SlideShare a Scribd company logo
Operational
Research
Hungarian Algorithm
Hungarian method
 This method is a “Combinatorial Optimization
Algorithm” that solves assignment problems
 Developed and published by Harold Kuhn in
1955
 Basically, this method is for assigning jobs by
one-for-one matching to identify the lowest
cost solution
 This method is actually a special case of
Primal-Dual algorithm
2
Example
A building firm possesses four cranes each of which
has a distance(km) from four different construction
sites as shown in the table
Construction site #
1 2 3 4
Crane #
1 90 75 75 80
2 35 85 55 65
3 125 95 90 105
4 45 110 95 115
3
The objective is to place the cranes in such a way
that the overall distance required for the transfer is as
small as possible
Instructions
1. Subtract the smallest number in each row from every number in the row. This is
called a row reduction. Enter the results in a new table.
2. Subtract the smallest number in each column of the new table from every
number in the column. This is called a column reduction. Enter the results in
another table.
3. Test whether an optimum assignment can be made. You do this by
determining the minimum number of lines needed to cover all zeros. If the
number of lines equals the number of rows, an optimum assignment is possible. In
that case, go to step 6. Otherwise go on to step 4.
4. If the number of lines is less than the number of rows, modify the table in this
way:
a. Subtract the smallest uncovered number from every uncovered
number in the table.
b. Add the smallest uncovered number to the numbers at intersections
of covering lines.
c. Numbers crossed out but not at intersections of cross-out lines carry
over unchanged to the next table.
5. Repeat steps 3 and 4 until an optimal table is obtained.
6. Make the assignments. Begin with rows or columns with only one zero. Match
items that have zeros, using only one match for each row and each column.
Cross out both the row and the column after the match.
4
Solution
 The COST matrix is:
5
90 75 75 80
35 85 55 65
125 95 90 105
45 110 95 115
Step 01
Find the Row minimum for each row and subtract it
from all entries on that row
 Resultant matrix is:
15 0 0 5
0 50 20 30
35 5 0 15
0 65 50 70
90 75 75 80
35 85 55 65
125 95 90 105
45 110 95 115
Solution Cont….
6
 Resultant matrix is:
15 0 0 0
0 50 20 25
35 5 0 10
0 65 50 65
Step 02
From each row, find the Row minimum and subtract
it from all entries in that row
Step 03
Draw a minimum number of lines across rows and
columns so that all the “zeros” are covered.
Solution Cont.
7
Step 04 – Test for optimality
Number of lines = 3
Number of rows in the Cost matrix = 4
3 ≠ 4  An optimum assignment is not possible
Step 05
Find the Smallest entry which is not covered by the
lines  20
and subtract it from each entry not covered by the
lines.
We add the smallest entry to the crossed entries
15 0 0 0
0 50 20 25
35 5 0 10
0 65 50 65
Solution Cont.
8
Repeat Step 03
35 0 0 0
0 30 0 5
55 5 0 10
0 45 30 45
 Resultant matrix is:
Number of lines = 3
Number of rows in the Cost matrix = 4
3 ≠ 4  An optimum assignment is not possible
35 0 0 0
0 30 0 5
55 5 0 10
0 45 30 45
Solution Cont.
9
40 0 5 0
0 25 0 0
55 0 0 5
0 40 30 40
If the number of lines = 4
Number of rows in the Cost matrix = 4
4 = 4  An optimum assignment is possible.
‘0’ positions determine the possible combinations
Repeat Step 03
Solution Cont.
10
1 2 3 4
1 40 0 5 0
2 0 25 0 0
3 55 0 0 5
4 0 40 30 40
Option 01
Crane 1  Site 2
Crane 2  Site 4
Crane 3  Site 3
Crane 4  Site 1
Distance = 275
Construction Site #
Crane#
Select a matching by choosing a set of zeros so that each row
or column has only one selected
Option 02
Crane 1  Site 4
Crane 2  Site 3
Crane 3  Site 2
Crane 4  Site 1
Distance = 275
 Hungarian Method is for assigning jobs by
a one-for-one matching to identify the
lowest-cost solution where each job must
be assigned to only one machine.
11
Conclusion
12
C. S. Suriyakula
AS/07/08/077

More Related Content

What's hot

Assignment Problem
Assignment ProblemAssignment Problem
Assignment Problem
Nakul Bhardwaj
 
Assignment Chapter - Q & A Compilation by Niraj Thapa
Assignment Chapter  - Q & A Compilation by Niraj ThapaAssignment Chapter  - Q & A Compilation by Niraj Thapa
Assignment Chapter - Q & A Compilation by Niraj Thapa
CA Niraj Thapa
 
Transportation problem_Operation research
Transportation problem_Operation researchTransportation problem_Operation research
Transportation problem_Operation research
Ataur Rahman
 
Transportation problems
Transportation problemsTransportation problems
Transportation problems
VishalHotchandani2
 
Game Theory - Dominance Strategy
Game Theory - Dominance StrategyGame Theory - Dominance Strategy
Game Theory - Dominance Strategy
Sundar B N
 
Linear Programming (graphical method)
Linear Programming (graphical method)Linear Programming (graphical method)
Linear Programming (graphical method)
Kamel Attar
 
Game Theory Operation Research
Game Theory Operation ResearchGame Theory Operation Research
Game Theory Operation Research
R A Shah
 
Transportation problem
Transportation problemTransportation problem
Transportation problem
Shubhagata Roy
 
Two Phase Method- Linear Programming
Two Phase Method- Linear ProgrammingTwo Phase Method- Linear Programming
Two Phase Method- Linear Programming
Manas Lad
 
Deepika(14 mba5012) transportation ppt
Deepika(14 mba5012)  transportation pptDeepika(14 mba5012)  transportation ppt
Deepika(14 mba5012) transportation pptDeepika Bansal
 
North West Corner Rule
North   West Corner RuleNorth   West Corner Rule
North West Corner Ruleitsvineeth209
 
simplex method
simplex methodsimplex method
simplex method
Dronak Sahu
 
Assignment problem
Assignment problemAssignment problem
Assignment problem
Anggy Herny Anggraeni
 
Lesson 33: The Assignment Problem
Lesson 33: The Assignment  ProblemLesson 33: The Assignment  Problem
Lesson 33: The Assignment Problem
Matthew Leingang
 
Linear Programming
Linear ProgrammingLinear Programming
Linear Programming
Pulchowk Campus
 
Assignment Poblems
Assignment Poblems Assignment Poblems
Assignment Poblems
vkabre
 
Simplex method: Slack, Surplus & Artificial variable
Simplex method:  Slack, Surplus & Artificial variableSimplex method:  Slack, Surplus & Artificial variable
Simplex method: Slack, Surplus & Artificial variable
DevyaneeDevyanee2007
 

What's hot (20)

Assignment Problem
Assignment ProblemAssignment Problem
Assignment Problem
 
Assignment Chapter - Q & A Compilation by Niraj Thapa
Assignment Chapter  - Q & A Compilation by Niraj ThapaAssignment Chapter  - Q & A Compilation by Niraj Thapa
Assignment Chapter - Q & A Compilation by Niraj Thapa
 
graphical method
graphical method graphical method
graphical method
 
Transportation problem_Operation research
Transportation problem_Operation researchTransportation problem_Operation research
Transportation problem_Operation research
 
Transportation problems
Transportation problemsTransportation problems
Transportation problems
 
Game Theory - Dominance Strategy
Game Theory - Dominance StrategyGame Theory - Dominance Strategy
Game Theory - Dominance Strategy
 
Linear Programming (graphical method)
Linear Programming (graphical method)Linear Programming (graphical method)
Linear Programming (graphical method)
 
Game Theory Operation Research
Game Theory Operation ResearchGame Theory Operation Research
Game Theory Operation Research
 
Vam
VamVam
Vam
 
Assignment problem
Assignment problemAssignment problem
Assignment problem
 
Transportation problem
Transportation problemTransportation problem
Transportation problem
 
Two Phase Method- Linear Programming
Two Phase Method- Linear ProgrammingTwo Phase Method- Linear Programming
Two Phase Method- Linear Programming
 
Deepika(14 mba5012) transportation ppt
Deepika(14 mba5012)  transportation pptDeepika(14 mba5012)  transportation ppt
Deepika(14 mba5012) transportation ppt
 
North West Corner Rule
North   West Corner RuleNorth   West Corner Rule
North West Corner Rule
 
simplex method
simplex methodsimplex method
simplex method
 
Assignment problem
Assignment problemAssignment problem
Assignment problem
 
Lesson 33: The Assignment Problem
Lesson 33: The Assignment  ProblemLesson 33: The Assignment  Problem
Lesson 33: The Assignment Problem
 
Linear Programming
Linear ProgrammingLinear Programming
Linear Programming
 
Assignment Poblems
Assignment Poblems Assignment Poblems
Assignment Poblems
 
Simplex method: Slack, Surplus & Artificial variable
Simplex method:  Slack, Surplus & Artificial variableSimplex method:  Slack, Surplus & Artificial variable
Simplex method: Slack, Surplus & Artificial variable
 

Viewers also liked

Assgnment=hungarian method
Assgnment=hungarian methodAssgnment=hungarian method
Assgnment=hungarian methodJoseph Konnully
 
Assignment problem ppt
Assignment problem ppt Assignment problem ppt
Assignment problem ppt
Babasab Patil
 
Kuhn munkres algorithm
Kuhn munkres algorithmKuhn munkres algorithm
Kuhn munkres algorithm
Abdullah Nasser
 
Assignment problem
Assignment problemAssignment problem
Assignment problemAbu Bashar
 
Quantitative Analysis For Decision Making
Quantitative Analysis For Decision MakingQuantitative Analysis For Decision Making
Quantitative Analysis For Decision Making
aminsand
 
Operational research on Assignment ppt
Operational research on Assignment pptOperational research on Assignment ppt
Operational research on Assignment pptNirali Solanki
 
Multiple Hypothesis Tracking Algorithm
Multiple Hypothesis Tracking AlgorithmMultiple Hypothesis Tracking Algorithm
Multiple Hypothesis Tracking Algorithm
sersem1
 
Linear programming
Linear programmingLinear programming
Linear programmingKrantee More
 
Qm assignment problem slides
Qm assignment problem slidesQm assignment problem slides
Qm assignment problem slidesAmit Pai
 
Application of greedy method
Application  of  greedy methodApplication  of  greedy method
Application of greedy methodTech_MX
 
Game theory
Game theoryGame theory
Game theory
NEELAM KUSHWAHA
 
Solving travelling salesman problem using firefly algorithm
Solving travelling salesman problem using firefly algorithmSolving travelling salesman problem using firefly algorithm
Solving travelling salesman problem using firefly algorithm
ishmecse13
 
Ap for b.tech. (mechanical) Assignment Problem
Ap for b.tech. (mechanical) Assignment Problem Ap for b.tech. (mechanical) Assignment Problem
Ap for b.tech. (mechanical) Assignment Problem
Prashant Khandelwal
 
Multi Agent Systems presentation
Multi Agent Systems presentationMulti Agent Systems presentation
Multi Agent Systems presentation
Aditya Gupta
 
Management of interest rate risk
Management of interest rate riskManagement of interest rate risk
Management of interest rate risk
spagi
 
1.2 numbers 0 30
1.2 numbers 0 301.2 numbers 0 30
1.2 numbers 0 30
aperlick
 
Management of interest rate risk
Management of interest rate riskManagement of interest rate risk
Management of interest rate risk
Sonam Basia
 
Travelling Salesman Problem
Travelling Salesman ProblemTravelling Salesman Problem
Travelling Salesman ProblemDaniel Raditya
 
Greedy algorithm
Greedy algorithmGreedy algorithm
Greedy algorithm
Caisar Oentoro
 
Greedy Algorithms
Greedy AlgorithmsGreedy Algorithms
Greedy Algorithms
Amrinder Arora
 

Viewers also liked (20)

Assgnment=hungarian method
Assgnment=hungarian methodAssgnment=hungarian method
Assgnment=hungarian method
 
Assignment problem ppt
Assignment problem ppt Assignment problem ppt
Assignment problem ppt
 
Kuhn munkres algorithm
Kuhn munkres algorithmKuhn munkres algorithm
Kuhn munkres algorithm
 
Assignment problem
Assignment problemAssignment problem
Assignment problem
 
Quantitative Analysis For Decision Making
Quantitative Analysis For Decision MakingQuantitative Analysis For Decision Making
Quantitative Analysis For Decision Making
 
Operational research on Assignment ppt
Operational research on Assignment pptOperational research on Assignment ppt
Operational research on Assignment ppt
 
Multiple Hypothesis Tracking Algorithm
Multiple Hypothesis Tracking AlgorithmMultiple Hypothesis Tracking Algorithm
Multiple Hypothesis Tracking Algorithm
 
Linear programming
Linear programmingLinear programming
Linear programming
 
Qm assignment problem slides
Qm assignment problem slidesQm assignment problem slides
Qm assignment problem slides
 
Application of greedy method
Application  of  greedy methodApplication  of  greedy method
Application of greedy method
 
Game theory
Game theoryGame theory
Game theory
 
Solving travelling salesman problem using firefly algorithm
Solving travelling salesman problem using firefly algorithmSolving travelling salesman problem using firefly algorithm
Solving travelling salesman problem using firefly algorithm
 
Ap for b.tech. (mechanical) Assignment Problem
Ap for b.tech. (mechanical) Assignment Problem Ap for b.tech. (mechanical) Assignment Problem
Ap for b.tech. (mechanical) Assignment Problem
 
Multi Agent Systems presentation
Multi Agent Systems presentationMulti Agent Systems presentation
Multi Agent Systems presentation
 
Management of interest rate risk
Management of interest rate riskManagement of interest rate risk
Management of interest rate risk
 
1.2 numbers 0 30
1.2 numbers 0 301.2 numbers 0 30
1.2 numbers 0 30
 
Management of interest rate risk
Management of interest rate riskManagement of interest rate risk
Management of interest rate risk
 
Travelling Salesman Problem
Travelling Salesman ProblemTravelling Salesman Problem
Travelling Salesman Problem
 
Greedy algorithm
Greedy algorithmGreedy algorithm
Greedy algorithm
 
Greedy Algorithms
Greedy AlgorithmsGreedy Algorithms
Greedy Algorithms
 

Similar to Hungarian algorithm

Chapter 1 Assignment Problems (DS) (1).pptx
Chapter 1 Assignment Problems (DS) (1).pptxChapter 1 Assignment Problems (DS) (1).pptx
Chapter 1 Assignment Problems (DS) (1).pptx
PriyankaLunavat
 
Hungarian Assignment Problem
Hungarian Assignment ProblemHungarian Assignment Problem
Hungarian Assignment Problem
VivekSaurabh7
 
Quantitativeanalysisfordecisionmaking 13427543542352-phpapp02-120719222252-ph...
Quantitativeanalysisfordecisionmaking 13427543542352-phpapp02-120719222252-ph...Quantitativeanalysisfordecisionmaking 13427543542352-phpapp02-120719222252-ph...
Quantitativeanalysisfordecisionmaking 13427543542352-phpapp02-120719222252-ph...
Firas Husseini
 
Asssignment problem
Asssignment problemAsssignment problem
Asssignment problem
Mamatha Upadhya
 
qadm-ppt-150918102124-lva1-app6892.pdf
qadm-ppt-150918102124-lva1-app6892.pdfqadm-ppt-150918102124-lva1-app6892.pdf
qadm-ppt-150918102124-lva1-app6892.pdf
Hari31856
 
Decision Science.pdf
Decision Science.pdfDecision Science.pdf
Decision Science.pdf
pandeyaman577
 
VOGEL'S APPROXIMATION METHOD
VOGEL'S APPROXIMATION METHODVOGEL'S APPROXIMATION METHOD
VOGEL'S APPROXIMATION METHOD
SwethaShree13
 
Simplex part 3 of 4
Simplex part 3 of 4Simplex part 3 of 4
Simplex part 3 of 4
Ed Dansereau
 
just reference
just referencejust reference
just reference
Sumin Kim
 
upload
uploadupload
upload
Sumin Kim
 
Basic math
Basic mathBasic math
Basic math
FathimaRifa
 
Unit ii-3-am
Unit ii-3-amUnit ii-3-am
Unit ii-3-am
Anurag Srivastava
 
Bcolz Groupby Discussion Document
Bcolz Groupby Discussion DocumentBcolz Groupby Discussion Document
Bcolz Groupby Discussion Document
Carst Vaartjes
 
MODI Method (Operations Research)
MODI Method (Operations Research) MODI Method (Operations Research)
MODI Method (Operations Research)
Nilraj Vasandia
 
Vogel's Approximation Method
Vogel's Approximation MethodVogel's Approximation Method
Vogel's Approximation Method
UsharaniRavikumar
 
Assignment problem
Assignment problemAssignment problem
Assignment problem
RafidahAli1
 
Algorithm for Hungarian Method of Assignment
Algorithm for Hungarian Method of AssignmentAlgorithm for Hungarian Method of Assignment
Algorithm for Hungarian Method of Assignment
Raja Adapa
 
Assignment method
Assignment methodAssignment method
Assignment method
R A Shah
 
A Comparative Analysis Of Assignment Problem
A Comparative Analysis Of Assignment ProblemA Comparative Analysis Of Assignment Problem
A Comparative Analysis Of Assignment Problem
Jim Webb
 

Similar to Hungarian algorithm (20)

Chapter 1 Assignment Problems (DS) (1).pptx
Chapter 1 Assignment Problems (DS) (1).pptxChapter 1 Assignment Problems (DS) (1).pptx
Chapter 1 Assignment Problems (DS) (1).pptx
 
Hungarian Assignment Problem
Hungarian Assignment ProblemHungarian Assignment Problem
Hungarian Assignment Problem
 
Quantitativeanalysisfordecisionmaking 13427543542352-phpapp02-120719222252-ph...
Quantitativeanalysisfordecisionmaking 13427543542352-phpapp02-120719222252-ph...Quantitativeanalysisfordecisionmaking 13427543542352-phpapp02-120719222252-ph...
Quantitativeanalysisfordecisionmaking 13427543542352-phpapp02-120719222252-ph...
 
Asssignment problem
Asssignment problemAsssignment problem
Asssignment problem
 
qadm-ppt-150918102124-lva1-app6892.pdf
qadm-ppt-150918102124-lva1-app6892.pdfqadm-ppt-150918102124-lva1-app6892.pdf
qadm-ppt-150918102124-lva1-app6892.pdf
 
Decision Science.pdf
Decision Science.pdfDecision Science.pdf
Decision Science.pdf
 
VOGEL'S APPROXIMATION METHOD
VOGEL'S APPROXIMATION METHODVOGEL'S APPROXIMATION METHOD
VOGEL'S APPROXIMATION METHOD
 
Simplex part 3 of 4
Simplex part 3 of 4Simplex part 3 of 4
Simplex part 3 of 4
 
just reference
just referencejust reference
just reference
 
basics
basicsbasics
basics
 
upload
uploadupload
upload
 
Basic math
Basic mathBasic math
Basic math
 
Unit ii-3-am
Unit ii-3-amUnit ii-3-am
Unit ii-3-am
 
Bcolz Groupby Discussion Document
Bcolz Groupby Discussion DocumentBcolz Groupby Discussion Document
Bcolz Groupby Discussion Document
 
MODI Method (Operations Research)
MODI Method (Operations Research) MODI Method (Operations Research)
MODI Method (Operations Research)
 
Vogel's Approximation Method
Vogel's Approximation MethodVogel's Approximation Method
Vogel's Approximation Method
 
Assignment problem
Assignment problemAssignment problem
Assignment problem
 
Algorithm for Hungarian Method of Assignment
Algorithm for Hungarian Method of AssignmentAlgorithm for Hungarian Method of Assignment
Algorithm for Hungarian Method of Assignment
 
Assignment method
Assignment methodAssignment method
Assignment method
 
A Comparative Analysis Of Assignment Problem
A Comparative Analysis Of Assignment ProblemA Comparative Analysis Of Assignment Problem
A Comparative Analysis Of Assignment Problem
 

Recently uploaded

Knowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and backKnowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and back
Elena Simperl
 
DevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA ConnectDevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA Connect
Kari Kakkonen
 
Assuring Contact Center Experiences for Your Customers With ThousandEyes
Assuring Contact Center Experiences for Your Customers With ThousandEyesAssuring Contact Center Experiences for Your Customers With ThousandEyes
Assuring Contact Center Experiences for Your Customers With ThousandEyes
ThousandEyes
 
JMeter webinar - integration with InfluxDB and Grafana
JMeter webinar - integration with InfluxDB and GrafanaJMeter webinar - integration with InfluxDB and Grafana
JMeter webinar - integration with InfluxDB and Grafana
RTTS
 
Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*
Frank van Harmelen
 
GraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge GraphGraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge Graph
Guy Korland
 
PCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase TeamPCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase Team
ControlCase
 
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
Product School
 
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Designing Great Products: The Power of Design and Leadership by Chief Designe...Designing Great Products: The Power of Design and Leadership by Chief Designe...
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Product School
 
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualitySoftware Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Inflectra
 
The Future of Platform Engineering
The Future of Platform EngineeringThe Future of Platform Engineering
The Future of Platform Engineering
Jemma Hussein Allen
 
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
UiPathCommunity
 
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Albert Hoitingh
 
How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...
Product School
 
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
Product School
 
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
Tobias Schneck
 
FIDO Alliance Osaka Seminar: Overview.pdf
FIDO Alliance Osaka Seminar: Overview.pdfFIDO Alliance Osaka Seminar: Overview.pdf
FIDO Alliance Osaka Seminar: Overview.pdf
FIDO Alliance
 
Key Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdfKey Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdf
Cheryl Hung
 
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
State of ICS and IoT Cyber Threat Landscape Report 2024 previewState of ICS and IoT Cyber Threat Landscape Report 2024 preview
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
Prayukth K V
 
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Thierry Lestable
 

Recently uploaded (20)

Knowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and backKnowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and back
 
DevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA ConnectDevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA Connect
 
Assuring Contact Center Experiences for Your Customers With ThousandEyes
Assuring Contact Center Experiences for Your Customers With ThousandEyesAssuring Contact Center Experiences for Your Customers With ThousandEyes
Assuring Contact Center Experiences for Your Customers With ThousandEyes
 
JMeter webinar - integration with InfluxDB and Grafana
JMeter webinar - integration with InfluxDB and GrafanaJMeter webinar - integration with InfluxDB and Grafana
JMeter webinar - integration with InfluxDB and Grafana
 
Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*
 
GraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge GraphGraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge Graph
 
PCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase TeamPCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase Team
 
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
 
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Designing Great Products: The Power of Design and Leadership by Chief Designe...Designing Great Products: The Power of Design and Leadership by Chief Designe...
Designing Great Products: The Power of Design and Leadership by Chief Designe...
 
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualitySoftware Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
 
The Future of Platform Engineering
The Future of Platform EngineeringThe Future of Platform Engineering
The Future of Platform Engineering
 
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
 
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
 
How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...
 
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
 
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
 
FIDO Alliance Osaka Seminar: Overview.pdf
FIDO Alliance Osaka Seminar: Overview.pdfFIDO Alliance Osaka Seminar: Overview.pdf
FIDO Alliance Osaka Seminar: Overview.pdf
 
Key Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdfKey Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdf
 
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
State of ICS and IoT Cyber Threat Landscape Report 2024 previewState of ICS and IoT Cyber Threat Landscape Report 2024 preview
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
 
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
 

Hungarian algorithm

  • 2. Hungarian method  This method is a “Combinatorial Optimization Algorithm” that solves assignment problems  Developed and published by Harold Kuhn in 1955  Basically, this method is for assigning jobs by one-for-one matching to identify the lowest cost solution  This method is actually a special case of Primal-Dual algorithm 2
  • 3. Example A building firm possesses four cranes each of which has a distance(km) from four different construction sites as shown in the table Construction site # 1 2 3 4 Crane # 1 90 75 75 80 2 35 85 55 65 3 125 95 90 105 4 45 110 95 115 3 The objective is to place the cranes in such a way that the overall distance required for the transfer is as small as possible
  • 4. Instructions 1. Subtract the smallest number in each row from every number in the row. This is called a row reduction. Enter the results in a new table. 2. Subtract the smallest number in each column of the new table from every number in the column. This is called a column reduction. Enter the results in another table. 3. Test whether an optimum assignment can be made. You do this by determining the minimum number of lines needed to cover all zeros. If the number of lines equals the number of rows, an optimum assignment is possible. In that case, go to step 6. Otherwise go on to step 4. 4. If the number of lines is less than the number of rows, modify the table in this way: a. Subtract the smallest uncovered number from every uncovered number in the table. b. Add the smallest uncovered number to the numbers at intersections of covering lines. c. Numbers crossed out but not at intersections of cross-out lines carry over unchanged to the next table. 5. Repeat steps 3 and 4 until an optimal table is obtained. 6. Make the assignments. Begin with rows or columns with only one zero. Match items that have zeros, using only one match for each row and each column. Cross out both the row and the column after the match. 4
  • 5. Solution  The COST matrix is: 5 90 75 75 80 35 85 55 65 125 95 90 105 45 110 95 115 Step 01 Find the Row minimum for each row and subtract it from all entries on that row  Resultant matrix is: 15 0 0 5 0 50 20 30 35 5 0 15 0 65 50 70 90 75 75 80 35 85 55 65 125 95 90 105 45 110 95 115
  • 6. Solution Cont…. 6  Resultant matrix is: 15 0 0 0 0 50 20 25 35 5 0 10 0 65 50 65 Step 02 From each row, find the Row minimum and subtract it from all entries in that row Step 03 Draw a minimum number of lines across rows and columns so that all the “zeros” are covered.
  • 7. Solution Cont. 7 Step 04 – Test for optimality Number of lines = 3 Number of rows in the Cost matrix = 4 3 ≠ 4  An optimum assignment is not possible Step 05 Find the Smallest entry which is not covered by the lines  20 and subtract it from each entry not covered by the lines. We add the smallest entry to the crossed entries 15 0 0 0 0 50 20 25 35 5 0 10 0 65 50 65
  • 8. Solution Cont. 8 Repeat Step 03 35 0 0 0 0 30 0 5 55 5 0 10 0 45 30 45  Resultant matrix is: Number of lines = 3 Number of rows in the Cost matrix = 4 3 ≠ 4  An optimum assignment is not possible 35 0 0 0 0 30 0 5 55 5 0 10 0 45 30 45
  • 9. Solution Cont. 9 40 0 5 0 0 25 0 0 55 0 0 5 0 40 30 40 If the number of lines = 4 Number of rows in the Cost matrix = 4 4 = 4  An optimum assignment is possible. ‘0’ positions determine the possible combinations Repeat Step 03
  • 10. Solution Cont. 10 1 2 3 4 1 40 0 5 0 2 0 25 0 0 3 55 0 0 5 4 0 40 30 40 Option 01 Crane 1  Site 2 Crane 2  Site 4 Crane 3  Site 3 Crane 4  Site 1 Distance = 275 Construction Site # Crane# Select a matching by choosing a set of zeros so that each row or column has only one selected Option 02 Crane 1  Site 4 Crane 2  Site 3 Crane 3  Site 2 Crane 4  Site 1 Distance = 275
  • 11.  Hungarian Method is for assigning jobs by a one-for-one matching to identify the lowest-cost solution where each job must be assigned to only one machine. 11 Conclusion