optimization in chastrobe software with genetic algorithm

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(VII) OPTIMIZATION 1 OPTIMIZATION IN CHASTROBE WITH GENETIC ALGORITHM Optimizing Project Duration and Idle Time in a Repetitive Project with Work Breaks and Resource-Sharing Activities Presented by Chachrist Srisuwanrat 2008

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Explanation of Genetic Algorithm in simulation and example of repetitive project modeled in ChaStrobe software in order to optimize project duration and idle time in the example with following features: 1. probabilistic activity durations 2. resource-sharing activities 3. options of scheduling work breaks 4. consider relaxing continuous resource utilization constraints

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Page 1: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 1

OPTIMIZATION IN CHASTROBE

WITH GENETIC ALGORITHM

Optimizing Project Duration and Idle Time in a Repetitive Project with Work Breaks and

Resource-Sharing Activities

Presented by

Chachrist Srisuwanrat

2008

Page 2: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 2

OPTIMIZATION IN CHASTROBE

Three Levels of Simulation Code and

Model Manipulation

• Parameter Manipulation

• Simulation Code Manipulation

• Simulation Model Manipulation

Two Search Methods

• The Exhaustive Search

• The Genetic Algorithm

Page 3: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 3

Flowchart of ChaStrobe’s Optimization

Page 4: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 4

GA OPTIMIZATION IN

CHASTROBE

Page 5: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 5

GA OPTIMIZATION IN

CHASTROBE (Cont.)

Page 6: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 6

Decision Variable Cells and Dynamic

Code Input

(b) Dynamic Code Input, Simulation

Code, and Cells referencing the

Decision Variable Cells

(a) Search Input, Decision Variable

Cells in Row 2, and Domain Values

for each Decision Variables

Page 7: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 7

Dynamic Code Positions

Page 8: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 8

User-Specified Objective Function in

Additional Code

Objective Function stored in ObjFunc variable,

and additional user-specified output

Page 9: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 9

Search Parameters for Optimization

Page 10: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 10

Intermediate Results from GA

Optimization

Page 11: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 11

Example of ChaStrobe’s Optimization

for a Repetitive Project with Resource-

Sharing Activities and Work Breaks

ACT Variability

Unit

1 2 3 4 5

Duration

A Normal[1,0.1] 40 45 40 40 45

M Normal[1,0.1] 15 15 10 10 10

B Normal[1,0.1] 50 40 50 50 40

X Normal[1,0.1] 20 30 25 20 20

U Normal[1,0.1] 15 20 15 25 20

V Normal[1,0.1] 40 40 45 45 40

C Normal[1,0.1] 15 15 15 15 15

N Normal[1,0.1] 20 25 30 20 25

Y Normal[1,0.1] 20 20 20 20 20

D Normal[1,0.1] 45 35 40 40 30

A Normal[1,0.1] 40 45 40 40 45

Page 12: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 12

Results

Page 13: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 13

Results: An unusual up-and-down

pattern in project duration and idle Time

Page 14: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 14

Optimization Input

Page 15: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 15

Search Input and Dynamic Input Code

for Optimization

Page 16: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 16

Objective Function

Page 17: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 17

Flowchart of ChaStrobe’s Optimization

Page 18: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 18

GA Results

Without optimization, the objective function value is -79 days

(1200 - project duration – project idle time = 1200-746 – 533)

Page 19: Optimization in ChaStrobe Software with Genetic Algorithm

(VII) OPTIMIZATION 19

Results from using GA solution

Objective Function Value is derived from:

1200 – Project Duration – Project Idle Time

Method Project

Duration

Project

Idle time

Objective

Function

Value

CPM 416 944 -160

SQS-AL 746 533 -79

SQS-AL* 591 21 588