Modelling Behaviour in Simulation Models with Examples
Explore the application of modelling behaviour in simulation models through examples like the Lorry Loading Bay Problem, Knowledge Based Improvement, Agent-Based Modelling, and the Newsvendor Problem. Understand decision-making scenarios, predictive performance strategies, and knowledge-based improvements in operational systems.
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Presentation Transcript
Modelling Behaviour in Simulation Models Stewart Robinson Dean, School of Business and Economics Professor of Management Science
Outline Four examples: A Lorry Loading Bay Problem Knowledge Based Improvement (KBI): Ford Engine Plant Agent-Based Modelling (ABS): the Axelrod Cultural Model ABS and KBI: the Newsvendor Problem
Modelling Behaviour Part I: 1995 A Lorry Loading Bay Problem Lorry park Bay 1 Bay 2 Bay 3 Bay 4 Capacity 10 Capacity 20 Capacity 20 Capacity 10
Modelling Behaviour Part I: 1995 A Lorry Loading Bay Problem: Decision tree derived from example cases using a simulation model Lorry Size Outcome <11 Lane1 = 0 Lane1 > 0 1 4 >= 11 Lane2 = 0 Lane3 = 0 Lane3 > 0 2 3 0
Modelling Behaviour Part II: Knowledge Based Improvement (KBI) Investigation of the operations system Stage 1 Understanding the process and the decision- making required Generate decision- making scenarios VIS model Elicit knowledge Predict performance of decision-making strategies Stage 4 Stage 2 Decisions taken under scenarios (data sets held for each decision-maker) Provide input to the VIS in place of the decision-makers Data sets Stage 3 Stage 5 Seek improvements Trains AI model Represent the decision-making strategy of each decision-maker
Modelling Behaviour Part II: Knowledge Based Improvement (KBI)
Modelling Behaviour Part II: Knowledge Based Improvement (KBI)
Modelling Behaviour Part II: Knowledge Based Improvement (KBI) Subject A Subject B 100% 100% 80% 80% 60% 60% 40% 40% 20% 20% 0% 0% 2D 3D VR Real-world 2D 3D VR Real-world Visual display vs real-world Visual display vs real-world Subject C Entry point for untested engines from Assy line B Exit point for empty plattens UV booth 100% Platten wash Exit point for tested engines 9 11 13 15 17 19 Path control switch Repair station A1 Test cell 80% 3/4 5/6 A2: For rejected engines and empty plattens required by repair loop A3: For relooped or repaired engines Entry point for rejects from UV J B Waiting stand Sensor2 60% A4 A5 Entry point for empty plattens and rejects from ATD 40% A6: For relooped engines or empty plattens Cell control switch for cell 17/19 9/11 13/15 A7: For empty plattens C D 20% Sensor1 Tilting table 7/8 10/12 14/16 18/20 1/2 0% F 2D 3D VR Real-world E Visual display vs real-world 1 2 3 4 5 6 7 8 10 12 14 16 18 20
Modelling Behaviour Part III: Agent Based Modelling Axelrod Culture Model: Diffusion of Ideas 1 2 3 4 5 6 7 8 9 10 1 1_CDD 2_CDD 3_CDD 4_CDD 5_EBA 6_CDD 7_CDD 8_CDD 9_CDD 10_CDD 2 11_CDD 12_CDD 13_CDD 14_CDD 15_EBA 16_CDD 17_CDD 18_CDD 19_FEE 20_CDD 3 21_CDD 22_CDD 23_CDD 24_EBA 25_EBA 26_CDD 27_CDD 28_CDD 29_FEE 30_CDD 4 5 31_CDD 32_CDD 33_CDD 34_CDD 35_EBA 36_AFC 37_AFC 38_CDD 39_FEE 40_FEE 41_CDD 42_CDD 43_CDD 44_CDD 45_CDD 46_CDD 47_AFC 48_AFC 49_FEE 50_FEE 6 51_CDD 52_CDD 53_CDD 54_CDD 55_CDD 56_CDD 57_AFC 58_AFC 59_FEE 60_CDD 7 61_CDD 62_CDD 63_CDD 64_CDD 65_CDD 66_CDD 67_CDD 68_AFC 69_CDD 70_CDD 8 71_CDD 72_CDD 73_CDD 74_CDD 75_CDD 76_CDD 77_CDD 78_CDD 79_CDD 80_CDD 9 81_CDD 82_CDD 83_CDD 84_CDD 85_CDD 86_CDD 87_CDD 88_CDD 89_CDD 90_CDD 91_CDD 92_CDD 93_CDD 94_CDD 95_CDD 96_CDD 97_CDD 98_CDD 99_CDD 100_CDD 10
Modelling Behaviour Part IV: KBI and Agent Based Modelling Agent Based Simulation of Supply Chains: Newsvendor Problem D w Supplier Retailer Market demand q Min{q,D} w(q) p Material Funds Information
Modelling Behaviour Part IV: KBI and Agent Based Modelling Adapted KBI approach Stage 1 Stage 4 Understand the decision-making process Key Run the ABS model outcomes Simulation games ABS model Decision models Stage 2 Stage 3 Conduct the gaming sessions Fit the decision- making strategies Data sets
Modelling Behaviour Part IV: KBI and Agent Based Modelling Examples of supplier decisions 250 200 Wholesale price 150 100 50 0 1 6 11 16 21 26 31 36 41 46 Period w(t) w*
Modelling Behaviour Part IV: KBI and Agent Based Modelling Examples of retailer decisions 350 300 250 Order quantity 200 150 100 50 0 1 6 11 16 21 26 31 36 41 46 Period q(t) d(t-1) q*
Modelling Behaviour Part IV: KBI and Agent Based Modelling Fitting (behavioural) regression models = + + + i i i i ( ) ( 1) ( 1) ( 1) w t w t q t P t Suppliers s i 0 w q P SUP1: w(t)1 = 115.85 + 0.506w(t-1) - 0.014q(t-1) Adj. R2 = 0.852 Retailers = + + + + j j j j j ( ) ( ) ( 1) ( 1) ( 1) q t w t q t d t P t o w q d P r j RET1: q(t)1 = 246.81- 0.945w(t) - 0.033q(t-1) 0.045d(t-1) Adj. R2 = 0.867
Modelling Behaviour Part IV: KBI and Agent Based Modelling Supplier Retailer Agent Based Model Set price w Waiting for price Determine order quantity q Waiting for order Deliver order q Waiting for delivery t+1 t+1 Satisfy customer demand Min (q, d) Waiting for payment Receive payment from retailer w. q Receive payment from customer p. Min (q, d)
Modelling Behaviour Part IV: KBI and Agent Based Modelling Efficiency scores F1 RET1 RET2 RET3 RET4 RETOPT F2 0.150 (0.001) 0.527 (0.006) 0.397 (0.003) 0.375 (0.003) 0.320 (0.001) SUP1 [ 0.000] [ 0.002] [ 0.001] [ 0.001] [ 0.000] 0.434 (0.002) 0.778 (0.008) 0.636 (0.005) 0.461 (0.005) 0.644 (0.005) SUP2 [ 0.001] [ 0.002] [ 0.001] [ 0.001] [ 0.001] 0.708 (0.005) 0.968 (0.012) 0.851 (0.009) 0.585 (0.008) 0.849 (0.008) SUP3 [ 0.001] [ 0.003] [ 0.002] [ 0.002] [ 0.002] 0.550 (0.003) 0.878 (0.010) 0.734 (0.007) 0.507 (0.006) 0.741 (0.006) SUPOPT [ 0.001] [ 0.003] [ 0.002] [ 0.002] [ 0.002]
