Evolutionary Optimization ToolFor Construction

Activities

Evolutionary Optimization ToolFor Construction

ActivitiesActivitiesActivities

Upgraded by

Dr. Ashraf A.F. Girgis

استخدام اداه للحلول التطورية المثلي �دارة أنشطة البناء

Presentation OutlinePresentation Outline

� General description– Genetic Algorithm optimization tool.– Genetic Reproduction.

� Structure & features of the tool.� How to design Xls file for opt. problem.

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� How to design Xls file for opt. problem.� Live demonstration – Examples.� Licensing from:� Getting GANetXL or SolveXL.� Links

Genetic Algorithm optimization toolGenetic Algorithm optimization tool

� A user friendly add-in which integrates into Microsoft Excel.

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� Uses evolutionary algorithms to solve complex optimisation problems.

� Interacts with Excel interface.� Any solution is a Chromosome which is

represented as a line of Genes (variables).

Genetic ReproductionGenetic Reproduction

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من كل جيل بصورة عشوائية بحيث يكون كل منھم ا فضل ) أبوين(يتم اختيار حلين ) بأي طريقة من الطرق المبينه(لياقة لتحقيق الشروط ثم يتم عمل التزاوج بينھما

.�نتاج جيل جديد من الحلول

يحدث بعض تحور للجينات بالجيل الجديد والحل الذي يستمر للجيل التالي ھو ا فضل والذي يحقق الشروط ، أما الحلول التي 5 تحقق أفضل لياقة للشروط فتنتھي

.وتموت

Structure of the Tool (GANetXL or SolveXL)Structure of the Tool (GANetXL or SolveXL)

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FeaturesFeatures� Single or multiple-objective optimization

techniques.� User defined constraints & penalty

multipliers.� Visualization of results and progress.

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� Visualization of results and progress.� Built-in help.� User manual.

Design Xls file for optimization problemDesign Xls file for optimization problem

� Decision variables – Genesaltered by GA within theirranges (x1,x2,x3,x4)must occupy continuous range

� Objective function(s)used to evaluate the

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used to evaluate thefitness of solutions (formula in C6)

� Constraintsused to limit values ofobjective function(s)(formula in C8)

Practical ExamplesPractical Examples1. Single-objective

– Optimum Quantity to be Supplied From Each Concrete Mixing Plant to Each Project.

2. Multiple-objective– Project Time - Cost trade-off Optimization,

According to Schedule.

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According to Schedule.– Project Time - Cost trade-off Optimization, with Loss

of Productivity.– Optimization Model of External Resource Allocation.– Readjustment of Schedule Activities to Overcome

Discovered Delays.

Example 1Optimum Quantity to be Supplied From Each Concrete Mixing Plant

to Each Project

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to Each Project(Specially designed Xls file)

Example 1 : Optimum Quantity to be Supplied From Each Plant to Each Project: (3 Patch Plants & 3 Projects A, B & C)Example 1 : Optimum Quantity to be Supplied From Each Plant to Each Project: (3 Patch Plants & 3 Projects A, B & C)

� 15 Delivery options for each project:

Specific concrete quantities are required for three projects to supplied from three concrete mixing plants of limited capacities with different costs for transportations; at minimum cost.

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� 15 Delivery options for each project:- 0 for 0% of required.- 1 for 25% of req.- 2 for 50% of req.- 3 for 75% of req.- 4 for 100% of req.

Example 1 : Optimum Quantity to be Supplied From Each Plant to Each Project:- (Cont.) Specially designed Xls fileBefore optimization:

Example 1 : Optimum Quantity to be Supplied From Each Plant to Each Project:- (Cont.) Specially designed Xls fileBefore optimization:

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Example 1 : Optimum Quantity to be Supplied From Each Plant to Each Project:- (Cont.) Specially designed Xls file Before optimization:

Example 1 : Optimum Quantity to be Supplied From Each Plant to Each Project:- (Cont.) Specially designed Xls file Before optimization:

Alternative

to A 1 Plant P1

to A 2 Plant P2

Price alternatives for percentages of supplying concrete for each project from each mixing plant. (Without optimization)

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to A 2 Plant P2

to A 3 Plant P3

to B 1 Plant P1

to B 2 Plant P2

to B 3 Plant P3

to C 1 Plant P1

to C 2 Plant P2

to C 3 Plant P3

Example 1 : Optimum Quantity to be Supplied From Each Plant to Each Project:- (Cont .) Specially designed Xls file Before optimization:

Example 1 : Optimum Quantity to be Supplied From Each Plant to Each Project:- (Cont .) Specially designed Xls file Before optimization:

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Example 1 : Optimum Quantity to be Supplied From Each Plant to Each Project: (Cont.) Specially designed Xls file After optimization: Saving > 50%

Example 1 : Optimum Quantity to be Supplied From Each Plant to Each Project: (Cont.) Specially designed Xls file After optimization: Saving > 50%

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Example 2Multi Objective Optimization

Project Time - Cost Optimization According to Schedule

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According to Schedule(Specially designed Xls file)

Example 2 : Multi Objective Optimization Specially designed Xls file Project Time - Cost Optimization According to Schedule :Example 2 : Multi Objective Optimization Specially designed Xls file Project Time - Cost Optimization According to Schedule :

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Example 2 : Multi Objective OptimizationProject Time - Cost Optimization According to Schedule After Multi Objective Optimization:

Example 2 : Multi Objective OptimizationProject Time - Cost Optimization According to Schedule After Multi Objective Optimization:

Duration 60 days.

Total Cost 150,000 $

Duration 63 days.

Total Cost 131,000 $ (13% less)

Duration 78 days. (Optimum)

Total Cost 107,500 $ (28% less)

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Duration 132 days.

Total Cost 95,800 $ (36% less)

Duration 105 days.

Total Cost 96,400 $

Example 3Multi Objective Optimization

Project Time - Cost Optimization with Loss of Productivity

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with Loss of Productivity(Specially designed Xls file)

Example 3 : Multi Objective OptimizationProject Time - Cost Optimization with Loss of ProductivityExample 3 : Multi Objective OptimizationProject Time - Cost Optimization with Loss of Productivity

As an improvement for Example (2), (Horner, R M W & Talhouni) equations of loss of productivity are included for excess number of crews, labor force and overtime hours.

Required is the best alternative for number of

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Required is the best alternative for number of crews, labor force and overtime for each activity for minimum overall cost and minimum total duration.

Example 3 : Multi Objective Optimization Project Time - Cost Optimization with Loss of ProductivityExample 3 : Multi Objective Optimization Project Time - Cost Optimization with Loss of Productivity

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Effects of Overtime% and Labor force in Loss of Productivity (Horner, R M W & Talhouni)Effects of Overtime% and Labor force in Loss of Productivity (Horner, R M W & Talhouni)

%Loss of Productivity vs %Over Time

y = -0.0303x2 + 0.4152x + 7E-16

R2 = 1

0%5%

10%15%20%25%30%35%40%45%

0% 20% 40% 60% 80% 100% 120%

Over Time % >40 hours/week

%L

oss

of

Pro

du

ctiv

ity

%Over time

Poly. (%Over time)

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Over Time % >40 hours/week

%Loss of Productivity vs %Increase of Labor force

y = -0.0292x4 + 0.1843x3 - 0.4234x2 + 0.5443x - 0.0021

R2 = 0.999

-10%

0%

10%

20%

30%

40%

50%

0% 50% 100% 150% 200% 250%

%Increase of Labor force

%L

oss

of

Pro

du

ctiv

ity

Example 3 : Multi Objective OptimizationProject Time - Cost Optimization with Loss of ProductivityAfter Optimization

Example 3 : Multi Objective OptimizationProject Time - Cost Optimization with Loss of ProductivityAfter Optimization

More Duration by 4.2%

With the Same Costs

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Same Durations

But More Cost by 4.0%

(More over time benefits with less cost)

Example 4 Multi Objective OptimizationOptimization Model of External

Resource Allocation

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Resource Allocation (Leu & Yang, 1999)

(Specially designed Xls file)

Example 4 : Multi Objective OptimizationOptimization Model of External Resource Allocation (Leu & Yang, 1999)

Example 4 : Multi Objective OptimizationOptimization Model of External Resource Allocation (Leu & Yang, 1999)

We have a project of nine activities, each requires three different resources. Each resource with different direct daily cost.

Many alternatives could be done for each activity of the project. For each alternative: a specific direct cost, related duration and required

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direct cost, related duration and required amount from each resource is required.

Project duration is specified by contract with daily penalty for delay as well as daily incentive for early completion.

It is required to determine the best alternative from each resource for each activity for minimum cost and minimum project duration.

Example 4 : Multi Objective Optimization Optimization Model of External Resource Allocation (Cont.)Example 4 : Multi Objective Optimization Optimization Model of External Resource Allocation (Cont.)

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Example 4 : Multi Objective OptimizationOptimization Model of External Resource Allocation (Cont.)After Optimization

Example 4 : Multi Objective OptimizationOptimization Model of External Resource Allocation (Cont.)After Optimization

Duration 53 days.

Total Cost Could be Ranging from 554,330.0$ up to 615,550.0$ (10% more)

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Variable Durations.

With the Same Total Cost

@ 570,000.0$

Example 5 Readjustment of Schedule Activities

to Overcome Discovered Delays

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Example 5 : Multi Objective OptimizationReadjustment of Activities to Overcome Discovered DelaysExample 5 : Multi Objective OptimizationReadjustment of Activities to Overcome Discovered Delays

� Readjust the project cost as low aspossible to overcome delay of 7 days has been discovered after finishing activity no.3, (by choosing suitable construction

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no.3, (by choosing suitable construction methods or material options for the rest of activities).

Example 5 : Multi Objective OptimizationReadjustment of Activities to Overcome Discovered Delays (Cont.)Example 5 : Multi Objective OptimizationReadjustment of Activities to Overcome Discovered Delays (Cont.)

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Example 5: Multi Objective OptimizationReadjustment of Activities to Overcome Discovered Delays (Cont.)Example 5: Multi Objective OptimizationReadjustment of Activities to Overcome Discovered Delays (Cont.)

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LicensingLicensing� Licenses are bound to specific computer

�

Registration +HW Identifier

Serial Number

RegistrationDetails

Serial No.

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� Serial number– contains expiration date– valid for limited number of days– chromosome size, population size– objectives count, generations count

Getting GANetXL or SolveXL Getting GANetXL or SolveXL Download from following site:

http://www.exeter.ac.uk/cws/ganetxlTo obtain a license contact:

Prof. Dragan Savic (d.savic@ex.ac.uk)

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Support, reporting bugs and problems:Josef Bicik (j.bicik@ex.ac.uk)

Thank you for your attention!

Questions & Discussion

Thank you for your attention!

Questions & DiscussionQuestions & DiscussionQuestions & Discussion

LinksLinks� Evolver 4.0 (Palisade)

http://www.palisade-europe.com/evolver/� Premium Solver Platform (Frontline Systems Inc. )

http://www.solver.com/xlsplatform.htm� OptWorks Excel (Pi Blue)

http://www.piblue.com/products/optworks_ex.html� GeneHunter (Ward Systems)

http://www.wardsystems.com/products.asp?p=genehunter� Generator (NewLight Industries, Ltd.)

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� Generator (NewLight Industries, Ltd.)http://www.nli-ltd.com/products/genetic_algorithms/generator.htm

� xl bit (XLPert Enterprise )http://www.xlpert.com/wxl%20bit.htm#gg1

� GenSheet (Inductive Solutions, Inc.)http://www.inductive.com/softgen.htm

� GA Optimization for MS Excel (freeware)http://www.alexschreyer.net/projects/xloptim/index.php