Simulation-based GA Optimization for Production Planning

1. Simulation-based GA Optimization for Production Planning Juan Esteban Díaz Leiva Dr Julia Handl Bioma 2014 September13, 2014

2. Business objectives ProductionPlanning • Production levels • Allocation of resources • Production Plan

3. Experience • & • “Sixth sense” ProductionPlanning • Inappropriate methods • Lack of appropriate instrument

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

5. Supportdecisionmaking Objective • Feasibility • Production • Planning • Simulation-basedoptimization • Applicablility Uncertainty & Real-lifecomplexity • Robustness

6. Simulation-based Optimization Model Figure 1. Order processing subsystem for work centre .

7. Simulation-based Optimization Model Figure 2. Production subsystem for work centre . Figure 3. Repair service station of work centre .

8. Simulation-based Optimization Model : subject to : : number of replications : fitness function value : vector of decision variables expected sum of backorders and inventory costs

9. Simulation-based Optimization Model where :demand

10. Simulation-based Optimization Model Requirement of sub-products : quantity available of sub-product :amount required of sub-product to produce one lot in process

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

13. Results Model modifications Figure 5. Order processing subsystem for work centre .

14. Results Model modifications Figure 6. Production subsystem for work centre .

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

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

17. Results 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

19. Post-doc PositionConstrained optimization (applied in the area of protein structure prediction)Start date: November 2014in collaboration between:Computer Sciences (Joshua Knowles), Faculty of Life Sciences (Simon Lovell) and MBS (Julia Handl).Info: j.handl@manchester.ac.uk

20. Q & A

21. Thank youSeptember 13, 2014Juan Esteban Diaz LeivaDr Julia Handl