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    An investigation into tooling requirements and strategies for FMS operation

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    A study of the minimum tooling requirements and strategies for efficient operation of Flexible Manufacturing Systems, FMS's, in Assembly set Production, ASP, i.e production in sets of parts to completely assemble one or more product units, is presented in this research work. The main investigating tool is a simulation model. With this model the tool groups to be loaded into machines and fixtured pallet requirements were studied in conjunction with two scheduling rules. One is a FCFS rule and the other is a new rule, called MRPAS, which schedules work on the basis of the number of parts still unfinished belonging to an Assembly Set. The results of the research work show that ASP can be efficiently carried out in FMS's. However this requires that a good system set-up and adequate operating strategies are used. In particular appropriate tooling levels and good tooling configurations,TC's, i.e. combinations of tools in groups to be loaded into the machines, must be established to achieve high FMS performance. Tooling combination and duplication heuristic rules and the simulation model can be used for achieving this aim. The heuristic approach is shown to be necessary due to the impossibility, in a reasonable time, of evaluating the performance of FMS's under the large number of alternative tooling configurations which are possible. The level of fixtured pallets used can also have a great influence on system performance. Appropriate levels of these resources to operate FMS's for given TC's can be established using the methodology developed in this work. It is also important that good scheduling rules are used. In the cases studied, the MRPAS rule produces the best performance expressed as the combination of FMS utilization and production of complete assembly sets. Moreover a very small assembly set batch size, ASBS, i.e. number of AS released together into the FMS, is likely to be preferable. In the cases studied an ASBS of one performed best overall
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