simulation-based ga optimization for production planning
DESCRIPTION
Simulation-based GA Optimization for Production Planning. Juan Esteban Díaz Leiva Dr Julia Handl. Bioma 2014 September 13, 2014. Business objectives. Production Planning. Production levels. Allocation of resources. Production Plan. Experience & “Sixth sense”. - PowerPoint PPT PresentationTRANSCRIPT
Simulation-based GA Optimization for Production
Planning
Juan Esteban Díaz LeivaDr Julia Handl
Bioma 2014September 13, 2014
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Production Planning
Production Plan
Production levels
Business objectives
Allocation of resources
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Production Planning
Lack of appropriate instrument
Inappropriate methods
Experience&
“Sixth sense”
Aplicable solution
SimulationDES
OptimizationGA
Simulation-based Optimization
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Objective
Simulation-based
optimization
Support decision making
Feasibility
Applicablility
Robustness
Uncertainty &
Real-life complexity
Production Planning
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Simulation-based Optimization Model
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Figure 1. Order processing subsystem for work centre .
Simulation-based Optimization Model
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Figure 2. Production subsystem for work centre .
Figure 3. Repair service station of work centre .
Simulation-based Optimization Model
:subject to :
: number of replications: fitness function value: vector of decision variables expected sum of backorders and inventory costs
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Simulation-based Optimization Model
where
: demand9
Simulation-based Optimization Model
Requirement of sub-products
: quantity available of sub-product
: amount required of sub-product to produce one lot in process
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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.
Results
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Original model
Figure 4. Best, mean and worst fitness value of the population at each iteration.
Results
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Model modifications
Figure 5. Order processing subsystem for work centre .
Results
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Model modifications
Figure 6. Production subsystem for work centre .
Results
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Profit maximization
Figure 7. Best, mean and worst fitness value of the population at each iteration (time: 8.17 h).
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Stochastic Simulation
ILP
deterministicCDF
Simulation-based
optimization
uncertainty
CDF
Results
Results
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Profit maximization
Figure 8. CDFs of profit obtained through stochastic simulation.
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
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
Q & A
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Thank you
September 13, 2014
Juan Esteban Diaz LeivaDr Julia Handl
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