

Econometric Models/Mathematical Modeling / TRIZ

Choice – What to do? Decisional Trees/ Decisional Matrix/ Linear Partial Information (LPI) Analysis/

Linear/Integer/Non-Linear Programming/ Heuristic Optimization Techniques – Genetic Algorithms, Simulated Annealing, Tabu Search, Artificial Life / AHP

Techniques & Methodologies

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Discrete-Event Systems and Dynamic Models

• Elements/Entities of a system may be physical or mathematical• Entities may be “resident” or “transient”

• In a Barber Shop – Barbers are resident, customers are transient

• System is an abstraction in some sense of reality • Entities will have attributes – which can be static or dynamic,

deterministic or stochastic

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Discrete-Event Systems and Dynamic Models

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System Execution – Discrete Event

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Event-Relationship Graphs

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Event-Relationship Graphs model of a single server queuing system

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Event-Relationship Graphs

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Verbal ERGs

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Parametric ERGs

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19

PETRI NETS

• Graph Models of System behaviour ( a bi-partite graph)• Abstract and Formal• Description and Analysis of – Info and Control Flow• Asynchronous and Concurrent activities can be modeled

System State: Holding of a set of conditionsState Change: End of some conditions and Start of some conditionsEvent: Elementary state change (atomic)

Definition: PNs are graph models for system description using notions of conditions and events

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PETRI NET Model of a marriage !

Man

P1

Woman

P2

P3Pundit/Qazi/Minister/ Judge

t1Husband

P4

Wife

P5

Pre-Conditions

Post-Conditions

Places

transition

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(a) Before Ceremony

(b) After Ceremony

PETRI NET Model of a marriage !

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Petri Nets (Bi-partite graph models of a system)

Formally a PN is defined as a 5 tuple PN = { P, T, F, W, M0} where

P = {p1 ,p2 ,...,pm} is a finite set of places.T = {t1,t2,...,tn} is a finite set of transitions.F {PT} {T P} is a set of arcs (flow

relation).W : F {1,2,3,...} is weight function.M0: P {0,1,2,3,...} is the initial marking.

Also, PT = and PT .


Dynamic Behaviour• Use of Tokens• # of tokens in a place => #

of data items/conditions• Token Distribution over

places is the system state• M0 is the initial marking• Transition Rules – simulate

system dynamics• Enabled Transition• Firing of Transition• Result of Transition (state

change) => change in marking

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Transition Firing


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State machines and Marked Graphs are special cases of Petri Nets

State Machine- PN in which For all T

- i/p place is 1- o/p place is 1

- Modeling of all sequential programs

- Represent decisions- Can’t Represent Concurrency


Marked Graphs- PN in which- For all P

- i/p transition is 1- o/p transition is 1

- Can’t represent decisions- Can represent concurrency

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Modeling Power of Petri Nets


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Modeling Power of Petri Nets

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Properties and Analysis Methods of Petri Nets

Ability to support analysis of many properties and problems associated with concurrent systems

• Reachability• Boundedness• Safeness• Liveness• Persistence• Coverability• Reversability


Analysis Methods• CoverabilityTree• Matrix State Equations• Reduction or Decomposition

techniques

System Simulation

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A PETRI NET BASED SIMULATION APPROACH FOR EVALUATING BENEFITS OF TIME TEMPERATURE INDICATOR AND WIRELESS TECHNOLOGIES IN PERISHABLE GOODS RETAIL MANAGEMENT

Navneet Bhushan and Kishore

17-18 June 2002, Cork, Ireland

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TALK ORGANISATION

• PERISHABLE GOODS RETAIL MANAGEMENT- CURRENT SCENARIO AND PROBLEMS

• TIME TEMPERATURE INDICATOR AND WIRELESS TECHNOLOGIES – THE PROPOSED SOLUTION

• PETRI NETS (PN) FOR MODELING & SIMULATION

• PN BASED SIMULATION OF PERISHABLES RETAIL MANAGEMENT

• TEST SCENARIO• PN MODEL OF THE SYSTEM• SIMULATION RESULTS• ANALYSIS• CONCLUSIONS AND FURTHER WORK

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PERISHABLE GOODS RETAIL MANAGEMENT

• Perishable Goods (PG) – Fruits, Meat Products, Medicines, Chemicals, etc. need Sufficient Cold Storage from the Production stage to the Consumption Stage so as to remain fresh

• Presently a Sell by Date label is fixed by the vendor• No way to find out whether the cold chain was maintained on the

way to retail store or not.• Problem lies in limitations of technology to ascertain the freshness

of the products.

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Potential breakage in the cold chain

Apply a Barcode with “Sell By Date”

Maintain the cold chain

Manage inventory based on FIFO or the “Sell-By-Date

Maintain the cold chain

Manage inventory based on FIFO or the “Sell-By-Date”

Higher shrinkSale of unsafe productSub Optimal use of available life

Vendor

Transportation

Warehousing

Distribution

Retail

The Current Scenario

PERISHABLE GOODS RETAIL MANAGEMENT

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CAN TECHNOLOGY SOLVE THE PROBLEM ? – A Proposal

• Combining Two Key Technologies • TIME TEMPERATURE INDICATORS (TTI) are capable of

measuring the life of temperature sensitive products - An adhesive label consisting of an enzyme and a substrate filled ampoule separated by a breakable seal. The colour of the ampoule changes from green at the start to yellow at the end of product life cycle. An increase in temperature beyond the specific temperature hasten the color change. Change in color if captured can tell the remaining life of the product.

• WIRELESS LOCAL AREA NETWORKING (WLAN) IEEE 802.11b standard, is a wireless networking technology that can integrate mobile devices to the wired infrastructure as well as to each other through wireless links. WLANs are already being deployed in large stores and organizations.

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Vendor

Transportation

Warehousing

Distribution

Retail

Apply a Barcode with “Sell By Date”

Manage inventory based revised “Sell-By-Date”

Update the Sell By date based on remaining life

Inform Vendor & transporter in case of reduced life

Find cause & take action

Find cause & take action

Retail Category Management systems to help decisions on pricing.

Vendor rating systems to monitor vendor performance

Manage the cold chain

Manage the cold chain

THE PROPOSED SOLUTION

http://www.citizen.co.uk/printer/portable/pd04.htm






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Update Inventory Information

Inventory movement based on remaining life

Vendor Rating systems updated

Category Management System

New price and sell by date information

New label printed to enable sale at an optimal price

THE PROPOSED SOLUTION – At The Retail Store


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MODELLING AND SIMULATION – PETRI NETS• A powerful modelling framework

for information flow.• Major use is for modelling

Concurrent occurrences with constraints, precedence or frequency of occurrences.

• Carl Adam Petri 1962

Directed, weighted, bipartite graph - two kinds of node, places and transitions.

Places represent conditions and transition represent events.

A transition has input and output places representing pre-conditions and post-conditions of events

Formal Definition: Petri Net (PN)

PN = { P,T,F,W,Mo) P = { p1,p2,…,pm } is finite set of places. T = { t1,t2,…,tn } is finite set of transitions. F { P X T } { T X P } is a set of arcs (flow relation). W : F-> {1,2,3,…} is weight function. Mo : P-> {0,1,2,3,…} is the initial marking. Also, P T = P U T ≠

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A state or marking in a PN is changed according to following transition rule:• A transition t is said to be enabled if each input place p of t marked with at

least w(p,t).• An enabled transition may or may not fire.• A firing enabled transition t removes w(p,t) tokens from each input place p

of t, adds w(p,t) tokens to each output place p of t.

Simulation of dynamic behavior of systems

Timed Transition Petri Nets• Transition have firing time associated with them.• Time may be Deterministic or Stochastic• The stochastic transition may follow an exponential distribution with

parameter

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Petri Net: Food Selling illustration

Customers in Store

Apples on Shelf

Oranges on Shelf

4

2

Occurrence of Purchase

Satisfied Customer

Firing

Customers in Store

Apples on Shelf

Oranges on Shelf

4

2

Occurrence of Purchase

Satisfied Customer

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THE SIMULATION SCENARIO

• The Demand: In multiple Retail Stores there has been observed an average demand for 200 Boxes of Perishable Goods per day. Further this demand has been observed to follow a Poisson probability distribution, i.e., the inter arrival time between two demands is exponentially distributed with mean 200 boxes per day.

• The Perishable Goods: The Perishable Goods are labeled with Sell by Date at the Vendor Place. This Sell by Date is computed assuming average cold storage conditions on the way from the vendor to the store. The boxes having the least Sell by Date are picked in a FIFO manner.

• The Vendor: The vendor sends on an average 2000 boxes every 10th day. This is assuming that retail stores have a capability of storing 2000 boxes for 10 days after which the goods expire. These boxes are sent in 20 trucks each carrying 100 boxes. These trucks reach the respective stores on an average in 2 days starting from the vendor to the stores. On an average 10% of these trucks per 10 days do not meet the cold storage requirements. 10% actually exceed the cold storage required by Sell by Date. And 80% meet the cold conditions required by Sell by Date. Also, we assume that the goods not meeting the required cold conditions (we call them Category C) perish on an average in 2 days of reaching the retail outlets. The goods meeting the cold storage (Category B) perish on an average in 8 days of reaching the retail outlets. While goods exceeding the cold storage (Category A) perish on an average in 10 days of reaching the retail outlet.

• Present Scenario: There is no way presently to distinguish between the three categories at the retail outlets. Hence the Boxes are randomly picked (average 200 boxes per day) from Category A, Category B or Category C. This leads to possible customer dissatisfaction if they buy Category C and loss by the store if they sell Category A goods much before their actual Sell by Date.

• Proposed Solution: In the Proposed Solution because of TTI labels, it is possible to distinguish between the three categories and schedule their selling based on Least Shelf Life First Out (LSFO) scheduling

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PN MODEL OF THE SYSTEM

ColdChainMaintained p = 0.8

BoxesWithVendor

Requested

Boxes

200

Transportation

CheckColdChain

ReachingStore

ColdChain ColdChainNotMaintained p= 0.1

VeryWellMaintained p = 0.1

100 ActuallyPerished

ExceedSellByDate

SellByDate

DemandCreated

Demand

200

PerishedPutOn Shelf

CategoryCSoldCategoryBSold

GoodToSell

CategoryASold

TotalPerished

G1

G2

G3

BoxesSent

BoxesNeeded

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SIMULATION RESULTS AND RELATIVE CHANGE IN PARAMETERS

Parameters Present Case (E)

Proposed Solution (N)

Relative Change (N/E)

Total Boxes Sent by Vendor (X)

69231 71224 1.029

Sold Category B (Y) 48972 61512 1.256

Sold Category C 5999 - -

Sold Category A 6061 - -

Perished (P) 220 87 0.395

Left (L) 7979 9625 1.206

% Category B sold (100 * Y/X)

70.7 86.4 1.222

% Perished (100 * P/X) 0.318 0.122 0.384

Category B Sales (% terms) have increased by 22%, perished goods have decreased by 60%.

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ANALYSIS OF THE SIMULATION RESULTS

Parameter Present Proposed Change (%)Boxes Sold (S) 61032 (Category A, B

and C)61512 (Category B

only)0.786

Revenue (S * $ 120) 61032 * 120 = 7323840 61512 * 120 = 7381440 0.786Profit (S * $20) (P) 61032 * 20 = 1220640 61512 * 20 = 1230240 0.786Loss Due to selling

Category C (1)5999 * $10 = 59990 0

Loss Due to Selling Category A (2)

6061 * $5 = 30305 0

Loss Due to Perished Boxes (3)

220 * $100 = 22000 87 * $100 = 8700 -60.455

Loss Due to Loss in Profit because of Perished Goods (4)

220* $20 = 4400 87 * $20 = 1740 -60.455

Total Loss (L= 1+2+3+4)

116695 10440 -91.054

Net Profit (P –L) 1103945 1219800 10.495

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CONCLUSIONS AND FURTHER WORK

• The present perishable goods retail management results in considerable loss for the retailer due to inability of the system to predict the storage conditions on the way from the vendor point to the retailer

• Combining two recent technologies, i.e., Time Temperature Integrators (TTI) and Wireless LAN (WLAN), we propose a solution that will considerably solve the problem.

• To evaluate the benefits of proposed solution vis-à-vis existing situation we developed a simulation algorithm based on a well-established technique called PETRI NETS.

• The results of the simulation analysis clearly shows the benefits of the proposed solution. In the assumed scenario, it has been shown that the Loss of perishable goods can be reduced up to 90% in monetary terms and Net profit for the retailer can grow by 10%.

• The study indicates the power of using SIMULATION in analyses of conceptualized solutions before designing the actual solution.

• Next step is to design and develop the solution as the benefits are clearly quantified using the simulation model described here.

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Case Study: Methodology paper (Petri Net based simulation of Air Defence System)

Performance Evaluation of an Air Defence System Modeled as a Petri Net

System analysis symposium (CASSA , Bangalore, 1997)

AbstractPetri Nets (PN) are one of the powerful models of information flow. The major use

of PNs has been in the modelling of systems of events in which it is possible for some events to occur concurrently but there are constraints on the occurrence, precedence or frequency of these occurrences. This paper presents a novel approach to model an Air Defence System (ADS) as a PN for performance evaluation. The ADS modeled as a PN is simulated to estimate various performance parameters such as throughput, penetration probability and response time of the system. This performance evaluation tool can be successfully adapted to other systems if the corresponding PN models of such systems are available.


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The problem – Air Defence System

One of the major factors contributing to Allied victory over Iraq in 1991 Gulf War was the failure of Iraqi Air Defence System. The results of any future war will depend to a large extent upon the performance of Air Defence Systems (ADS) of respective countries. How the ADS of any country will perform under a given threat is a difficult question to answer. For this purpose there is a need to develop a performance evaluation tool for finding out the effectiveness of the ADS. There are four main techniques usually considered for performance evaluation namely, expert judgement, direct measurement, analytical modelling and simulation. Out of these, simulation provides a flexibility and efficiency that is not possible in other techniques. However, describing a system for simulation purposes requires a much involved modelling exercise [3,5].


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Uses of Air Power

• Traditional Use• Counter Air/Deep Strike• Close Air Support

• Extensive Air Bombardment followed by ground action (1991 Gulf War)

• Winning Future wars with Air Force only

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1991 Gulf War (Force Package Concept)

A W A C S

would ensure A/c coming from different bases would arrive at the target in a pre-decided manner

Defence Suppression A/c

EF-111 jam Iraqi Long Range Radar forcing SAM Crews to turn on their radar

F-4G wild weasel fires Msl( HARM/Shrike) to knock out SAM radar

Time

Actions

Fighters /Interceptors

F-15 or F-16 would come as AD Escorts

Actual Bombers

Delivery of bomber payload

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BOR DERBOR DERBOR DER

Enemy Aircraft

Airborne radar

Mobile radarRadar

Sensor Fusion Post (SFP)

ACC1

ACC2

SFP2

SFP3

Central Air OperationCentre (CAOC)

AIR CONTROLCENTRE

(ACC)SAMOP

Centrel

SHORADOPERATION

CENTRE

FIGHTERSQUADRONOP CENTRE

SAMSite

Air DefenceGuns

FighterSquadron

The system description – Air Defence System Modeled on

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Petri Nets

• Petri Net (PN) is one of the most powerful formal models of information flow. The major use of PN has been the modelling of systems of events in which it is possible for some events to occur concurrently but there are constraints on the occurrence, precedence or frequency of these occurrences. PNs are a graphical and mathematical modelling tool applicable to many systems. As a graphical tool, PNs can be used as a visual communication aid similar to flowcharts, block diagrams and networks. In addition, tokens are used to simulate the dynamic and concurrent activities of systems. As a mathematical tool, it is possible to set up state equations, algebraic equations and other mathematical models governing the behaviour of systems. The concept of PN was originated in the year 1962 when Carl Adam Petri presented his dissertation. Some time later the work of Petri came to the attention of A.W. Holt who applied these concepts to various concurrent processing concepts.


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Petri Nets

Formally a PN is defined as a 5 tuple PN = { P, T, F, W, M0} where

P = {p1 ,p2 ,...,pm} is a finite set of places.T = {t1,t2,...,tn} is a finite set of transitions.F {PT} {T P} is a set of arcs (flow relation).W : F {1,2,3,...} is weight function.M0: P {0,1,2,3,...} is the initial marking.

Also, PT = and PT .


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Petri Nets (the dynamic behaviour model of a system)

The behaviour of many systems can be described in terms of system states and their changes. In order to simulate the dynamic behaviour of a system, a state or marking in a PN is changed according to the following transition (firing) rule :• A transition t is said to be enabled if each input place p of t is marked with

atleast w(p,t) tokens, where w(p,t) is the weight of the arc from p to t.• An enabled transition may or may not fire ( depending upon whether or not

the event actually takes place)• A firing of an enabled transition t removes w(p,t) tokens from each input

place p of t, and adds w(t,p) tokens to each output place p of t, where w(t,p) is the weight of the arc from t to p.

• A transition without any input place is called a source transition, and one without any output place is called the sink transition. A timed transition PN is one in which the transitions have a firing time associated with them. This time may be deterministic or stochastic. If the deterministic transition has zero time it is called immediate. The stochastic transition may follow an exponential distribution with parameter .


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Simulation Methodology

• Modeling of the hypothetical ADS as a Time Transition Petri Net (TTPN).

• Modeling of each ACC-SFP pair in the ADS as a petri net object. The ACC-SFP pairs are modeled in such a way that they maintain a recursive structure and hence are easily extended to a series of ACC-SFP pairs connected through CAOC.

• The simulator is modeled as an object interacting with the petrinet objects and obtaining information about transitions that are about to fire.

• Event driven simulation of the events that were generated by the petrinet objects is carried out

• A sensitivity analysis of the simulation results towards various input parameter is carried out.


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Delay Delay StartStop Arrival

Limit300 .Arrivals

Arrival

Threats

DetectedUndetected

Back SensorUndetected

PenetrationsPenetration

TX1SFP

TL

Gone1

Gone2CAOC

ACC

NS

Plane

No response

ACCL

TR

ACCRSAM

Gun

Sqdn

SAMOC

Fighter Plane

SAM

Gun

Pres

Sres

Gres

SHORADOC

NG

NP

ACC

TX2

Transition with exponential firing

Transition with determinstic firing

Fig. 3 : TTPN Model of an AD System

Timed-transition Petri Net Model of an AD system

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Delay

Arrival

Penetration

No response

Fighter

SAM

Gun

AD1 AD2 AD3

CAOC CAOC

ACC ACC ACCCAOC

TL3

TR2

TR1

TL3

PN Model of a CAOC linking three AD Systems

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Penetration probability variation wrt Arrival rate


Table 3 : Variation of Throughput & Penetration probability with arrival rate parameter (a) Arrival Rate Parameter

0.008 0.0167 0.042 0.1 1.0 2.0 5.0

Arrival 3963 5457 7281 9000 9900 9900 9900 Responded 3678 5057 6750 8110 8882 8871 8873 no Response 3 4 11 237 317 318 334 Penetrated 25 36 48 53 60 63 62 Fighter Response

2833 3886 5198 6195 6811 6778 6804

SAM Response

525 724 960 1172 1279 1295 1273

GUN Response

321 448 592 743 792 798 796

Throughput 0.93 0.93 0.93 0.90 0.897 0.888 0.896 Penetration Probability

0.007 0.007 0.008 0.032 0.038 0.038 0.04

Using Petri Nets methodology it is possible to study a large number of system parameters. The technique described in this paper can be used to carry out performance modelling and system analysis of any system that can be described as a discrete event dynamic system.

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S-400 Missile system (Triumf) from Russia – can we simulate it and see how effective it will be in Indian scenarios? Compare it with the

existing air defence system?

The S-400 uses three different missiles to cover its entire performance envelope. These are the extremely long range 40N6, long range 48N6 and medium range 9M96 missile. Each one has different capabilities.

Structure

30K6E administration system: manages 8 divisions (battalions)[9][10][11] 55K6E command and control centre based on Ural-532301.91N6E[12] Panoramic radar detection system (range of 600 km) with protection against jamming. Mounted on an MZKT-7930. 300 targets. Decimetric band (S).[13]

6 battalions of 98ZH6E Surface-to-air missile systems consisting of (an independent combat system for autonomous operation):[14] Each battalion can hit no more than 6 goals on their own.[15]+2 another battalions if they are within range 40 km. 92N6(or 2)E Multi-functional radar (range of 400 km). 100 targets.[16]

5P85TE2 launchers and/or 5P85SE2 on the trailer (up to 12 launchers).Surface-to-air missiles allowed by Russian Presidential decree: 48N6E, 48N6E2, 48N6E3, 48N6DM, 9M96E, 9M96E2 and ultra distance 40N6E.[17]

Own the radars system S-400 this is Active electronically scanned array (official government statement)[18]

The Petri Net based Air Defence modeling and simulation methodology can be used

https://en.wikipedia.org/wiki/S-400_(missile)

https://en.wikipedia.org/wiki/Ural-5323







https://en.wikipedia.org/wiki/Active_electronically_scanned_array


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HPSim Live Tool demo Model of a Tank Operation

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Simulation Results

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Contents

May 1, 2023

• About Crafitti

CRAFITTI: Your Global Innovation and IP Think Tank

Crafitti offers active collaboration to craft ideas in multiple innovation contexts.

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Crafitti Consulting Private Limited

May 1, 2023 59

Crafting INNOVATION Together

• We co-craft end-customer value with every mind working with us by– Enabling Emergence of NEW– Maximizing the LIFE of EACH

IDEA– Empowering IDEAS– Making Innovation Happen– Future-proofing by creating

Future insights and forecasts

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May 1, 2023© Crafitti Consulting Private Ltd.

Our Clients (Representative list)

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WHAT YOU TOLD US…

May 1, 2023 61

• WE DON’T KNOW WHAT’S NEXT• WE DON’T HAVE IDEAS• WE HAVE LOTS OF IDEAS BUT DON’T KNOW WHAT TO DO

WITH THEM• CAN YOU HELP US INVENT• WE HAVE LOTS OF INVENTIONS BUT WE DON’T KNOW IF

THEY CAN BE PATENTED• WE HAVE PATENTS BUT WE DON’T KNOW HOW STRONG

THEY ARE• WE HAVE PATENTS BUT WE DON’T KNOW THEIR VALUE• WE HAVE PRODUCT BUT WE DON’T KNOW THE MARKET• WE HAVE PRODUCT BUT WE DON’T KNOW TO

COMMUNICATE ITS VALUE• WE DON’T NEED INNOVATION BUT CAN YOU BRING US

CLIENTS

WE SAID WE ALSO DON’T

KNOW

BUT TOGETHER WE CAN FIND

OUT

AND TOGETHER WE CREATED

ANSWERS

Crafting Innovation Together

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Crafitti acts through active collaboration to

craft innovation in multiple contexts.

May 1, 2023 62© Crafitti Consulting Private Ltd.

CRAFITTI: Your Global Innovation ThinkTank

Increasing Innovation Momentum in the Enterprise• A systematic, time-

bound, flexible, initiative to transform the Enterprise into Invention Empowered Enterprise

• Unlike other such initiatives we work closely with each and every mind of the Enterprise based on scientific methods honed through years of practice

• The Enterprise is self-enabled and empowered to adapt to and in fact architect positive change continuously

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Crafitti Consulting: Background

63

• INNOVATION RESEARCH AND CONSULTING in business, science and technology contexts

• Started in June 2008 and was incubated at the NSR Centre for Entrepreneurial Learning at IIM Bangalore

• Crafitti’s frameworks provide potent platforms to innovate in crafting strategies, breakthrough products, new services, technological alternatives, patent portfolios, process design and embedding successful change in organizations.

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May 1, 2023 64

Thank You!

Navneet [email protected]

+91 9902766961

INVENT ! TOGETHER

System modeling using ERG and Petri Nets

Software

Transcript of System modeling using ERG and Petri Nets