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Simulation-based GA Optimization for Production Planning Juan Esteban Díaz Leiva Dr Julia Handl Bioma 2014 September 13, 2014
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2 Production Planning Production Plan Production levels Business objectives Allocation of resources
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3 Production Planning Lack of appropriate instrument Inappropriate methods Experience& “Sixth sense”
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Aplicable solution Simulation DES Simulation DES Optimization GA Optimization GA Simulation-based Optimization 4
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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
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Simulation-based Optimization Model 6
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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.
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Results 12 Original model Figure 4. Best, mean and worst fitness value of the population at each iteration.
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Results 13 Model modifications
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Results 14 Model modifications
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Results 15 Profit maximization Figure 7. Best, mean and worst fitness value of the population at each iteration (time: 8.17 h).
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16 Stochastic Simulation ILP deterministic CDF Simulation-based optimization uncertainty CDF Results
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17 Profit maximization Figure 8. CDFs of profit obtained through stochastic simulation.
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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
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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
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Q & A 20
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Thank you September 13, 2014 Juan Esteban Diaz Leiva Dr Julia Handl 21
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