A Genetic Algorithm Tool for Designing Manufacturing Facilities in the Capital Goods Industry

1. A Genetic Algorithm Tool for Designing Manufacturing Facilities in the Capital Goods Industry Dr Christian Hicks, University of Newcastle, England Email: Chris.Hicks@ncl.ac.uk

2. Green field – designer free to select processes, machines, transport, layout, building and infrastructure Brown field – existing situation imposes many constraints Types of Facilities Design Problems

3. Includes: Job assignment – selection of machines for each operation and definition of operation sequences Cell formation – assignment of machine tools and product families to cells Layout design – geometric design of manufacturing facilities and the location of resources Transportation system design This paper considers cell formation and layout design Facilities Layout Problem

4. “Eyeballing” Coding and classification Product Flow Analysis Machine-part incidence matrix methods Rank Order Clustering Close Neighbour Algorithm Agglomerative clustering Various similarity coefficients Alternative clustering strategies Cell Formation Methods

5. Rank Order Clustering Applied to data Obtained from a capital goods company

6. Similarity Coefficient

7. Agglomerative clustering using the single linkage strategyEquation 1

8. Agglomerative clustering with complete linkage strategy

9. Limitations Few natural machine-part clusters Long and complex routings mitigate against self contained cells Clustering only uses routing information Geometric information is not used. Clustering applied to capital goods companies

10. Based upon: Manufacturing System Simulation Model (Hicks 1998) GA scheduling tool (Pongcharoen et al. 2000) Genetic Algorithm Design Tool

12. Use GAs to create sequences of machines Apply a placement algorithm to generate layout. Measure total direct or rectilinear distance to evaluate the layout. GA Procedure

13. Genetic Algorithm Similar to Pongcharoen et al except, the repair process is different and it is implemented in Pascal

14. Placement Algorithm

15. 52 Machine tools 3408 complex components 734 part types Complex product structures Total distance travelled Directdistance 232Km Rectilinear distance 642Km Case Study

16. Initial facilities layout

17. Total rectilinear distance travelled vs. generation (brown field)

18. Resultant Brown-field layout

19. Total rectilinear distance vs. generation (green field) Note the rapid convergence with lower totals than for the brown field problem

20. Resultant layout (green field) Note that brown field constraints, such as walls Have been ignored.

21. Significant body of research relating to facilities layout, particularly for job and flow shops. Much research related to small problems. Capital goods companies very complex due to complex routings and subsequent assembly requirements. Clustering methods are generally inconclusive when applied to capital goods companies. GA tool shows an improvement of 70% in the green field case and 30% in the brown field case. Conclusions

22. The GA layout generation tool is embedded within a large sophisticated simulation model. Dynamic layout evaluation criteria can be used. The integration with a GA scheduling tool provides a mechanism for simultaneously “optimising” layout and schedules. Future Work