© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/1 A Genetic Algorithm Tool for Designing Manufacturing Facilities in the Capital Goods Industry Dr Christian Hicks, University of Newcastle, England

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/2 Capital Goods Companies Complex products e.g. turbine generators, oilrigs, cranes Complex processes including component manufacturing, assembly, construction and commissioning Highly customised designs Very low volume production with highly variable demand.

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/3 Capital goods company activities

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/4 Types of Facilities Design Problems Green field – designer free to select processes, machines, transport, layout, building and infrastructure Brown field – existing situation imposes many constraints

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/5 Facilities Layout Problem 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

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/6 Cell Formation Methods “ 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

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/7 Rank Order Clustering Applied to data Obtained from a capital goods company

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/8 Similarity Coefficient

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/9 Agglomerative clustering using the single linkage strategyEquation 1

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/10 Agglomerative clustering with complete linkage strategy

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/11 Clustering applied to capital goods companies 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.

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/12 Genetic Algorithm Design Tool Based upon: Manufacturing System Simulation Model (Hicks 1998) GA scheduling tool (Pongcharoen et al. 2000)

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/13

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/14 GA Procedure Use GAs to create sequences of machines Apply a placement algorithm to generate layout. Measure total direct or rectilinear distance to evaluate the layout.

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/15 Genetic Algorithm Similar to Pongcharoen et al except, the repair process is different and it is implemented in Pascal

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/16 Placement Algorithm

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/17 Case Study 52 Machine tools 3408 complex components 734 part types Complex product structures Total distance travelled –Direct distance 232Km –Rectilinear distance 642Km

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/18 Initial facilities layout

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/19 Total rectilinear distance travelled vs. generation (green field)

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/20 Resultant Brown-field layout

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/21 Total rectilinear distance vs. generation (green field) Note the rapid convergence with lower totals than for the brown field problem

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/22 Resultant layout (green field) Note that brown field constraints, such as walls Have been ignored. The solution is not realistic because there is insufficient space for materials.

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/23 Conclusions 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 55% in the green field case and 30% in the brown field case.

© C.Hicks, University of Newcastle C.F.Earl, Open University IDMME02/24 Future Work 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.