KOH, S. C. L., GUNASEKARAN, A. and SAAD, S. M. (2006). Parts verification for multi-level-dependent demand manufacturing systems: a recognition and classification structure. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 31 (3-4), 305-315.Full text not available from this repository.
This research has developed and implemented a part recognition and classification structure to execute parts verification in a multi-level-dependent demand manufacturing system. The part-recognition algorithm enables the parent and child relationship between parts to be recognised in a finite-capacitated manufacturing system. This algorithm was developed using the SIMAN simulation language and implemented in a multi-level-dependent demand manufacturing simulation model. The part-classification structure enables the modelling of a multi-level-dependent demand manufacturing system between parts to be carried out effectively. The part-classification structure was programmed using Visual Basic Application (VBA) and was integrated into the work-to-list generated from a simulated materials requirements planning (MRP) model. This part-classification structure was then implemented in the multilevel-dependent demand manufacturing simulation model. Two stages of implementation, namely, parameterisation and execution, of the part recognition and classification structure were carried out. A real case study was used and five detailed steps of execution were processed. Simulation experiments and MRP were run to verify and validate the part recognition and classification structure. The results led to the conclusion that implementation of the recognition and classification structure has effectively verified the correct parts and sub-assemblies used for the correct product and order. No parts nor sub-assembly shortages were found, and the quantity required was produced. The scheduled release for some orders was delayed due to overload of the required resources. When the loading is normal, all scheduled release timing is adhered to. The recognition and classification structure has a robust design; hence, it can be easily adapted to new system parameters to study different or more complex cases.
|Additional Information:||Times Cited: 0|
|Research Institute, Centre or Group:||Materials and Engineering Research Institute > Centre for Robotics and Automation > Systems Modelling and Integration Group|
|Depositing User:||Danny Weston|
|Date Deposited:||28 Apr 2010 10:59|
|Last Modified:||28 Apr 2010 10:59|
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