Using simulation-based optimization in production planning

Transcription

1 Using simulation-based optimization in production planning Christian Almeder (joined work with M. Gansterer, S. Katzensteiner, R.F. Hartl, University of Vienna)

2 Outline Introduction: Simulation vs. Optimization Example (Blackbox): Optimization of a stochastic flexible flow shop with limited buffer Example (Blackbox/Surrogate): Optimization of parameters in a hierarchical planning concept Conclusion Prof. Dr. Christian Almeder GOR AG SCM

3 Introduction Simulation What can/should a (discrete-event) simulation model achieve? Representing a real system on a adequate level of abstraction Adequate for Testing and evaluating of different configurations and/or parameter settings Identifying suitable (good) configurations Conclusions for the real system Prof. Dr. Christian Almeder GOR AG SCM

4 Introduction Optimization What can/should an optimization model achieve? Representing a real system on a adequate level of abstraction Adequate for Testing and evaluating of different configurations and/or parameter settings Identifying good (best) configurations Conclusions for the real system Prof. Dr. Christian Almeder GOR AG SCM

5 Introduction Simulation vs. Optimization Simulation + high degree of detail (nonlinear and stochastic elements) + Analysis of various scenarios Optimization + identifying of very good solutions long computational times low degree of detail (very) limited possibility of optimization Usually used to support strategic/tactical decisions Usually used to support tactical/operational decisions Prof. Dr. Christian Almeder GOR AG SCM

6 parameters Introduction Simulation + Optimization Blackbox simulation model used for evaluating the objective function optimization algorithm searches randomly in the solution space most used way of combining simulation and optimization small effort necessary search space: tiny Blackbox optimization algorithm simulation model evaluation Prof. Dr. Christian Almeder GOR AG SCM

7 parameters Introduction Simulation + Optimization Surrogate model simulation model is replaced by a simplified (regression) model optimization algorithm uses surrogate model for evaluation surrogate model is faster search space: small - medium Surrogate model optimization algorithm surrogate model simulation model evaluation Prof. Dr. Christian Almeder GOR AG SCM

8 parameters Introduction Simulation + Optimization Integration (simplified) optimization model (low detail) simulation model is the data source for the optimization model reduction of iterations necessary ( intelligent optimization model) search space: medium - large Integration optimization model simulation model experiments / decisions Prof. Dr. Christian Almeder GOR AG SCM

9 Optimization of a stochastic flexible flow shop with limited buffer Problem setting: Large and heavy products (up to 100m long, weighing several tons) Difficult to handle Limited buffer capacities between production steps Stochastic processing times (due to quality inspection and manual reworking) Downtime of roller mill extremely expensive should be avoided Find best production sequence to avoid downtime and maximize throughput press cutting drilling roller mill press cuttin drilling Prof. Dr. Christian Almeder GOR AG SCM

10 Optimization of a stochastic flexible flow shop with limited buffer VNS algorithm for determining the production sequence: evaluation using sampling (several simulation experiments with fixed seeds) acceptance criteria t-test (high computational effort) comparing means and defining an a-priori theshold based on the observed variance of some initial simulation experiments (case study) Prof. Dr. Christian Almeder GOR AG SCM

11 VNS Prof. Dr. Christian Almeder GOR AG SCM

12 Optimization of a stochastic flexible flow shop with limited buffer Result (simplified model) runtime: 60 seconds, sample size for t-test: 15 buffer size SPT1 SPT2 SPT3 VNS-best VNS-first 1 job 9,99% 6,41% 8,44% 1,04% 0,97% 10% 13,08% 6,28% 8,35% 1,09% 1,00% 20% 12,31% 5,56% 7,39% 1,08% 1,03% deterministc 14,29% 7,91% 10,28% 0,87% 0,68% stoch. proc. times 10,94% 5,00% 7,08% 0,79% 0,76% stoch. prc. times + breakdowns 10,14% 5,35% 6,82% 1,55% 1,56% Prof. Dr. Christian Almeder GOR AG SCM

13 Optimization of a stochastic flexible flow shop with limited buffer Real case: Simulation model used for evaluation (-> longer runtimes) Considering only the scheduling of single production campaigns (set of jobs where no setup of the roller mill is necessary) usually 6 hours 2 days Objective consists of a (non-linear) combination of costs for tardiness (caused by an increased makespan of a production campaign) costs for machine downtime t-test is time consuming -> use a threshold for the difference of the means ( %) Prof. Dr. Christian Almeder GOR AG SCM

14 Optimization of a stochastic flexible flow shop with limited buffer Sample size: runs (fixed seeds) Measurement of solution quality: t-test with sample size 100 Starting solution: real production plan Method avg. improvement simple local search (~ 1h) -1.7% - 4.2% VND 2h 4.1% - 6.1% VND 1h >3.6% Prof. Dr. Christian Almeder GOR AG SCM

15 Optimization of a stochastic flexible flow shop with limited buffer Conclusions: Local search based methods can improve solutions quickly Easy to apply to stochastic problems Time consuming statistic tests are not necessary in practice VNS/VND produces much more robust results than local simple local search methods Prof. Dr. Christian Almeder GOR AG SCM

16 Optimization of parameters in a hierarchical planning concept Based on given settings for HPP we optimize planning parameters and compare 6 different search methods explore the parameter space systematically identify most favorable method provide managerial insights Prof. Dr. Christian Almeder GOR AG SCM

17 Optimization of parameters in a hierarchical planning concept Production environment Data from automotive suppliers Make-to-order production 5-stage production process 12 final products Shop floor stochastics Scenarios High demand Low demand Few orders Peaks Prof. Dr. Christian Almeder GOR AG SCM

18 Framework for HPP Prof. Dr. Christian Almeder GOR AG SCM

19 Framework for HPP Prof. Dr. Christian Almeder GOR AG SCM

20 Framework for HPP Prof. Dr. Christian Almeder GOR AG SCM

21 Optimization of parameters in a hierarchical planning concept Investigated parameters Planned leadtime Safety stock Lotsize Objective function Combination of service level and inventory level Fill rate ( service level) minus inventory cost Prof. Dr. Christian Almeder GOR AG SCM

22 Optimization of parameters in a hierarchical planning concept Methods: Response Surface Methodology (RSM) OptQuest (OQ) Random Restart with RSM for local search (RR_RSM) Random Restart with OQ for local search (RR_OQ) VNS with RSM for local search (VNS_RSM) VNS with OQ for local search (VNS_OQ) Prof. Dr. Christian Almeder GOR AG SCM

23 RSM Prof. Dr. Christian Almeder GOR AG SCM

24 VNS Prof. Dr. Christian Almeder GOR AG SCM

25 Parameter space Grid planned leadtime (PLT): 0-25 safety stock (SS): 0-30 lotsize (LS): 1-80 Regular grid (range divided by 10) 1331 points evaluated 4 times 5324 experiments Linear interpolation Prof. Dr. Christian Almeder GOR AG SCM

26 Optimization of parameters in a hierarchical planning concept Prof. Dr. Christian Almeder GOR AG SCM

27 Optimization of parameters in a hierarchical planning concept Prof. Dr. Christian Almeder GOR AG SCM

28 Optimization of parameters in a hierarchical planning concept Performance Analysis Prof. Dr. Christian Almeder GOR AG SCM

29 Results of VNS_OQ Prof. Dr. Christian Almeder GOR AG SCM

30 Optimization of parameters in a hierarchical planning concept Performance VNS_OQ wins (?) Analysis Planned leadtimes are sensitive to erratic demand Safety stocks are stable even in case of erratic demand The parameters are strongly influencing each other Prof. Dr. Christian Almeder GOR AG SCM

31 Conclusions Simulation-based optimization can/should be used in operational planning (if needed/possible) The way of combination strongly depends on the problem setting computational time of the simulation model time available for optimization variability of the system a unified optimization and simulation modelling approach is still missing. Prof. Dr. Christian Almeder GOR AG SCM

32 Thank you for your attention! Questions? Gansterer, M., Almeder, C., Hartl, R.F., Simulation-based optimization methods for setting production planning parameters. International Journal of Production Economics 151, Almeder, C., Hartl, R.F., A metaheuristic optimization approach for a realworld stochastic flexible flow shop problem with limited buffer. International Journal of Production Economics 145, Almeder, C., Preusser, M., Hartl, R.F., Simulation and optimization of supply chains: alternative or complementary approaches? OR Spectrum 31, Prof. Dr. Christian Almeder GOR AG SCM