Simulation-based GA Optimization for Production Planning
description
Transcript of Simulation-based GA Optimization for Production Planning
![Page 1: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/1.jpg)
Simulation-based GA Optimization for Production
Planning
Juan Esteban Díaz LeivaDr Julia Handl
Bioma 2014September 13, 2014
![Page 2: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/2.jpg)
2
Production Planning
Production Plan
Production levels
Business objectives
Allocation of resources
![Page 3: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/3.jpg)
3
Production Planning
Lack of appropriate instrument
Inappropriate methods
Experience&
“Sixth sense”
![Page 4: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/4.jpg)
Aplicable solution
SimulationDES
OptimizationGA
Simulation-based Optimization
4
![Page 5: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/5.jpg)
Objective
Simulation-based
optimization
Support decision making
Feasibility
Applicablility
Robustness
Uncertainty &
Real-life complexity
Production Planning
5
![Page 6: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/6.jpg)
Simulation-based Optimization Model
6
Figure 1. Order processing subsystem for work centre .
![Page 7: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/7.jpg)
Simulation-based Optimization Model
7
Figure 2. Production subsystem for work centre .
Figure 3. Repair service station of work centre .
![Page 8: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/8.jpg)
Simulation-based Optimization Model
:subject to :
: number of replications: fitness function value: vector of decision variables expected sum of backorders and inventory costs
8
![Page 9: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/9.jpg)
Simulation-based Optimization Model
where
: demand9
![Page 10: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/10.jpg)
Simulation-based Optimization Model
Requirement of sub-products
: quantity available of sub-product
: amount required of sub-product to produce one lot in process
10
![Page 11: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/11.jpg)
Simulation-based Optimization Model
GA (MI-LXPM) [2]• real coded• Laplace crossover• power mutation• tournament selection• truncation procedure for integer restrictions• parameter free penalty approach [1]
11[1] K. Deb. An efficient constraint handling method for genetic algorithms. Computer methods in applied mechanics and engineering, 186(2):311-338, 2000.[2] K. Deep, K. P. Singh, M. Kansal, and C. Mohan. A real coded genetic algorithm for solving integer and mixed integer optimization problems. Applied Mathematics and Computation, 212(2):505-518, 2009.
![Page 12: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/12.jpg)
Results
12
Original model
Figure 4. Best, mean and worst fitness value of the population at each iteration.
![Page 13: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/13.jpg)
Results
13
Model modifications
Figure 5. Order processing subsystem for work centre .
![Page 14: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/14.jpg)
Results
14
Model modifications
Figure 6. Production subsystem for work centre .
![Page 15: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/15.jpg)
Results
15
Profit maximization
Figure 7. Best, mean and worst fitness value of the population at each iteration (time: 8.17 h).
![Page 16: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/16.jpg)
16
Stochastic Simulation
ILP
deterministicCDF
Simulation-based
optimization
uncertainty
CDF
Results
![Page 17: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/17.jpg)
Results
17
Profit maximization
Figure 8. CDFs of profit obtained through stochastic simulation.
![Page 18: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/18.jpg)
Conclusions
Production plan• production levels and allocation of work
centres
Process uncertainty• delays
Real life complexity• no complete analytic formulation
Better performance of solutions• stochastic simulation 18
![Page 19: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/19.jpg)
Post-doc Position Constrained optimization (applied in the area of protein structure prediction)
Start date: November 2014
in collaboration between:Computer Sciences (Joshua Knowles), Faculty of Life Sciences (Simon Lovell) and MBS (Julia Handl).Info: [email protected] 19
![Page 20: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/20.jpg)
Q & A
20
![Page 21: Simulation-based GA Optimization for Production Planning](https://reader035.fdocuments.net/reader035/viewer/2022070404/56813b16550346895da3c4e1/html5/thumbnails/21.jpg)
Thank you
September 13, 2014
Juan Esteban Diaz LeivaDr Julia Handl
21