1. 1 Franck FONTANILI - CGI IMSM'07 Content of the presentation Introduction and context Problem Proposed solution Results Conclusions and perspectives discrete-event simulation, genetic algorithm, multicriterion optimization Discrete events simulation and genetic algorithm-based manufacturing execution Keywords

2. 2 Franck FONTANILI - CGI IMSM'07 Overview Manufacturing context: assembly of mixed models Stage of preparation of the release of a campaign How to determine a « good » value of control parameters ? Introduction and context Coupling DES and optimization algorithm

3. 3 Franck FONTANILI - CGI IMSM'07 Manufacturing system Free automated transfer with belt conveyors Manual or automated assembly operations Workstations layout in: series or parallel Introduction and context Belt conveyors Pallet Coding system Product to assemble

4. 4 Franck FONTANILI - CGI IMSM'07 Flow of pallets Introduction and context 5 123 46 5 13 46 2 2 Non-permutable phases Non-redundant phases = generalized flow-shop In case of saturation of one of the bypass workstations: = loop on the central conveyor

5. 5 Franck FONTANILI - CGI IMSM'07 Assembly campaigns planning Problem Example of a campaign with 5 orders Release sequencingMixed process

6. 6 Franck FONTANILI - CGI IMSM'07 Assembly campaigns planning Problem Line is empty Implementation of values of control parameters for campaign n RampUpRampDownSteady state Preparation and optimization of control parameters for campaign n+1

7. 7 Franck FONTANILI - CGI IMSM'07 Flow control parameters Release sequence of k assemby orders Inter-release Time (IrTi) Problem AAAAAAAAAAAABBBBBCCCCCCCC AAAAAAAAAAAABBBBBCCCCCCCC sequencing 1 2 3 k! combinations 120 combinations for 5 orders (without splitting) [max IrTi -min IrTi +1) k combinations 371.293 combinations for 5 orders 2 sec.<IrTi<16 sec. BBBBBCCCCCCCCAAAAAAAAAAAA

8. 8 Franck FONTANILI - CGI IMSM'07 Flow control parameters Capacity of upstream conveyor on j workstations Number of pallets to be used (Np) Problem StAm [max StAm -min StAm +1) j combinations 46.656 combinations for 6 workstations 0<StAm<7 0<Np<26

9. 9 Franck FONTANILI - CGI IMSM'07 Flow control parameters Capacity of downstream conveyor Priority rule on the exit of workstation Splitting of the sequence of the assembly orders Etc. Problem With only the 3 most sensitive parameters : Inter-Release Time (IrTi) Capacity of upstream conveyor (StAm) Number of pallets (Np) More than 10 11 combinations What combination to be used ?

10. 10 Franck FONTANILI - CGI IMSM'07 Use of Simulation Simulation is a frequently used tool during stage of:  Design  Improvement of manufacturing systems (existent or to be built) Proposal: use of simulation during stage of:  preparation the execution of a campaign to provide a decision-making aid for the choice of the values to fix at the flow control parameters Proposed solution

11. 11 Franck FONTANILI - CGI IMSM'07 Use of Simulation Simulation of a k order campaign on j workstations Proposed solution Campaign to release 0 sec.<IrTi(k)<13 sec. 0<StAm(j)<7 19<Np<36 Objective function Control parameters Simulation model designed with Witness

12. 12 Franck FONTANILI - CGI IMSM'07 Choice of the objective function Optimization criteria Total Lead Time of the campaign (Lt) between the release of the first pallet and the delivery of the last. Average Work in Process (WIP) between the loading workstation and the unloading workstation Total number of Setup (Set) corresponding to the change over from one product to another Multicriterion weighted objective function Proposed solution F(x) = 0,55.||Lt(x)|| + 0,24.||WIP(x)|| + 0,21.||Set(x)|| Normalised criterion To minimize

13. 13 Franck FONTANILI - CGI IMSM'07 Choice of the objective function Running a simulation Proposed solution Campaign to releaseObjective function Control parameters

14. 14 Franck FONTANILI - CGI IMSM'07 Coupling Simulation with Optimization Proposed solution Simulation model Optimization Algorithm = Genetic Algorithm Campaign to release Control parameters Algorithm parameters Objective function Why a Genetic Algorithm? High-performance for complex problems Exploration of parallel solutions Easy to program From the algorithm (coded in Delphi) Witness is an Object Linked Embedding (OLE)

15. 15 Franck FONTANILI - CGI IMSM'07 Evolution and Genetic Algorithm Proposed solution Chromosome Gene Individual Generation Crossover Mutation Selection Evaluation For m generations For n individuals

16. 16 Franck FONTANILI - CGI IMSM'07 Coding our problem with GA Proposed solution Gene 1 IrTi(1)IrTi(2)IrTi(3)IrTi(4)IrTi(5) Gene 2Gene 3Gene 4Gene 5 StAm(1)StAm(2)StAm(3)StAm(4)StAm(5)StAm(6)Rp Gene 6Gene 7Gene 8 Gene 10Gene 11Gene 12 a chromosome = a combination of control parameters 1- Evaluation 2- Elitist selection parent #1 2- Elitist selection parent #2 3- Crossover (crossing point) 4- Mutation (mutant) For generations = 1 to 30 For individual = 1 to 9 Next generation Next individual

17. 17 Franck FONTANILI - CGI IMSM'07 Running simulation and GA Proposed solution

18. 18 Franck FONTANILI - CGI IMSM'07 Objective function Results obtained by coupling simulation and GA

19. 19 Franck FONTANILI - CGI IMSM'07 Normalized criteria Results obtained by coupling simulation and GA

20. 20 Franck FONTANILI - CGI IMSM'07 Best solution found by the GA Results obtained by coupling simulation and GA 0,00 0,20 0,40 0,60 0,80 1,00 0,000,200,400,600,801,00 Setup WIP The best solution is (at the 264th iteration after 5 minutes) : Best of WIP Best of Setup Best of Lead Time

21. 21 Franck FONTANILI - CGI IMSM'07 Conclusions GA finds a « good » solution in less than 5 minutes allowing its use during the preparation time (idle time) Simulation coupled with GA provides a decision- making aid to the manager. Take into account other parameters: sequencing and orders splitting Take into account other constraints : scheduling on each workstation Conclusions and perspectives Perspectives