AN ENHANCED METHODOLOGY FOR REDUCING TIME CONSUMPTION IN DISCRETE EVENT SIMULATION PROJECTS

Transcription

1 AN ENHANCED METHODOLOGY FOR REDUCING TIME CONSUMPTION IN DISCRETE EVENT SIMULATION PROJECTS PhD Student Björn Johansson Department of Production Engineering Chalmers University of Technology Gothenburg, Sweden PhD Student Thomas Grünberg Production Division Posten AB / Woxén Centrum Gothenburg, Sweden

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4 Productivity management Pre Pre engineering Manufacturing Logistics 1. Factors 2. Impact 3. Measurements 4. Methods/tools

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8 Analytical solution (eg queuing theory) Chalmers University of Technology SIMULATION Physical model Mathematical model Experiment with the actual system Experiment with a model of the system

9 Contacts

10 Keep one's things in order Base for decision support Points towards the right direction Time saving

11 Product lifecycle times Volume Volume C D B A Time A B C E Time New products New products

12 Parts/time Demand Loss of earnings Capacity time

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14 Preparations Modelling Analysis

15 Problem formulation Setting of objectives and overall project plan Experimental design Production runs and analysis Model Building Data Collection Coding More runs? Verified? Document program and report results Implementation Validated? Banks et al, 1996

16 Problem definition / Goal Level of detail Data collection

17 Clearly specified problem Time Delimitations Consultants Concrete, specific goals makes the following steps easier!

18 Specification of parameters Setting level of detail Layout Input data Times Batching Geometries Choosing output data: 645 h 782m Easy to interpret Easy to control Easy to affect 45se k 763mm

19 The most unconfident data is the weakest link in the model Compare distributions with the original data Use data in existing form Avoid personal assumptions

20 Conceptual Model Computer Model Verified? No Yes Validated? No Yes

21 Simple model. Show logical couplings between resources. Sketch or outline. Discussion base to create understanding of the system and the problem.

22 The simulation model is coded into the computer Conceptual model Input data Level of detail

23 VERIFICATION Guarantee the correct behaviour of each element in the model, ensuring that the code is correct VALIDATION The overall behaviour of the model is representative of the real world.

24 Simulation runs Conclusions Documentation Continuos improvements Implementation

25 Problem formulation Setting of objectives and overall project plan Chalmers University of Technology O Data collection Validated? O Model Building O Coding O O Experimental design O Production runs and analysis O Verified? Document program and report results Implementation

26 Problem formulation Setting of objectives and overall project plan Chalmers University of Technology O Data collection Validated? O Model Building O Coding O O Experimental design O Production runs and analysis O Verified? Document program and report results Implementation

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29 Feeling of incompletion Strive for the optimal system Fun to make simulation runs Consultants

30 Project time scales % problem definition model building and testing experimentation project completion Robinson, 1994

31 Preparations Model 50-80% Analysis

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37 PhD Student Björn Johansson epartment of Production Engineering Chalmers University of Technology Gothenburg, Sweden PhD Student Thomas Grünberg Production Division Posten AB / Woxén Centrum Gothenburg, Sweden job@pe.chalmers.se thomas.grunberg@posten.s