Optimization in ChaStrobe Software with Genetic Algorithm
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The document discusses the optimization of project duration and idle time in Chastrobe using a genetic algorithm and other simulation methods. It includes several optimization techniques, decision variables with dynamic input, and results showcasing the performance of the genetic algorithm. Key findings include intermediate results and comparisons of project durations and idle times under different scenarios.
![(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](https://image.slidesharecdn.com/7optimizationinchastrobewithgeneticalgorithm-140819110915-phpapp01/85/Optimization-in-ChaStrobe-Software-with-Genetic-Algorithm-11-320.jpg)
Optimization in ChaStrobe Software with Genetic Algorithm
- 1. (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
- 2. (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
- 3. (VII) OPTIMIZATION 3 Flowchart of ChaStrobe’s Optimization
- 4. (VII) OPTIMIZATION 4 GA OPTIMIZATION IN CHASTROBE
- 5. (VII) OPTIMIZATION 5 GA OPTIMIZATION IN CHASTROBE (Cont.)
- 6. (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
- 7. (VII) OPTIMIZATION 7 Dynamic Code Positions
- 8. (VII) OPTIMIZATION 8 User-Specified Objective Function in Additional Code Objective Function stored in ObjFunc variable, and additional user-specified output
- 9. (VII) OPTIMIZATION 9 Search Parameters for Optimization
- 10. (VII) OPTIMIZATION 10 Intermediate Results from GA Optimization
- 11. (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
- 13. (VII) OPTIMIZATION 13 Results: An unusual up-and-down pattern in project duration and idle Time
- 14. (VII) OPTIMIZATION 14 Optimization Input
- 15. (VII) OPTIMIZATION 15 Search Input and Dynamic Input Code for Optimization
- 16. (VII) OPTIMIZATION 16 Objective Function
- 17. (VII) OPTIMIZATION 17 Flowchart of ChaStrobe’s Optimization
- 18. (VII) OPTIMIZATION 18 GA Results Without optimization, the objective function value is -79 days (1200 - project duration – project idle time = 1200-746 – 533)
- 19. (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