1. Simulation-based GA Optimization for Production Planning Juan Esteban Díaz Leiva Dr Julia Handl Bioma 2014 September 13, 2014

2. 2 Production Planning Production Plan Production levels Business objectives Allocation of resources

3. 3 Production Planning Lack of appropriate instrument Inappropriate methods Experience& “Sixth sense”

4. Aplicable solution Simulation DES Simulation DES Optimization GA Optimization GA Simulation-based Optimization 4

5. Objective Simulation-based optimization Support decision making Support decision making Feasibility Feasibility Applicablility Applicablility Robustness Robustness Uncertainty & Real-life complexity Uncertainty & Real-life complexity Production Planning PlanningProduction 5

6. Simulation-based Optimization Model 6

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11. 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.

12. Results 12 Original model Figure 4. Best, mean and worst fitness value of the population at each iteration.

13. Results 13 Model modifications

14. Results 14 Model modifications

15. Results 15 Profit maximization Figure 7. Best, mean and worst fitness value of the population at each iteration (time: 8.17 h).

16. 16 Stochastic Simulation ILP deterministic CDF Simulation-based optimization uncertainty CDF Results

17. 17 Profit maximization Figure 8. CDFs of profit obtained through stochastic simulation.

18. 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

19. 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: j.handl@manchester.ac.uk 19

20. Q & A 20

21. Thank you September 13, 2014 Juan Esteban Diaz Leiva Dr Julia Handl 21