Optimization in ChaStrobe Software with Genetic Algorithm
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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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Cited from my thesis:
For Professor Photios G. Ioannou.
My academic journey would not have been possible without my beloved and respected advisor, Professor Photios G. Ioannou. Great indebtedness goes to his belief in me and the opportunity he has offered to continue my academic journey at the University of Michigan. It has been an honor to work under his direction. During the development of this research, he has always reinforced to me that we can make a difference, a great one. And here I am, with this research. His interesting guidance, valuable advice, and constructive skepticism have contributed to the achievement of this research. I wish him and his family a blissful and healthy life.
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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